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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tickdb-packages.R
\docType{package}
\name{tickdb-package}
\alias{tickdb-package}
\title{tickdb}
\description{
Functions to access the Liquid Capital Tick Database
}
\author{
David Bellot \email{dbellot@liquidcapital.com}
}
|
/tickdb/man/tickdb-package.Rd
|
no_license
|
sheryllan/tickdb
|
R
| false | true | 301 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tickdb-packages.R
\docType{package}
\name{tickdb-package}
\alias{tickdb-package}
\title{tickdb}
\description{
Functions to access the Liquid Capital Tick Database
}
\author{
David Bellot \email{dbellot@liquidcapital.com}
}
|
#' Function to return the team data
#'
#' Returns data on the fantasy football team
#'
#' @param GameID The number assigned
#' to the annual fantasy footbal compettion. Games should
#' not be confused with a weekly NFL football game. See
#' also the GetGameID() function.
#'
#' @param LeagueID The ID number representing the specific
#' fantasy football league.
#'
#' @param team The team ID, possible values
#' are based on the number of teams in the league, ypically
#' ranging from 1-10.
#'
#' @param DataType The type of data desired for the team.
#' Possible values are stats, standings, or matchups.
#'
#' @param sig See also the GetSig() function
#' @return List. Fiels will vary by the datatype used.
#' @import httr
#'
GetTeamData<-function(GameID,LeagueID,team=1,DataType="stats",sig){
base <- "http://fantasysports.yahooapis.com/fantasy/v2/team/"
formatting <- switch(DataType,
stats = "/stats?format=json",
standings = "/standings?format=json",
matchups = "/matchups?format=json")
x <- paste(GameID,".l.",LeagueID,".t.",team,sep="")
y <- paste(base,x,formatting,sep="")
z <- httr::GET(y,sig)
}
|
/R/GetTeamData.R
|
no_license
|
kuhnrl30/Touchdown
|
R
| false | false | 1,155 |
r
|
#' Function to return the team data
#'
#' Returns data on the fantasy football team
#'
#' @param GameID The number assigned
#' to the annual fantasy footbal compettion. Games should
#' not be confused with a weekly NFL football game. See
#' also the GetGameID() function.
#'
#' @param LeagueID The ID number representing the specific
#' fantasy football league.
#'
#' @param team The team ID, possible values
#' are based on the number of teams in the league, ypically
#' ranging from 1-10.
#'
#' @param DataType The type of data desired for the team.
#' Possible values are stats, standings, or matchups.
#'
#' @param sig See also the GetSig() function
#' @return List. Fiels will vary by the datatype used.
#' @import httr
#'
GetTeamData<-function(GameID,LeagueID,team=1,DataType="stats",sig){
base <- "http://fantasysports.yahooapis.com/fantasy/v2/team/"
formatting <- switch(DataType,
stats = "/stats?format=json",
standings = "/standings?format=json",
matchups = "/matchups?format=json")
x <- paste(GameID,".l.",LeagueID,".t.",team,sep="")
y <- paste(base,x,formatting,sep="")
z <- httr::GET(y,sig)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotLollipops.R
\name{plotLollipops.GrowthData}
\alias{plotLollipops.GrowthData}
\title{plot lollipops for fits to molt increment data}
\usage{
plotLollipops.GrowthData(
mdfr,
type = "zscores",
extreme = 3,
dw = 1,
size = 0.1,
xlab = "pre-molt size",
ylab = type
)
}
\arguments{
\item{mdfr}{\itemize{
\item input dataframe from \code{\link[=extractMDFR.Fits.GrowthData]{extractMDFR.Fits.GrowthData()}}
}}
\item{type}{\itemize{
\item type of value to plot (e.g., "zscores")
}}
\item{extreme}{\itemize{
\item criteria for extreme values
}}
\item{dw}{\itemize{
\item dodge width (x-axis units)
}}
\item{size}{\itemize{
\item line size for lollipop stems
}}
\item{xlab}{\itemize{
\item x-axis label
}}
\item{ylab}{\itemize{
\item y-axis label
}}
}
\value{
ggplot2 plot object
}
\description{
function to plot lollipops for fits to molt increment data.
}
\details{
Plots \code{type} as lollipops against pre-molt size.
}
|
/man/plotLollipops.GrowthData.Rd
|
permissive
|
wStockhausen/rCompTCMs
|
R
| false | true | 1,014 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotLollipops.R
\name{plotLollipops.GrowthData}
\alias{plotLollipops.GrowthData}
\title{plot lollipops for fits to molt increment data}
\usage{
plotLollipops.GrowthData(
mdfr,
type = "zscores",
extreme = 3,
dw = 1,
size = 0.1,
xlab = "pre-molt size",
ylab = type
)
}
\arguments{
\item{mdfr}{\itemize{
\item input dataframe from \code{\link[=extractMDFR.Fits.GrowthData]{extractMDFR.Fits.GrowthData()}}
}}
\item{type}{\itemize{
\item type of value to plot (e.g., "zscores")
}}
\item{extreme}{\itemize{
\item criteria for extreme values
}}
\item{dw}{\itemize{
\item dodge width (x-axis units)
}}
\item{size}{\itemize{
\item line size for lollipop stems
}}
\item{xlab}{\itemize{
\item x-axis label
}}
\item{ylab}{\itemize{
\item y-axis label
}}
}
\value{
ggplot2 plot object
}
\description{
function to plot lollipops for fits to molt increment data.
}
\details{
Plots \code{type} as lollipops against pre-molt size.
}
|
#figures and tests from ANOVA lecture
#iris example, from first lecture
#
library(ggplot2)
#point plot####
ggplot(iris, aes(Species,Sepal.Length)) +
geom_point(aes(colour=Species), size = 3) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))
#bar chart with error bars ####
require(Rmisc)
function_output <- summarySE(iris, measurevar="Sepal.Length", groupvars =
c("Species"))
ggplot(function_output, aes(x=Species, y=Sepal.Length)) +
geom_col(aes(fill=Species), size = 3) +
geom_errorbar(aes(ymin=Sepal.Length-ci, ymax=Sepal.Length+ci), size=1.5) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))
#boxplot####
ggplot(iris, aes(Species,Sepal.Length)) +
geom_boxplot(aes(colour=Species), size = 3) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))
#stacked histogram####
ggplot(iris, aes(Sepal.Length)) +
geom_histogram(aes(fill=Species), size=3) +
xlab("Sepal Length (cm)")+
ylab("Frequency")+
ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))
#facet####
ggplot(iris, aes(Sepal.Length)) +
geom_histogram(aes(fill=Species), size=3) +
xlab("Sepal Length (cm)")+
ylab("Frequency")+
ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
legend.position = "bottom",
plot.title = element_text(hjust = 0.5, face="bold", size=32))+
facet_wrap(~Species, ncol = 1)
#point plot with means####
ggplot(iris, aes(Species,Sepal.Length)) +
geom_point(aes(colour=Species), size = 3) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32)) +
geom_hline(aes(yintercept=mean(iris$Sepal.Length)), size = 2, color = "orange")
#point plot with means for each group####
#make extra column for output and rename here
function_output$Sepal.Length <- function_output$mean
ggplot(iris, aes(Species,Sepal.Length)) +
geom_point(aes(colour=Species), size = 3) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32)) +
geom_errorbar(aes(ymin=Sepal.Length, ymax=Sepal.Length), size=1.5,
data = function_output, color = "black")
#combine these two ####
ggplot(iris, aes(Species,Sepal.Length)) +
geom_point(aes(colour=Species), size = 3) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32)) +
geom_errorbar(aes(ymin=Sepal.Length, ymax=Sepal.Length), size=1.5,
data = function_output, color = "black") +
geom_hline(aes(yintercept=mean(iris$Sepal.Length)), size = 2, color = "orange")
#show null hypothesis
ggplot(iris, aes(Species,Sepal.Length)) +
geom_point(aes(colour=Species), size = 3) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32)) +
geom_errorbar(aes(ymin=Sepal.Length, ymax=Sepal.Length), size=1.5,
data = function_output, color = "black") +
geom_hline(aes(yintercept=mean(iris$Sepal.Length)), size = 2, color = "orange") +
geom_errorbar(aes(ymin=mean(iris$Sepal.Length), ymax=mean(iris$Sepal.Length)), size=1.5, color = "purple", linetype = 3)
#p value via simulation ####
#get overall variance and mean estimate
variance_estimate <- sum((function_output$N -1) * (function_output$sd)^2)/(sum(function_output$N)-length(function_output$N))
mean_sepal <- mean(iris$Sepal.Length)
#sample
ratio <- data.frame(rep = 1:10000, mse = rep(NA,10000),
msg = rep(NA,10000), ratio = rep(NA,10000))
for(i in 1:10000){
setosa <- rnorm(50, mean_sepal, sd= sqrt(variance_estimate))
versicolor <- rnorm(50, mean_sepal, sd= sqrt(variance_estimate))
virginica <- rnorm(50, mean_sepal, sd= sqrt(variance_estimate))
mean_overall <- mean(c(setosa, versicolor, virginica))
ratio$mse[i] <- (49 * var(setosa) + 49 * var(versicolor) + 49 * var(virginica))/(150 - 3)
ratio$msg[i] <- (50 * (mean(setosa)-mean_overall)^2 +
50 * (mean(versicolor)-mean_overall)^2 +
50 * (mean (virginica)-mean_overall)^2)/2
ratio$ratio[i] <- ratio$msg[i]/ratio$mse[i]
}
summary(lm(Sepal.Length~Species, iris))$fstatistic[1]
ggplot(ratio, aes(ratio)) +
geom_histogram(aes(y=..count../sum(..count..)), fill = "orange", bins = 15) +
ggtitle("Ratio under null hypothesis") +
ylab("Probability") +
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))
#with f####
#notice i specify degrees of freedom for numerator (df1) and denomitor (df2)
ggplot(ratio, aes(ratio)) +
geom_histogram(aes(y=..count../sum(..count..)), fill = "orange",
breaks = seq(0,15,1)) +
stat_function(fun = df, args = list(df1 =2, df2 = 147),size = 3, color = "green") +
ggtitle("Signal under null hypothesis") +
ggtitle("Ratio under null hypothesis") +
ylab("Probability") +
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))
#build model####
iris_anova <- lm(Sepal.Length~Species, iris)
#it creates an object you can manipulate
#check assumptions####
par(mfrow = c(2,2))
plot(iris_anova)
#look at outputs using p-values
summary(iris_anova)
#you can get specific group means by builidng model without intercept
iris_anova_no_intercept <- lm(Sepal.Length~Species - 1, iris)
summary(iris_anova_no_intercept)
#but don't do this in general (R and F stats will be skewed!)
#just use to get group coefficients if needed
#note relationship between model coefficients here as well
#Anova command from car package is needed to give overall group p-value
library(car)
Anova(iris_anova, type = "III")
#since our overall anova was significant, we need to carry out multiple comparisons
#to see what groups are driving the difference
#multcomp
library(multcomp)
#where's the almost difference? use Tukey's HSD for all pairs
library(multcomp)
compare_cont_tukey <- glht(iris_anova, linfct = mcp(Species = "Tukey"))
summary(compare_cont_tukey)
#or you can specify what you want to consider
compare_virginica_only <- glht(iris_anova, linfct = mcp(Species =
c("virginica - versicolor = 0",
"virginica - setosa = 0")))
#remember to control for error rates!
summary(compare_virginica_only, test=adjusted("holm"))
#other options
summary(compare_virginica_only, test=adjusted("fdr"))
#we can "find" significance more easily, but you need to justify why you did this
#we can "find" significance more easily, but you need to justify why you did this
#using set contrasts####
#are versicolor and virginica different from setosa
contr <- rbind("setosa - virginica - setosa" = c(-1,.5,.5))
summary(glht(iris_anova, linfct = mcp(Species = contr)))
summary(iris_anova) # this appears to be right
#orthogonal
contr <- rbind("setosa - virginica - setosa" = c(-1,.5,.5),
"virginica - setosa" = c(0,-1,1))
summary(glht(iris_anova, linfct = mcp(Species = contr)))
summary(iris_anova) # this appears to be right
#R2####
#R2 explains how much variation is explained by our model (groups in this instance)
summary(iris_anova)
#or
summary(iris_anova)$r.squared
#note you can have a low p-value model that also explains only small amount of
#variation in the data
#graphical comparison####
#add comparison portion to fuction_output
function_output$comparison <- "NA"
#enter by hand for small groups by comparing function_output means with multcomp
#output (usually tukey)
function_output
summary(compare_cont_tukey)
#all different here, so
function_output$comparison <- letters[1:3]
ggplot(function_output, aes(x=Species, y=Sepal.Length)) +
geom_point(aes(fill=Species), size = 3) +
geom_errorbar(aes(ymin=Sepal.Length-ci, ymax=Sepal.Length+ci), size=1.5) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))+
#offset from mean by hand
geom_text(aes(x = Species, y = mean + .35, label = comparison), size = 28)
#another option for labelling if you are doing tukey is to automate (easy for more comparisons)
post_hoc_spray_cld <- cld(compare_cont_tukey)
#fortify to make a dataframe
post_hoc_spray_cld_fortify <- fortify(post_hoc_spray_cld)
#rename lhs to group and then merge (rename to whatever you used as groupvar in
#summary SE)
names(post_hoc_spray_cld_fortify)[names(post_hoc_spray_cld_fortify) == "lhs"] <- "Species"
function_output <- merge(function_output, post_hoc_spray_cld_fortify)
#plot
ggplot(function_output, aes(x=Species, y=Sepal.Length)) +
geom_point(aes(fill=Species), size = 3) +
geom_errorbar(aes(ymin=Sepal.Length-ci, ymax=Sepal.Length+ci), size=1.5) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))+
#offset from mean by hand
geom_text(aes(x = Species, y = Sepal.Length + .35, label = letters), size = 28)
#kruskal.wallis####
kruskal.test(Sepal.Length ~ Species, data = iris)
pairwise.wilcox.test(iris$Sepal.Length,
iris$Species,
p.adjust.method="holm")
#building to bootstrap options####
library(WRS2)
#first, just using trimmed means, which help
t1way(Sepal.Length~Species, iris)
contrasts <- lincon(Sepal.Length~Species, iris)
contrasts
#if we don't trim mean, we use Welch's approximation for ANOVA
t1way(Sepal.Length~Species, iris, tr = 0)
contrasts <- lincon(Sepal.Length~Species, iris, tr = 0)
contrasts
#to actually use the bootstrap version
t1waybt(Sepal.Length~Species, iris)
bootstrap_post_hoc <- mcppb20(Sepal.Length~Species, iris)
#use p.adjust to correct for FWER
p.adjust(as.numeric(bootstrap_post_hoc$comp[,6]), "holm")
|
/lecture_files/7_ANOVAs.R
|
no_license
|
kkruegerhirt/CUNY-BioStats
|
R
| false | false | 14,724 |
r
|
#figures and tests from ANOVA lecture
#iris example, from first lecture
#
library(ggplot2)
#point plot####
ggplot(iris, aes(Species,Sepal.Length)) +
geom_point(aes(colour=Species), size = 3) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))
#bar chart with error bars ####
require(Rmisc)
function_output <- summarySE(iris, measurevar="Sepal.Length", groupvars =
c("Species"))
ggplot(function_output, aes(x=Species, y=Sepal.Length)) +
geom_col(aes(fill=Species), size = 3) +
geom_errorbar(aes(ymin=Sepal.Length-ci, ymax=Sepal.Length+ci), size=1.5) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))
#boxplot####
ggplot(iris, aes(Species,Sepal.Length)) +
geom_boxplot(aes(colour=Species), size = 3) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))
#stacked histogram####
ggplot(iris, aes(Sepal.Length)) +
geom_histogram(aes(fill=Species), size=3) +
xlab("Sepal Length (cm)")+
ylab("Frequency")+
ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))
#facet####
ggplot(iris, aes(Sepal.Length)) +
geom_histogram(aes(fill=Species), size=3) +
xlab("Sepal Length (cm)")+
ylab("Frequency")+
ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
legend.position = "bottom",
plot.title = element_text(hjust = 0.5, face="bold", size=32))+
facet_wrap(~Species, ncol = 1)
#point plot with means####
ggplot(iris, aes(Species,Sepal.Length)) +
geom_point(aes(colour=Species), size = 3) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32)) +
geom_hline(aes(yintercept=mean(iris$Sepal.Length)), size = 2, color = "orange")
#point plot with means for each group####
#make extra column for output and rename here
function_output$Sepal.Length <- function_output$mean
ggplot(iris, aes(Species,Sepal.Length)) +
geom_point(aes(colour=Species), size = 3) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32)) +
geom_errorbar(aes(ymin=Sepal.Length, ymax=Sepal.Length), size=1.5,
data = function_output, color = "black")
#combine these two ####
ggplot(iris, aes(Species,Sepal.Length)) +
geom_point(aes(colour=Species), size = 3) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32)) +
geom_errorbar(aes(ymin=Sepal.Length, ymax=Sepal.Length), size=1.5,
data = function_output, color = "black") +
geom_hline(aes(yintercept=mean(iris$Sepal.Length)), size = 2, color = "orange")
#show null hypothesis
ggplot(iris, aes(Species,Sepal.Length)) +
geom_point(aes(colour=Species), size = 3) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32)) +
geom_errorbar(aes(ymin=Sepal.Length, ymax=Sepal.Length), size=1.5,
data = function_output, color = "black") +
geom_hline(aes(yintercept=mean(iris$Sepal.Length)), size = 2, color = "orange") +
geom_errorbar(aes(ymin=mean(iris$Sepal.Length), ymax=mean(iris$Sepal.Length)), size=1.5, color = "purple", linetype = 3)
#p value via simulation ####
#get overall variance and mean estimate
variance_estimate <- sum((function_output$N -1) * (function_output$sd)^2)/(sum(function_output$N)-length(function_output$N))
mean_sepal <- mean(iris$Sepal.Length)
#sample
ratio <- data.frame(rep = 1:10000, mse = rep(NA,10000),
msg = rep(NA,10000), ratio = rep(NA,10000))
for(i in 1:10000){
setosa <- rnorm(50, mean_sepal, sd= sqrt(variance_estimate))
versicolor <- rnorm(50, mean_sepal, sd= sqrt(variance_estimate))
virginica <- rnorm(50, mean_sepal, sd= sqrt(variance_estimate))
mean_overall <- mean(c(setosa, versicolor, virginica))
ratio$mse[i] <- (49 * var(setosa) + 49 * var(versicolor) + 49 * var(virginica))/(150 - 3)
ratio$msg[i] <- (50 * (mean(setosa)-mean_overall)^2 +
50 * (mean(versicolor)-mean_overall)^2 +
50 * (mean (virginica)-mean_overall)^2)/2
ratio$ratio[i] <- ratio$msg[i]/ratio$mse[i]
}
summary(lm(Sepal.Length~Species, iris))$fstatistic[1]
ggplot(ratio, aes(ratio)) +
geom_histogram(aes(y=..count../sum(..count..)), fill = "orange", bins = 15) +
ggtitle("Ratio under null hypothesis") +
ylab("Probability") +
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))
#with f####
#notice i specify degrees of freedom for numerator (df1) and denomitor (df2)
ggplot(ratio, aes(ratio)) +
geom_histogram(aes(y=..count../sum(..count..)), fill = "orange",
breaks = seq(0,15,1)) +
stat_function(fun = df, args = list(df1 =2, df2 = 147),size = 3, color = "green") +
ggtitle("Signal under null hypothesis") +
ggtitle("Ratio under null hypothesis") +
ylab("Probability") +
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))
#build model####
iris_anova <- lm(Sepal.Length~Species, iris)
#it creates an object you can manipulate
#check assumptions####
par(mfrow = c(2,2))
plot(iris_anova)
#look at outputs using p-values
summary(iris_anova)
#you can get specific group means by builidng model without intercept
iris_anova_no_intercept <- lm(Sepal.Length~Species - 1, iris)
summary(iris_anova_no_intercept)
#but don't do this in general (R and F stats will be skewed!)
#just use to get group coefficients if needed
#note relationship between model coefficients here as well
#Anova command from car package is needed to give overall group p-value
library(car)
Anova(iris_anova, type = "III")
#since our overall anova was significant, we need to carry out multiple comparisons
#to see what groups are driving the difference
#multcomp
library(multcomp)
#where's the almost difference? use Tukey's HSD for all pairs
library(multcomp)
compare_cont_tukey <- glht(iris_anova, linfct = mcp(Species = "Tukey"))
summary(compare_cont_tukey)
#or you can specify what you want to consider
compare_virginica_only <- glht(iris_anova, linfct = mcp(Species =
c("virginica - versicolor = 0",
"virginica - setosa = 0")))
#remember to control for error rates!
summary(compare_virginica_only, test=adjusted("holm"))
#other options
summary(compare_virginica_only, test=adjusted("fdr"))
#we can "find" significance more easily, but you need to justify why you did this
#we can "find" significance more easily, but you need to justify why you did this
#using set contrasts####
#are versicolor and virginica different from setosa
contr <- rbind("setosa - virginica - setosa" = c(-1,.5,.5))
summary(glht(iris_anova, linfct = mcp(Species = contr)))
summary(iris_anova) # this appears to be right
#orthogonal
contr <- rbind("setosa - virginica - setosa" = c(-1,.5,.5),
"virginica - setosa" = c(0,-1,1))
summary(glht(iris_anova, linfct = mcp(Species = contr)))
summary(iris_anova) # this appears to be right
#R2####
#R2 explains how much variation is explained by our model (groups in this instance)
summary(iris_anova)
#or
summary(iris_anova)$r.squared
#note you can have a low p-value model that also explains only small amount of
#variation in the data
#graphical comparison####
#add comparison portion to fuction_output
function_output$comparison <- "NA"
#enter by hand for small groups by comparing function_output means with multcomp
#output (usually tukey)
function_output
summary(compare_cont_tukey)
#all different here, so
function_output$comparison <- letters[1:3]
ggplot(function_output, aes(x=Species, y=Sepal.Length)) +
geom_point(aes(fill=Species), size = 3) +
geom_errorbar(aes(ymin=Sepal.Length-ci, ymax=Sepal.Length+ci), size=1.5) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))+
#offset from mean by hand
geom_text(aes(x = Species, y = mean + .35, label = comparison), size = 28)
#another option for labelling if you are doing tukey is to automate (easy for more comparisons)
post_hoc_spray_cld <- cld(compare_cont_tukey)
#fortify to make a dataframe
post_hoc_spray_cld_fortify <- fortify(post_hoc_spray_cld)
#rename lhs to group and then merge (rename to whatever you used as groupvar in
#summary SE)
names(post_hoc_spray_cld_fortify)[names(post_hoc_spray_cld_fortify) == "lhs"] <- "Species"
function_output <- merge(function_output, post_hoc_spray_cld_fortify)
#plot
ggplot(function_output, aes(x=Species, y=Sepal.Length)) +
geom_point(aes(fill=Species), size = 3) +
geom_errorbar(aes(ymin=Sepal.Length-ci, ymax=Sepal.Length+ci), size=1.5) +
ylab("Sepal Length (cm)")+ggtitle("Sepal Length of various iris species")+
theme(axis.title.x = element_text(face="bold", size=28),
axis.title.y = element_text(face="bold", size=28),
axis.text.y = element_text(size=20),
axis.text.x = element_text(size=20),
legend.text =element_text(size=20),
legend.title = element_text(size=20, face="bold"),
plot.title = element_text(hjust = 0.5, face="bold", size=32))+
#offset from mean by hand
geom_text(aes(x = Species, y = Sepal.Length + .35, label = letters), size = 28)
#kruskal.wallis####
kruskal.test(Sepal.Length ~ Species, data = iris)
pairwise.wilcox.test(iris$Sepal.Length,
iris$Species,
p.adjust.method="holm")
#building to bootstrap options####
library(WRS2)
#first, just using trimmed means, which help
t1way(Sepal.Length~Species, iris)
contrasts <- lincon(Sepal.Length~Species, iris)
contrasts
#if we don't trim mean, we use Welch's approximation for ANOVA
t1way(Sepal.Length~Species, iris, tr = 0)
contrasts <- lincon(Sepal.Length~Species, iris, tr = 0)
contrasts
#to actually use the bootstrap version
t1waybt(Sepal.Length~Species, iris)
bootstrap_post_hoc <- mcppb20(Sepal.Length~Species, iris)
#use p.adjust to correct for FWER
p.adjust(as.numeric(bootstrap_post_hoc$comp[,6]), "holm")
|
## The following pair of functions aim to cache the inverse of a matrix
## This function creates a special 'matrix' object, which is a list containing
## functions to: set the matrix, get the matrix, set the inverse the matrix and
## get the inverse of the matrix.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setinv <- function(z) inv <<- z
getinv <- function() inv
list(set = set, get = get, setinv = setinv, getinv = getinv)
}
## This function calculates the inverse of a matrix.
## The function first checks to see if the inverse of the matrix has already
## been calculated. If so, it retrieves the object from the cache. Otherwise,
## this function does the inversion and sets the object in the cache.
cacheSolve <- function(x, ...) {
inv <- x$getinv()
if(!is.null(inv)) {
message("getting cached data")
return(inv)
}
data <- x$get()
inv <- solve(data, ...)
x$setinv(inv)
inv
}
|
/ProgrammingAssignment2/cachematrix.R
|
no_license
|
rouille/CourseraDataScienceJHU
|
R
| false | false | 1,056 |
r
|
## The following pair of functions aim to cache the inverse of a matrix
## This function creates a special 'matrix' object, which is a list containing
## functions to: set the matrix, get the matrix, set the inverse the matrix and
## get the inverse of the matrix.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setinv <- function(z) inv <<- z
getinv <- function() inv
list(set = set, get = get, setinv = setinv, getinv = getinv)
}
## This function calculates the inverse of a matrix.
## The function first checks to see if the inverse of the matrix has already
## been calculated. If so, it retrieves the object from the cache. Otherwise,
## this function does the inversion and sets the object in the cache.
cacheSolve <- function(x, ...) {
inv <- x$getinv()
if(!is.null(inv)) {
message("getting cached data")
return(inv)
}
data <- x$get()
inv <- solve(data, ...)
x$setinv(inv)
inv
}
|
# File name: 04a_plot_marg_fitted_xp09.R
# Online archive: gitlab
# Authors: Brice Beffara & Amélie Bret
# Tue Jun 26 11:57:07 2018 ------------------------------
# Contact: brice.beffara@slowpen.science amelie.bret@univ-grenoble-alpes.fr http://slowpen.science
#
# This R script was used to create the marginal plots from brms models
# corresponding to the 10th experiment of Amelie Bret's doctoral work
#
# This R script plots marginal effect corresponding to the association
# of RWA with grebles' ratings in all combinations of the other indepentdent variables :
# usvalence : positive (0.5) vs. negative (-0.5)
# and load : load (0.5) vs. no load (-0.5)
#
# This program is believed to be free of errors, but it comes with no guarantee!
# The user bears all responsibility for interpreting the results.
#
# This preambule is largely inspired by John K. Kruschke's work at https://osf.io/wp2ry/
#
### To run this program, please do the following:
### 1. Install the general-purpose programming language R from
### http://www.r-project.org/
### Install the version of R appropriate for your computer's operating
### system (Windows, MacOS, or Linux).
### 2. Install the R editor, RStudio, from
### http://rstudio.org/
### This editor is not necessary, but highly recommended.
### 3. After the above actions are accomplished, this program should
### run as-is in R. You may "source" it to run the whole thing at once,
### or, preferably, run lines consecutively from the beginning.
################################################################################
# Loading packages needed (and installing if necessary) for this part
if (!require("pacman")) install.packages("pacman")
p_load(ggplot2, # main package for plots
colorRamps, # to add color palettes
ggpubr, # to combine plots
extrafontdb, # to get more available fonts for plots
hrbrthemes, # for ggplot2 theme
extrafont,
ggExtra,
install = TRUE,
update = getOption("pac_update"),
character.only = FALSE)
#------------------------------------------------------------------------------------
# First we determine all the possible combinations of modalities
# between the two categorical independent variables :
# "load" and "usvalence"
#------------------------------------------------------------------------------------
# !!orginally!! conditioned stimuli with !!negative!! valence
cond_on <- data.frame(load = -0.5, usvalence = -0.5,
cond__ = "noload_negative")
# !!orginally!! conditioned stimuli with !!positive!! valence
cond_op <- data.frame(load = -0.5, usvalence = 0.5,
cond__ = "noload_positive")
# !!spread!! conditionned stimuli with !!negative!! valence
cond_sn <- data.frame(load = 0.5, usvalence = -0.5,
cond__ = "load_negative")
# !!spread!! conditionned stimuli with !!positive!! valence
cond_sp <- data.frame(load = 0.5, usvalence = 0.5,
cond__ = "load_positive")
#------------------------------------------------------------------------------------
# We then select the marginal effects of RWA from the model
# for each combination of modalities
#------------------------------------------------------------------------------------
# !!orginally!! conditioned stimuli with !!negative!! valence
marg_on <- marginal_effects(load_resp, effects = "RWAscore", ordinal = TRUE,
conditions = cond_on, method = c("fitted"), # here his where we specify the combination
re_formula = NULL)
# !!orginally!! conditioned stimuli with !!positive!! valence
marg_op <- marginal_effects(load_resp, effects = "RWAscore", ordinal = TRUE,
conditions = cond_op, method = c("fitted"), # here his where we specify the combination
re_formula = NULL)
# !!spread!! conditionned stimuli with !!negative!! valence
marg_sn <- marginal_effects(load_resp, effects = "RWAscore", ordinal = TRUE,
conditions = cond_sn, method = c("fitted"), # here his where we specify the combination
re_formula = NULL)
# !!spread!! conditionned stimuli with !!positive!! valence
marg_sp <- marginal_effects(load_resp, effects = "RWAscore", ordinal = TRUE,
conditions = cond_sp, method = c("fitted"), # here his where we specify the combination
re_formula = NULL)
#------------------------------------------------------------------------------------
# Now we can plot marginal effects for each combination of conditions...
#------------------------------------------------------------------------------------
# load fonts and themes
hrbrthemes::import_roboto_condensed()
loadfonts()
# !!orginally!! conditioned stimuli with !!negative!! valence
marg_plot_on = plot(marg_on, plot = FALSE)[[1]] + # here his where we specify the marginal effects of interest
scale_fill_gradientn(colors = matlab.like(10), na.value = "transparent") +
scale_y_continuous(name="Évaluations", breaks = c(1, 2, 3, 4, 5, 6, 7, 8, 9), expand = c(0,0)) +
scale_x_continuous(name="RWA", breaks = scales::pretty_breaks(n = 10), expand=c(0,0)) +
labs(fill="Probabilité",
subtitle="Musique neutre pendant le contre-conditionnement positif") + # here his where we mention the marginal effects of interest
theme_ipsum_rc(base_size = 13,
subtitle_size = 20,
axis_title_size = 15) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# !!orginally!! conditioned stimuli with !!positive!! valence
marg_plot_op = plot(marg_op, plot = FALSE)[[1]] + # here his where we specify the marginal effects of interest
scale_fill_gradientn(colors = matlab.like(10), na.value = "transparent") +
scale_y_continuous(name="Évaluations", breaks = c(1, 2, 3, 4, 5, 6, 7, 8, 9), expand = c(0,0)) +
scale_x_continuous(name="RWA", breaks = scales::pretty_breaks(n = 10), expand=c(0,0)) +
labs(fill="Probabilité",
subtitle="Musique neutre pendant le contre-conditionnement négatif") + # here his where we mention the marginal effects of interest
theme_ipsum_rc(base_size = 13,
subtitle_size = 20,
axis_title_size = 15) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# !!spread!! conditionned stimuli with !!negative!! valence
marg_plot_sn = plot(marg_sn, plot = FALSE)[[1]] + # here his where we specify the marginal effects of interest
scale_fill_gradientn(colors = matlab.like(10), na.value = "transparent") +
scale_y_continuous(name="Évaluations", breaks = c(1, 2, 3, 4, 5, 6, 7, 8, 9), expand = c(0,0)) +
scale_x_continuous(name="RWA", breaks = scales::pretty_breaks(n = 10), expand=c(0,0)) +
labs(fill="Probabilité",
subtitle="Charge cognitive pendant le contre-conditionnement positif") + # here his where we mention the marginal effects of interest
theme_ipsum_rc(base_size = 13,
subtitle_size = 20,
axis_title_size = 15) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# !!spread!! conditionned stimuli with !!positive!! valence
marg_plot_sp = plot(marg_sp, plot = FALSE)[[1]] + # here his where we specify the marginal effects of interest
scale_fill_gradientn(colors = matlab.like(10), na.value = "transparent") +
scale_y_continuous(name="Évaluations", breaks = c(1, 2, 3, 4, 5, 6, 7, 8, 9), expand = c(0,0)) +
scale_x_continuous(name="RWA", breaks = scales::pretty_breaks(n = 10), expand=c(0,0)) +
labs(fill="Probabilité",
subtitle="Charge cognitive pendant le contre-conditionnement négatif") + # here his where we mention the marginal effects of interest
theme_ipsum_rc(base_size = 13,
subtitle_size = 20,
axis_title_size = 15) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
#------------------------------------------------------------------------------------
# ...and combine plots together
#------------------------------------------------------------------------------------
# Combine plot
marg_all <- ggarrange(marg_plot_on,
marg_plot_op,
marg_plot_sn,
marg_plot_sp,
ncol = 2, nrow = 2)
# uncomment to display plot
# marg_all
# save plot
ggsave("plots/marg_all_xp09_french.pdf", width = 50, height = 30, units = "cm")
|
/drafts/xp09/04a_plot_marg_fitted_xp09.R
|
no_license
|
bricebeffara/rwa_attitude_change
|
R
| false | false | 8,624 |
r
|
# File name: 04a_plot_marg_fitted_xp09.R
# Online archive: gitlab
# Authors: Brice Beffara & Amélie Bret
# Tue Jun 26 11:57:07 2018 ------------------------------
# Contact: brice.beffara@slowpen.science amelie.bret@univ-grenoble-alpes.fr http://slowpen.science
#
# This R script was used to create the marginal plots from brms models
# corresponding to the 10th experiment of Amelie Bret's doctoral work
#
# This R script plots marginal effect corresponding to the association
# of RWA with grebles' ratings in all combinations of the other indepentdent variables :
# usvalence : positive (0.5) vs. negative (-0.5)
# and load : load (0.5) vs. no load (-0.5)
#
# This program is believed to be free of errors, but it comes with no guarantee!
# The user bears all responsibility for interpreting the results.
#
# This preambule is largely inspired by John K. Kruschke's work at https://osf.io/wp2ry/
#
### To run this program, please do the following:
### 1. Install the general-purpose programming language R from
### http://www.r-project.org/
### Install the version of R appropriate for your computer's operating
### system (Windows, MacOS, or Linux).
### 2. Install the R editor, RStudio, from
### http://rstudio.org/
### This editor is not necessary, but highly recommended.
### 3. After the above actions are accomplished, this program should
### run as-is in R. You may "source" it to run the whole thing at once,
### or, preferably, run lines consecutively from the beginning.
################################################################################
# Loading packages needed (and installing if necessary) for this part
if (!require("pacman")) install.packages("pacman")
p_load(ggplot2, # main package for plots
colorRamps, # to add color palettes
ggpubr, # to combine plots
extrafontdb, # to get more available fonts for plots
hrbrthemes, # for ggplot2 theme
extrafont,
ggExtra,
install = TRUE,
update = getOption("pac_update"),
character.only = FALSE)
#------------------------------------------------------------------------------------
# First we determine all the possible combinations of modalities
# between the two categorical independent variables :
# "load" and "usvalence"
#------------------------------------------------------------------------------------
# !!orginally!! conditioned stimuli with !!negative!! valence
cond_on <- data.frame(load = -0.5, usvalence = -0.5,
cond__ = "noload_negative")
# !!orginally!! conditioned stimuli with !!positive!! valence
cond_op <- data.frame(load = -0.5, usvalence = 0.5,
cond__ = "noload_positive")
# !!spread!! conditionned stimuli with !!negative!! valence
cond_sn <- data.frame(load = 0.5, usvalence = -0.5,
cond__ = "load_negative")
# !!spread!! conditionned stimuli with !!positive!! valence
cond_sp <- data.frame(load = 0.5, usvalence = 0.5,
cond__ = "load_positive")
#------------------------------------------------------------------------------------
# We then select the marginal effects of RWA from the model
# for each combination of modalities
#------------------------------------------------------------------------------------
# !!orginally!! conditioned stimuli with !!negative!! valence
marg_on <- marginal_effects(load_resp, effects = "RWAscore", ordinal = TRUE,
conditions = cond_on, method = c("fitted"), # here his where we specify the combination
re_formula = NULL)
# !!orginally!! conditioned stimuli with !!positive!! valence
marg_op <- marginal_effects(load_resp, effects = "RWAscore", ordinal = TRUE,
conditions = cond_op, method = c("fitted"), # here his where we specify the combination
re_formula = NULL)
# !!spread!! conditionned stimuli with !!negative!! valence
marg_sn <- marginal_effects(load_resp, effects = "RWAscore", ordinal = TRUE,
conditions = cond_sn, method = c("fitted"), # here his where we specify the combination
re_formula = NULL)
# !!spread!! conditionned stimuli with !!positive!! valence
marg_sp <- marginal_effects(load_resp, effects = "RWAscore", ordinal = TRUE,
conditions = cond_sp, method = c("fitted"), # here his where we specify the combination
re_formula = NULL)
#------------------------------------------------------------------------------------
# Now we can plot marginal effects for each combination of conditions...
#------------------------------------------------------------------------------------
# load fonts and themes
hrbrthemes::import_roboto_condensed()
loadfonts()
# !!orginally!! conditioned stimuli with !!negative!! valence
marg_plot_on = plot(marg_on, plot = FALSE)[[1]] + # here his where we specify the marginal effects of interest
scale_fill_gradientn(colors = matlab.like(10), na.value = "transparent") +
scale_y_continuous(name="Évaluations", breaks = c(1, 2, 3, 4, 5, 6, 7, 8, 9), expand = c(0,0)) +
scale_x_continuous(name="RWA", breaks = scales::pretty_breaks(n = 10), expand=c(0,0)) +
labs(fill="Probabilité",
subtitle="Musique neutre pendant le contre-conditionnement positif") + # here his where we mention the marginal effects of interest
theme_ipsum_rc(base_size = 13,
subtitle_size = 20,
axis_title_size = 15) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# !!orginally!! conditioned stimuli with !!positive!! valence
marg_plot_op = plot(marg_op, plot = FALSE)[[1]] + # here his where we specify the marginal effects of interest
scale_fill_gradientn(colors = matlab.like(10), na.value = "transparent") +
scale_y_continuous(name="Évaluations", breaks = c(1, 2, 3, 4, 5, 6, 7, 8, 9), expand = c(0,0)) +
scale_x_continuous(name="RWA", breaks = scales::pretty_breaks(n = 10), expand=c(0,0)) +
labs(fill="Probabilité",
subtitle="Musique neutre pendant le contre-conditionnement négatif") + # here his where we mention the marginal effects of interest
theme_ipsum_rc(base_size = 13,
subtitle_size = 20,
axis_title_size = 15) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# !!spread!! conditionned stimuli with !!negative!! valence
marg_plot_sn = plot(marg_sn, plot = FALSE)[[1]] + # here his where we specify the marginal effects of interest
scale_fill_gradientn(colors = matlab.like(10), na.value = "transparent") +
scale_y_continuous(name="Évaluations", breaks = c(1, 2, 3, 4, 5, 6, 7, 8, 9), expand = c(0,0)) +
scale_x_continuous(name="RWA", breaks = scales::pretty_breaks(n = 10), expand=c(0,0)) +
labs(fill="Probabilité",
subtitle="Charge cognitive pendant le contre-conditionnement positif") + # here his where we mention the marginal effects of interest
theme_ipsum_rc(base_size = 13,
subtitle_size = 20,
axis_title_size = 15) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# !!spread!! conditionned stimuli with !!positive!! valence
marg_plot_sp = plot(marg_sp, plot = FALSE)[[1]] + # here his where we specify the marginal effects of interest
scale_fill_gradientn(colors = matlab.like(10), na.value = "transparent") +
scale_y_continuous(name="Évaluations", breaks = c(1, 2, 3, 4, 5, 6, 7, 8, 9), expand = c(0,0)) +
scale_x_continuous(name="RWA", breaks = scales::pretty_breaks(n = 10), expand=c(0,0)) +
labs(fill="Probabilité",
subtitle="Charge cognitive pendant le contre-conditionnement négatif") + # here his where we mention the marginal effects of interest
theme_ipsum_rc(base_size = 13,
subtitle_size = 20,
axis_title_size = 15) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
#------------------------------------------------------------------------------------
# ...and combine plots together
#------------------------------------------------------------------------------------
# Combine plot
marg_all <- ggarrange(marg_plot_on,
marg_plot_op,
marg_plot_sn,
marg_plot_sp,
ncol = 2, nrow = 2)
# uncomment to display plot
# marg_all
# save plot
ggsave("plots/marg_all_xp09_french.pdf", width = 50, height = 30, units = "cm")
|
## makeCacheMatrix is really a list of functions. cacheSolve function computes the
## inverse of the special "matrix" returned by makeCacheMatrix.
## Within the makeCacheMatrix function, "set" function sets the matrix to a new
## matrix y, "get" returns the matrix, "setinv" sets the inverse of the matrix, and
## "getinv" returns the inverse of the matrix.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- Null
}
get <- function() x
setinv <- function(solve) inv <<- solve
getinv <- function() inv
list(set = set, get = get, setinv = setinv, getinv = getinv)
}
## In the cacheSolve function, if the inverse has already been calculated (and the
## matrix has not changed), then the cachesolve should retrieve the inverse from the
## cache.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inv <- x$getinv()
if(!is.null(inv)) {
message("getting cached data")
return(inv)
}
data <- x$get()
inv <- solve(data, ...)
x$setinv(inv)
inv
}
|
/cachematrix.R
|
no_license
|
bahman20/Assignment2
|
R
| false | false | 1,240 |
r
|
## makeCacheMatrix is really a list of functions. cacheSolve function computes the
## inverse of the special "matrix" returned by makeCacheMatrix.
## Within the makeCacheMatrix function, "set" function sets the matrix to a new
## matrix y, "get" returns the matrix, "setinv" sets the inverse of the matrix, and
## "getinv" returns the inverse of the matrix.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- Null
}
get <- function() x
setinv <- function(solve) inv <<- solve
getinv <- function() inv
list(set = set, get = get, setinv = setinv, getinv = getinv)
}
## In the cacheSolve function, if the inverse has already been calculated (and the
## matrix has not changed), then the cachesolve should retrieve the inverse from the
## cache.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inv <- x$getinv()
if(!is.null(inv)) {
message("getting cached data")
return(inv)
}
data <- x$get()
inv <- solve(data, ...)
x$setinv(inv)
inv
}
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Do not modify this file since it was automatically generated from:
%
% Reporter.R
%
% by the Rdoc compiler part of the R.oo package.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\name{write.Reporter}
\alias{write.Reporter}
\alias{Reporter.write}
\alias{write.Reporter}
\alias{write,Reporter-method}
\title{Writes objects to document}
\usage{\method{write}{Reporter}(this, ..., sep="", line=FALSE)}
\arguments{
\item{...}{The object to be written.}
\item{sep}{Separator between object. Default value is \code{""} (note
the difference from \code{cat()} and \code{paste()}.}
\item{line}{If \code{\link[base:logical]{TRUE}}, a newline is written at the end, otherwise not.}
}
\description{
Writes objects to document.
}
\value{
Returns nothing.
}
\author{Henrik Bengtsson (\url{http://www.braju.com/R/})}
\seealso{
For more information see \code{\link{Reporter}}.
}
\keyword{internal}
\keyword{methods}
|
/man/write.Reporter.Rd
|
no_license
|
HenrikBengtsson/R.io
|
R
| false | false | 1,075 |
rd
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Do not modify this file since it was automatically generated from:
%
% Reporter.R
%
% by the Rdoc compiler part of the R.oo package.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\name{write.Reporter}
\alias{write.Reporter}
\alias{Reporter.write}
\alias{write.Reporter}
\alias{write,Reporter-method}
\title{Writes objects to document}
\usage{\method{write}{Reporter}(this, ..., sep="", line=FALSE)}
\arguments{
\item{...}{The object to be written.}
\item{sep}{Separator between object. Default value is \code{""} (note
the difference from \code{cat()} and \code{paste()}.}
\item{line}{If \code{\link[base:logical]{TRUE}}, a newline is written at the end, otherwise not.}
}
\description{
Writes objects to document.
}
\value{
Returns nothing.
}
\author{Henrik Bengtsson (\url{http://www.braju.com/R/})}
\seealso{
For more information see \code{\link{Reporter}}.
}
\keyword{internal}
\keyword{methods}
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/stata.R
\name{theme_stata}
\alias{theme_stata}
\title{Themes based on Stata graph schemes}
\usage{
theme_stata(base_size = 11, base_family = "sans", scheme = "s2color")
}
\arguments{
\item{base_size}{base font size}
\item{base_family}{base font family}
\item{scheme}{One of "s2color", "s2mono", "s1color", "s1rcolor", or "s1mono", "s2manual",
"s1manual", or "sj"}
}
\description{
Themes based on Stata graph schemes
}
\note{
Stata graph schemes include what \pkg{ggplot2} seperates
into themes and scales, as well as defaults specific to different
graph types (which ggplot does not support).
}
\examples{
dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
q1 <- (qplot(carat, price, data=dsamp, colour=clarity)
+ ggtitle("Diamonds"))
q2 <- (qplot(carat, price, data=dsamp)
+ facet_wrap(~ clarity)
+ ggtitle("Diamonds"))
q1mono <- (qplot(carat, price, shape=clarity, color=clarity,
data=dsamp)
+ scale_shape_stata()
+ ggtitle("Diamonds"))
## s2color
(q1 + theme_stata() + scale_colour_stata(scheme = "s2color"))
(q2 + theme_stata())
## s2mono
(q1mono + theme_stata(scheme = "s2mono") + scale_colour_stata("mono"))
(q2 + theme_stata(scheme = "s2mono"))
## s1color
(q1 + theme_stata(scheme = "s1color") + scale_colour_stata("s1color"))
(q2 + theme_stata(scheme = "s1color"))
## s1rcolor
(q1 + theme_stata(scheme = "s1rcolor") + scale_colour_stata("s1rcolor"))
(ggplot(dsamp, aes(x=carat, y=price)) + geom_point(colour="white")
+ facet_wrap(~ clarity) + scale_colour_stata("s1rcolor")
+ ggtitle("Diamonds"))
## s1mono
(q1mono + theme_stata(scheme = "s1mono") + scale_colour_stata("mono"))
(q2 + theme_stata(scheme = "s1mono"))
}
\references{
\url{http://www.stata.com/help.cgi?schemes}
}
|
/man/theme_stata.Rd
|
no_license
|
nishantsbi/ggthemes
|
R
| false | false | 1,829 |
rd
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/stata.R
\name{theme_stata}
\alias{theme_stata}
\title{Themes based on Stata graph schemes}
\usage{
theme_stata(base_size = 11, base_family = "sans", scheme = "s2color")
}
\arguments{
\item{base_size}{base font size}
\item{base_family}{base font family}
\item{scheme}{One of "s2color", "s2mono", "s1color", "s1rcolor", or "s1mono", "s2manual",
"s1manual", or "sj"}
}
\description{
Themes based on Stata graph schemes
}
\note{
Stata graph schemes include what \pkg{ggplot2} seperates
into themes and scales, as well as defaults specific to different
graph types (which ggplot does not support).
}
\examples{
dsamp <- diamonds[sample(nrow(diamonds), 1000), ]
q1 <- (qplot(carat, price, data=dsamp, colour=clarity)
+ ggtitle("Diamonds"))
q2 <- (qplot(carat, price, data=dsamp)
+ facet_wrap(~ clarity)
+ ggtitle("Diamonds"))
q1mono <- (qplot(carat, price, shape=clarity, color=clarity,
data=dsamp)
+ scale_shape_stata()
+ ggtitle("Diamonds"))
## s2color
(q1 + theme_stata() + scale_colour_stata(scheme = "s2color"))
(q2 + theme_stata())
## s2mono
(q1mono + theme_stata(scheme = "s2mono") + scale_colour_stata("mono"))
(q2 + theme_stata(scheme = "s2mono"))
## s1color
(q1 + theme_stata(scheme = "s1color") + scale_colour_stata("s1color"))
(q2 + theme_stata(scheme = "s1color"))
## s1rcolor
(q1 + theme_stata(scheme = "s1rcolor") + scale_colour_stata("s1rcolor"))
(ggplot(dsamp, aes(x=carat, y=price)) + geom_point(colour="white")
+ facet_wrap(~ clarity) + scale_colour_stata("s1rcolor")
+ ggtitle("Diamonds"))
## s1mono
(q1mono + theme_stata(scheme = "s1mono") + scale_colour_stata("mono"))
(q2 + theme_stata(scheme = "s1mono"))
}
\references{
\url{http://www.stata.com/help.cgi?schemes}
}
|
library(MASS)
library(cluster)
library(emdbook)
library(dplyr)
library(reshape2)
library(RobustGaSP)
library(mvtnorm)
library(Matrix)
library(stringr)
library(GGally)
col_alpha <- function(col_str, alpha){
if(alpha<0|alpha>1){
stop('Alpha must be within (0,1)')
}
rgb_mat <- col2rgb(col_str,alpha = TRUE)
return(rgb(rgb_mat[1],rgb_mat[2],rgb_mat[3],alpha = 255*alpha, maxColorValue = 255))
}
# base_save_path = "/Users/Jake/Box Sync/Research/Computer_Emulation/Shanshan_Project/Results/"
# save_folder = ""
#
# save_path = paste0(base_save_path,save_folder)
##########################################################################
####These are plots to assess the sampler from jetscape_2_concatenate.R
##########################################################################
#Test for Normality
#None are <0.05 even without accounting for multiple testing
normality_test <- function(Y_final){
for(j in 1:dim(Y_final)[2]){
print(shapiro.test(Y_final[,j])$p.value)
}
}
#How many PCs?
calc_Vq <- function(Y_svd,save_pics = FALSE, plotit = FALSE){
eigs <- Y_svd$d^2
(V_q <- cumsum(eigs)/sum(eigs))
if(plotit){
if(save_pics) pdf(paste0(save_path,'var_explained.pdf'))
plot(V_q[1:6],type = 'o',
xlab = "Total Number of PCs R",
main = "Fraction of Variance Explained",
ylab = expression(F[R]),
cex.lab = 2,
cex.axis = 1.3,
cex.main = 1.4,
mgp = c(2.3,1,0),
pch = 19)
if(save_pics) dev.off()
}
return(V_q)
}
###########
##Results
#########
plot_heatmaps <- function(param_plot,ranges,
param_names = colnames(param_plot),
pairs = NULL,
save_param_names = NULL,
save_pics = FALSE,save_path = "", save_end = "", title = 'Posterior Heatmap',
plt_points = FALSE,design = NULL, ...){
if(save_pics & save_path==""){
print('Warning: saving plots to working directory')
}
#If you don't specify pairs, just do all of them
if(is.null(pairs)){
num_params = dim(param_plot)[2]
num_pairs = sum(1:(num_params-1))
i = 0
pairs = vector('list',num_pairs)
for(k in 1:(num_params-1)){
for(l in k:num_params)
i = i + 1
pairs[[i]] <- c(k,l)
}
}
num_pairs = length(pairs)
if(is.null(save_param_names)){
save_param_names <- vector('list',num_pairs)
}
for(i in 1:num_pairs){
(k = pairs[[i]][1])
(l = pairs[[i]][2])
f1 <- kde2d(param_plot[,k], (param_plot[,l]), n = 100,
lims = c(ranges[[k]],ranges[[l]]))
if(is.null(save_param_names[[i]])){
save_param_names[[i]] <- paste0(param_names[k],'_',param_names[l])
print(save_param_names[[i]])
}
if(save_pics) pdf(paste0(save_path,'heatmap_',save_param_names[[i]],save_end,'.pdf'))
par(mar=c(5.1, 5.1, 4.1, 2.1))
image(f1,
xlab = param_names[k],
ylab = "",
main = title,
cex.lab = 2,
cex.axis = 1.3,
cex.main = 1.4,
mgp = c(3,1,0), ...#,
# xlim = ranges[[1]],
# ylim = ranges[[2]]
)
title(ylab = param_names[l],
mgp = c(2.1,1,0),
cex.lab = 2,
cex.axis = 1.3)
perc_lvl = c(.6,.75,.9)
HPDregionplot(param_plot[,c(k,l)], prob = perc_lvl,
col=c("black"), lty = c(1,5,3), add=TRUE)
if(plt_points){
points(design[,k],design[,l],pch = 19, cex = 0.7)
}
# legend('topright',paste0(perc_lvl*100,"%"),title = "Highest Density Kernel Estimate",
# lty = c(1,5,3),
# bty="n")
if(save_pics) dev.off()
}
}
make_param_plot <- function(res_params, ranges){
param_plot <- matrix(0,dim(res_params)[1],dim(res_params)[2])
for(j in 1:dim(param_plot)[2]){
param_plot[,j] <- res_params[,j]*(ranges[[j]][2] - ranges[[j]][1]) + ranges[[j]][1]
}
colnames(param_plot) <- names(ranges)
return(param_plot)
}
unmake_param_plot <- function(param_plot, ranges){
res_params <- matrix(0,dim(param_plot)[1], dim(param_plot)[2])
for(j in 1:dim(res_params)[2]){
res_params[,j] <- (param_plot[,j] - ranges[[j]][1])/(ranges[[j]][2] - ranges[[j]][1])
}
colnames(res_params) <- names(ranges)
return(res_params)
}
transform_draws <- function(param_plot,cols_to_transform = c(1,2),
new_names = c('A','C')){
X = param_plot[,cols_to_transform[1]]
Y = param_plot[,cols_to_transform[2]]
new_param_plot = param_plot
new_param_plot[,1] = X*Y
new_param_plot[,2] = X*(1-Y)
colnames(new_param_plot)[cols_to_transform] = new_names
return(new_param_plot)
}
transform_ranges <- function(ranges,vars_to_transform = c(1,2),
new_names = c('A','C')){
ranges_t <- ranges
ranges_t[[1]] <- c(0,ranges[[1]][2])
ranges_t[[2]] <- c(0, ranges[[1]][2])
names(ranges_t)[1:2] <- new_names
return(ranges_t)
}
heat_pairplot <- function(param_plot,
ranges,
split_num = dim(param_plot)[1],
param_names = colnames(param_plot),
title = 'Calibration Pairplot',save_pics = FALSE,
save_path = "",
save_end = "",
include_legend = FALSE,
legend_names = NULL,
legend_cols = NULL,
off_diag = "heatmap"){
panel.hist <- function(x,col_hist,split_num, ...)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x[1:split_num], plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y,xlim = c(0,0.35),col = rgb(1,0,0,.1), ...)
if(split_num<length(x)){
h <- hist(x[(split_num+1):length(x)], plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y,xlim = c(0,0.35),col = rgb(0,0,1,.1), ...)
}
}
panel.dens <- function(x,split_num,...){
usr <- par("usr"); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
plot(density(x[1:split_num],))
h <- hist(x[1:split_num], plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y,xlim = c(0,0.35),col = rgb(1,0,0,.1), ...)
if(split_num<length(x)){
h <- hist(x[(split_num+1):length(x)], plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y,xlim = c(0,0.35),col = rgb(0,0,1,.1), ...)
}
}
panel.scatter <- function(x,y,split_num,...){
first_pts = 1:split_num
points(x[first_pts],y[first_pts],#pch = 19,
col = rgb(1,0,0,.01),cex=.7,
...)
#If you actually want to split
if(split_num<length(x)){
second_pts <- (split_num+1):length(x)
points(x[second_pts],y[second_pts],#pch =19,
col = rgb(0,0,1,.01), cex = 0.7,
...)
}
}
panel.image <- function(x,y,split_num,...){
f1 <- kde2d(x, y, n = 100)
image(f1,add = TRUE)
}
if(off_diag=="scatter"){
panel_off = panel.scatter
}else if(off_diag=="heatmap"){
if(split_num<dim(param_plot)[1]){
stop('Heatmap only appropriate for one set of calibration results')
}
panel_off = panel.image
}else{stop("Whoops off_diag parameter is wrong")}
if(save_pics){ pdf(paste0(save_path,'calibration_pairplot',save_end,".pdf"))}
pairs(param_plot,
panel = panel_off,
diag.panel = panel.hist,
#pch = 19,
#cex = .3,
# col = rgb(1,0,0,.1),
labels = param_names,
upper.panel = NULL,
cex.lab = 1.5,
cex.axis = 1.3,
cex.main = 1.4,
mgp = c(2.3,1,0),
#col_hist = 'green',
las = 2,
main = title,
split_num = split_num)
if(include_legend){
legend('topright',legend_names,col = legend_cols,
pch = 19,inset = .13)
}
if(save_pics) dev.off()
}
gg_heat_pairs <- function(all_data,
ranges,
cols_to_use = 1:4,
scatter_alpha = 0.1,
col_names = c('A','B','C','D')){
both_col <- 'red3'
lhc_col <- 'deepskyblue'
rhic_col <- 'forestgreen'
my_dens <- function(data, mapping, ...) {
ggplot(data = data, mapping=mapping) +
geom_density(..., alpha = 0.7, size = 1.1) +
scale_fill_manual(values=c( both_col, lhc_col, rhic_col )) +
scale_color_manual(values=c( both_col,lhc_col, rhic_col))
}
my_scatter <- function(data,mapping,...){
ggplot(data = data, mapping = mapping) +
geom_jitter(alpha = scatter_alpha,size = .3) +
guides(col = guide_legend(override.aes = list(shape = 15,
size = 8,
alpha = 1),
label.theme = element_text(size = 12))) +
scale_fill_manual(values=c( both_col, lhc_col, rhic_col )) +
scale_color_manual(values=c( both_col,lhc_col, rhic_col)) +
theme(legend.title=element_blank())
}
p <- ggpairs(all_data, mapping = aes(color = Collider), columns = cols_to_use,
lower = list(continuous = my_scatter),
diag = list(continuous = my_dens),
upper = list(continuous = 'blank'),
columnLabels = col_names,
labeller = 'label_parsed'
#,legend = c(2,1)
)
for(i in 1:length(cols_to_use)){
for(j in 1:length(cols_to_use)){
if(j<=i){
p[i,j] <- p[i,j] + xlim(ranges[[j]][1],ranges[[j]][2]) +
theme(axis.text = element_text(size = 11.5))
}
}
}
#p <- p + theme(strip.text = element_text(size = 15))
#Remove yaxis on first plot, to remove confusion
#p[1,1] <- p[1,1] + theme(axis.text.y = element_blank(),
# axis.ticks = element_blank())
points_legend <- gglegend(my_scatter)
p[1,p$ncol] <- points_legend(all_data, ggplot2::aes(all_data[1,cols_to_use[1]],
all_data[1,cols_to_use[2]],
color = Collider))
#p[1,p$ncol-1] <- points_legend(all_data, ggplot2::aes(all_data[1,cols_to_use[1]],
# all_data[1,cols_to_use[2]],
# color = Collider))
#print(p + theme(strip.text = element_text(size = 15)))
print(p)
}
make_combined_pairplot <- function(save_lists,
labels,
ranges,
save_pics = FALSE,
save_path = '',
save_end = '',
cols_to_use = 1:5,
scatter_alpha = 0.1,
num_samples = 1E4,
str_param_to_use = 'transformed_params',
col_names = c('A','B','C','D'),
make_pdf = FALSE){
load(save_lists[1])
data_to_plot <- matrix(,0,ncol(save_list[[str_param_to_use]]))
if(length(save_lists)!=length(labels)){
stop('lists and labels not same length')
}
for(i in 1:length(save_lists)){
load(save_lists[i])
cur_data <- as.data.frame(save_list[[str_param_to_use]]) %>%
dplyr::sample_n(.,num_samples)
cur_data$Collider <- labels[i]
data_to_plot <- rbind(data_to_plot, cur_data)
}
if(save_pics){
if(make_pdf){
pdf(paste0(save_path,'combined_pairplot',save_end,'.pdf'))
}else{
png(paste0(save_path,'combined_pairplot',save_end,'.png'))
}
}
gg_heat_pairs(data_to_plot,
ranges = ranges,
cols_to_use = cols_to_use,
col_names = col_names,
scatter_alpha = scatter_alpha)
if(save_pics) dev.off()
}
plot_lines <- function(pred_dat,exp_dat,
med_pred_vec = NULL,
pT_col = 'pT',
exp_col = 'RAA_exp',
err_col_stat = 'exp_err_stat',
err_col_sys = 'exp_err_sys',
col_str = 'red',
col_pts = 'darkred',
all_col_pts = c('darkred','darkblue'),
all_col_strs = c('red','blue'),
alpha_val = 0.01,
delta = rep(0,dim(exp_dat)[1]),
include_legend = TRUE,
legend_loc = 'top_left',
include_med = FALSE,
add = FALSE,
include_arrows = TRUE,
cen_labels = NULL,
plot_title = "",
data_loc_name = "",
...){
if(!add){
#xlimits
pt_lim = min(exp_dat[,pT_col])
if(str_detect(plot_title,'AuAu200')){
pt_lim = c(pt_lim,20)
txt_x = 11.7
}else if(str_detect(plot_title,'PbPb2760')){
pt_lim = c(pt_lim,90)
#txt_x = 45 #for MATTER (?why)
txt_x = 27
}else if(str_detect(plot_title,'PbPb5020')){
pt_lim = c(pt_lim,100)
#txt_x = 45 #for MATTER (?why)
txt_x = 30
}else{
print('Warning: Using auto-generated tick marks for pT')
pt_lim = c(pt_lim,max(exp_dat[,pT_col]))
}
plot(exp_dat[,pT_col],pred_dat[,1],
cex = 0.5,
col = col_alpha(col_str,alpha_val),
type = 'l',
# ylim = c(min(exp_dat[,exp_col] - 2*exp_dat[,err_col_sys],
# exp_dat[,exp_col] - 2*exp_dat[,err_col_stat]),
# max(exp_dat[,exp_col] + 2*exp_dat[,err_col_sys],
# exp_dat[,exp_col] + 2*exp_dat[,err_col_stat])),
ylim = c(0,1.8),
xlim = pt_lim,
xlab = expression(p[T]~"(GeV)"),
ylab = expression(R[AA]),
cex.lab = 2,
mgp = c(2.3,1,0),
pch = 15,
yaxt = 'n',
xaxt = 'n',
...)
title(main = plot_title, line = -1.5, cex.main = 1.3, adj = .97)
box(lwd = 3)
#Add data location labels
text(txt_x, 1.47, paste0('Data from ',data_loc_name),cex = 1, font = 2)
#y axis ticks and labels
axis(side = 2, at = seq(0,1.5,by = 0.3), las = 1, lwd.ticks = 3, labels = NA)
axis(side = 2, at = seq(0,1.5,by = 0.3), las = 1,
cex.axis = 1.2, lwd = 0, line = -0.4)
#x axis ticks and labels
if(str_detect(plot_title,'AuAu')){
pt_ticks = seq(10,20,by = 2)
}else if(str_detect(plot_title,'PbPb')){
pt_ticks = seq(10,100, by = 10)
}else{
print('Warning: Using auto-generated tick marks for pT')
pt_ticks = seq(min(exp_dat[,pT_col]),max(exp_dat[,pT_col]), length.out = 5)
}
axis(side = 1, at = pt_ticks, lwd.ticks = 3, labels = NA)
axis(side = 1, at = pt_ticks, cex.axis = 1.2, lwd = 0, line = -0.4)
}else{
lines(exp_dat[,pT_col],pred_dat[,1],
cex = 0.5,
col = col_alpha(col_str,alpha_val),type = 'l')
}
#x_range = max(exp_dat[,pT_col]) - min(exp_dat[,pT_col])
#arrow_offset = x_range/130
arrow_offset = 0
for(i in 2:dim(pred_dat)[2]){
lines(exp_dat[,pT_col],pred_dat[,i] + delta,cex = 0.5,
col = col_alpha(col_str,alpha_val))
}
if(include_med){
lines(exp_dat[,pT_col], med_pred_vec,lty = 2, lwd = 2)
}
##############
### DEPRECIATED: NOW WE PUT THE POINTS AND ARROWS IN AFTER ALL LINES
###############
# if(!add){
# exp_pch = 15
# }else{
# exp_pch = 17
# }
#
# ## Add experimental points
# points(exp_dat[,pT_col],exp_dat[,exp_col],
# pch = exp_pch, col = col_pts)
#
# #ylim = c(0,1))
# if(include_arrows){
# arrow_stat_col = col_pts
# arrow_sys_col = col_pts
#
# #Statistical errors
# arrows(exp_dat[,pT_col]-arrow_offset, exp_dat[,exp_col] - exp_dat[,err_col_stat],
# exp_dat[,pT_col]-arrow_offset, exp_dat[,exp_col] + exp_dat[,err_col_stat],
# length=0.05, angle=90, code=3,col = arrow_stat_col)
#
# #Systematic errors
# arrows(exp_dat[,pT_col]+arrow_offset, exp_dat[,exp_col] - exp_dat[,err_col_sys],
# exp_dat[,pT_col]+arrow_offset, exp_dat[,exp_col] + exp_dat[,err_col_sys],
# length=0.05, angle=90, code=3, col = arrow_sys_col)
# }
if(include_legend){
legend_cex = 1.1
if(include_med){
legend(legend_loc, c(paste(cen_labels,'Centrality'),'Median Predictions'),
lty = c(1,1,2),lwd = c(2,2,2),col = c(all_col_strs,'black'),
box.lwd = 2, cex = legend_cex)
#Put the point shapes on top
legend(legend_loc, c(rep("",length(cen_labels)),""), col = all_col_pts,
pch = c(15,17,NA), bty='n', cex = legend_cex)
}else{
legend(legend_loc, paste(cen_labels,'Centrality'),lwd = 2, col = all_col_strs,
box.lwd = 2, cex = legend_cex)
#Put the point shapes on top
legend(legend_loc, rep("",length(cen_labels)), col = all_col_pts,
pch = c(15, 17), bty='n', box.lwd = 2, cex = legend_cex)
}
}
}
plot_draws_together <- function(draws,
train_mod,
rot_mat,
original_dset_list,
q_index = NA,
condition_on_q = FALSE,
dset_labels = NULL,
data_orig_labels = NULL,
cen_groups = NULL,
cen_labels = NULL,
RAA_med_vals = NULL,
med_pred_type = 'RAA',
col_str_vec = c('red','blue'),
col_pts_vec = c('darkred','darkblue'),
include_med = FALSE,
scale_mean_vec = NULL, #sd_vec
scale_sd_vec = NULL, #y_means
delta_list = NULL,
num_samples = 1E3,
title_end = "",
alpha_val = 0.01,
save_pics = FALSE,
save_path = "",
save_end = "",
include_legend = TRUE,
include_arrows = TRUE,
legend_spots = replicate(J,'top_left')){
if(is.null(scale_sd_vec)|is.null(scale_mean_vec)){
stop('Must set values for scale_sd_vec and scale_mean_vec. Try sd_vec and y_means')
}else if(dim(draws)[1]<num_samples){
stop('Num samples bigger than number of draws')
}else if(sum(do.call(c,lapply(cen_groups,length)) - length(col_str_vec))){
print('Centrality Groups:')
print(cen_group)
print('Color vector:')
print(col_str_vec)
stop("Color vec and centrality group lengths not all the same")
}
Y_pred = exp_predictions(theta = draws,
model_list = train_mod,
y_sd = scale_sd_vec,
y_means = scale_mean_vec,
Y_svd = comp_mod$Y_svd,
cov_extra = comp_mod$cov_extra,
q = length(train_mod))$mean %>% t()
Y_pred <- Y_pred[sample(1:dim(Y_pred)[1],num_samples),]
if(include_med){
if(med_pred_type=='emulator'){
if(condition_on_q){
q_mode <- density(draws[,q_index])$x[which.max(density(draws[,q_index])$y)]
q_width = 0.3
good_q <- which(draws[,q_index] > (q_mode - q_width) &
draws[,q_index] < (q_mode + q_width) )
draws <- draws[good_q, ]
}
(param_meds <- apply(draws,2,median) %>% t())
(med_pred <- exp_predictions(theta = param_meds,
model_list = train_mod,
y_sd = scale_sd_vec,
y_means = scale_mean_vec,
Y_svd = comp_mod$Y_svd,
cov_extra = comp_mod$cov_extra,
q = length(train_mod))$mean %>% t())
}else if(med_pred_type=='RAA'){
(med_pred = RAA_med_vals)
}else{
stop('med_pred_type must be one of "emulator", "RAA"')
}
print(paste0('Median prediction values are ',med_pred_type))
}else{
med_pred <- matrix(0,1,length(scale_mean_vec))
}
cur_col <- 1
for(j in 1:length(cen_groups)){
(cur_cen_labels = cen_labels[[j]])
(cur_group = names(cen_groups)[j])
(K_j <- length(cen_groups[[j]]))
(cur_data_loc_name = data_orig_labels[j])
exp_dset_list_j = vector('list',K_j)
for(k in 1:K_j){
(dset_index = cen_groups[[j]][k])
(cur_color <- col_str_vec[k])
(cur_pts_col <- col_pts_vec[k])
(num_pT <- dim(original_dset_list[[dset_index]])[1])
cur_dset <- t(Y_pred[,cur_col:(cur_col + num_pT - 1)])
if(med_pred_type=='emulator'){
(cur_med_pred <- med_pred[,cur_col:(cur_col + num_pT - 1)])
}else{
(cur_med_pred <- RAA_med_vals[[j]][[k]])
}
exp_dset <- cbind('pT' = original_dset_list[[dset_index]]$pT,
'RAA_exp' = original_dset_list[[dset_index]]$RAA_exp,
'exp_err_stat' = original_dset_list[[dset_index]]$Stat_err,
'exp_err_sys' = original_dset_list[[dset_index]]$Sys_err)
exp_dset_list_j[[k]] <- exp_dset
(cur_col = cur_col + num_pT)
#Plot 'em
if(k==1){
if(save_pics) pdf(paste0(save_path,dset_labels[j],save_end,'.pdf'))
plot_lines(pred_dat = cur_dset,
exp_dat = exp_dset,
med_pred_vec = cur_med_pred,
delta = delta_list[[dset_index]],
alpha_val = alpha_val,
include_legend = include_legend,
legend_loc = legend_spots[dset_index],
include_med = include_med,
col_str = cur_color,
all_col_strs = col_str_vec,
col_pts = cur_pts_col,
all_col_pts = col_pts_vec,
cen_labels = cur_cen_labels,
include_arrows = include_arrows,
plot_title = paste(cur_group,title_end),
data_loc_name = cur_data_loc_name
)
}else{
plot_lines(pred_dat = cur_dset,
exp_dat = exp_dset,
med_pred_vec = cur_med_pred,
delta = delta_list[[dset_index]],
alpha_val = alpha_val,
include_legend = include_legend,
legend_loc = legend_spots[dset_index],
include_med = include_med,
col_str = cur_color,
all_col_strs = col_str_vec,
col_pts = cur_pts_col,
all_col_pts = col_pts_vec,
cen_labels = cur_cen_labels,
include_arrows = include_arrows,
add = TRUE)
}
if(k==K_j){
#########
## Plot all the points on top of the lines
#########
pT_col = 'pT'
exp_col = 'RAA_exp'
err_col_stat = 'exp_err_stat'
err_col_sys = 'exp_err_sys'
#Loop over the datasets in this collision system
for(i in 1:K_j){
(col_pts <- col_pts_vec[i])
exp_dat = exp_dset_list_j[[i]]
if(i < K_j){
exp_pch = 15
}else{
exp_pch = 17
}
## Add experimental points
points(exp_dat[,pT_col],exp_dat[,exp_col],
pch = exp_pch, col = col_pts)
#ylim = c(0,1))
if(include_arrows){
arrow_stat_col = col_pts
arrow_sys_col = col_pts
#x_range = max(exp_dat[,pT_col]) - min(exp_dat[,pT_col])
#arrow_offset = x_range/130
arrow_offset = 0
#Statistical errors
arrows(exp_dat[,pT_col]-arrow_offset, exp_dat[,exp_col] - exp_dat[,err_col_stat],
exp_dat[,pT_col]-arrow_offset, exp_dat[,exp_col] + exp_dat[,err_col_stat],
length=0.05, angle=90, code=3,col = arrow_stat_col)
#Systematic errors
arrows(exp_dat[,pT_col]+arrow_offset, exp_dat[,exp_col] - exp_dat[,err_col_sys],
exp_dat[,pT_col]+arrow_offset, exp_dat[,exp_col] + exp_dat[,err_col_sys],
length=0.05, angle=90, code=3, col = arrow_sys_col)
}
}
if(save_pics) dev.off()
}
}#k loop
}#j loop
}
#Predict PCA vals
###DEPRECIATED######
plot_draws <- function(draws,
train_mod,
rot_mat,
original_dset_list,
include_med = FALSE,
dset_labels = NULL,
scale_mean_vec = NULL, #sd_vec
scale_sd_vec = NULL, #y_means
delta_list = NULL,
num_samples = 1E3,
title_end = "",
alpha_val = 0.01,
save_pics = FALSE,
save_path = "",
save_end = "",
include_legend = TRUE,
include_arrows = TRUE,
legend_spots = replicate(J,'top_left')){
if(is.null(scale_sd_vec)|is.null(scale_mean_vec)){
stop('Must set values for scale_sd_vec and scale_mean_vec. Try sd_vec and y_means')
}else if(dim(draws)[1]<num_samples){
stop('Num samples bigger than number of draws')
}
Y_pred = exp_predictions(theta = draws,
model_list = train_mod,
y_sd = scale_sd_vec,
y_means = scale_mean_vec,
Y_svd = comp_mod$Y_svd,
cov_extra = comp_mod$cov_extra,
q = length(train_mod))$mean %>% t() #%>%
#dplyr::slice(sample(1:dim(Y_pred)[1],num_samples))
# ##Predict the mu_Z, collect them
# colnames(draws) <- colnames(ranges)
# post_pred_mod <-lapply(train_mod, RobustGaSP::predict,
# # testing_trend = as.data.frame(cbind(rep(1,dim(draws)[1]),draws)),
# testing_input = as.data.frame(draws))
#
# pred_means <- lapply(post_pred_mod,function(x)x$mean) %>%
# do.call(cbind,.)
#
#
# #Rotate
# Y_pred_scaled <- pred_means %*%t(rot_mat)
#
# #Scale
#
# Y_pred <- sweep(Y_pred_scaled,2,scale_sd_vec,FUN = "*") %>%
# sweep(2,scale_mean_vec,FUN = '+')
# #Y_pred <- sweep(Y_pred_scaled,2,apply(scale_mat,2,mean),FUN = '+')
#
Y_pred <- Y_pred[sample(1:dim(Y_pred)[1],num_samples),]
if(include_med){
(param_meds <- apply(draws,2,median) %>% t())
(med_pred <- exp_predictions(theta = param_meds,
model_list = train_mod,
y_sd = scale_sd_vec,
y_means = scale_mean_vec,
Y_svd = comp_mod$Y_svd,
cov_extra = comp_mod$cov_extra,
q = length(train_mod))$mean %>% t())
}else{
med_pred <- matrix(0,1,length(scale_mean_vec))
}
cur_col <- 1
for(j in 1:length(original_dset_list)){
(num_pT <- dim(original_dset_list[[j]])[1])
cur_dset <- t(Y_pred[,cur_col:(cur_col + num_pT - 1)])
cur_med_pred <- med_pred[,cur_col:(cur_col + num_pT - 1)]
exp_dset <- cbind('pT' = original_dset_list[[j]]$pT,
'RAA_exp' = original_dset_list[[j]]$RAA_exp,
'exp_err_stat' = original_dset_list[[j]]$Stat_err,
'exp_err_sys' = original_dset_list[[j]]$Sys_err)
(cur_col = cur_col + num_pT)
#Plot 'em
if(save_pics) pdf(paste0(save_path,dset_labels[j],save_end,'.pdf'))
plot_lines(pred_dat = cur_dset,
exp_dat = exp_dset,
med_pred_vec = cur_med_pred,
delta = delta_list[[j]],
main = paste(dset_labels[j],title_end),
alpha_val = alpha_val,
include_legend = include_legend,
legend_loc = legend_spots[j],
include_med = include_med)
if(save_pics) dev.off()
}
}
plot_discrep_fun <- function(discrep_list,
ell_mat,
lambda_mat,
pT_train,
pT_pred,
alpha = 2,
include_pts = TRUE,
dset_labels = dset_strings,
save_pics = FALSE,
save_path = "",
save_suffix = ""){
mu_delt <- sig_delt <- vector('list',length(discrep_list))
for(j in 1:length(discrep_list)){
y = discrep_list[[j]]
x_train = matrix(pT_train[[j]])
x_pred = matrix(sort(c(pT_train[[j]],pT_pred[[j]])))
ell_j = mean(ell_mat[,j])
lambda_j = mean(lambda_mat[,j])
sig_22 = cov_mat_multi(x_train,
x_train,
ell_vec = ell_j,
lambda = lambda_j,
alpha_vec = alpha,
nugget = 1E-6)
sig_22_inv <- chol2inv(chol(sig_22))
sig_12 = cov_mat_multi(x_pred,
x_train,
ell_vec = ell_j,
lambda = lambda_j,
alpha_vec = alpha,
nugget = 1E-6)
sig_21 = t(sig_12)
sig_11 = cov_mat_multi(x_pred,
x_pred,
ell_vec = ell_j,
lambda = lambda_j,
alpha_vec = alpha,
nugget = 1E-6)
mu_delt[[j]] <- sig_12%*%sig_22_inv%*%y
sig_delt[[j]] <- diag(sig_11 - sig_12%*%sig_22_inv%*%sig_21)
if(save_pics)pdf(paste0(save_path,"discrep_",dset_labels[j],save_suffix,".pdf"))
plot(x_pred,mu_delt[[j]],type = 'l',
# ylim = c(min(mu_delt[[j]] - 2*sqrt(sig_delt[[j]])),
# max(mu_delt[[j]] + 2*sqrt(sig_delt[[j]]))))
ylim = c(-.23,.23),
xlab = expression(p[T]~"(GeV)"),
#ylab = expression(delta[j]),
main = paste0("Discrepancy for ",dset_labels[j]),
cex.lab = 2,
cex.main = 2,
ylab = "")
title(ylab = expression(delta[j]),cex.lab = 2,mgp=c(2,1,0))
lines(x_pred,mu_delt[[j]] - 2*sqrt(sig_delt[[j]]),col = 'blue',
lty = 2)
lines(x_pred,mu_delt[[j]] + 2*sqrt(sig_delt[[j]]),col = 'blue',
lty = 2)
abline(h=0)
if(include_pts){
points(x_train,y)
}
if(save_pics) dev.off()
}
invisible(list(mu_delt = mu_delt,sig_delt = sig_delt))
}
###########
##Validation Plot
##########
##Requires:
#Holdout point
#Design
#Train dset
# V
# y_sd
# y_mean
##Ugh...should change this to use the new prediction function
predict_holdout <- function(holdout,
design = comp_mod$all_data$scaled_d,
Y_scaled = comp_mod$Y_final,
#Y_orig = Y,
y_sd_cur = comp_mod$y_sd,
y_mean_cur = comp_mod$y_means,
q = length(comp_mod$train_mods), plotit = FALSE,
verbose = FALSE,
cover_level = 95){
train_d <- design[-holdout, ]
test_d <- design[holdout, ]
train_Y <- Y_scaled[-holdout, ]
Y_orig <- sweep(Y_scaled,2,y_sd_cur,"*")%>%
sweep(2,y_mean_cur, "+")
test_Y <- Y_orig[holdout, ]
V_train <- svd(train_Y)$v[ , 1:q]
train_Z = as.matrix(train_Y)%*%V_train
sink("NUL")
train_mod <- lapply(as.data.frame(train_Z),rgasp,design = train_d,
kernel_type = 'pow_exp')
sink()
test_mod <- lapply(train_mod,RobustGaSP::predict,testing_input = test_d)
pred_Z <- lapply(test_mod,function(x)x$mean) %>%
do.call(c,.) %>%
t()
pred_err_Z <- lapply(test_mod,function(x)x$sd^2) %>%
do.call(c,.)
pred_Y <- pred_Z %*% t(V_train) %*% diag(y_sd_cur) + y_mean_cur
pred_err_Y <- diag(y_sd_cur) %*% V_train %*% diag(pred_err_Z) %*% t(V_train) %*% diag(y_sd_cur) %>%
diag()
if(cover_level == 95){
sd_mult = 2
}else if(cover_level == 60){
sd_mult = 1
}else{
stop('Must pick 95% or 60% for cover level')
}
if(plotit){
plot(as.numeric(test_Y),as.numeric(pred_Y),pch = 19,cex = .5,
xlab = 'Holdout Values',
ylab = 'Predicted Values',
main = paste0('Emulator Prediction, Holdout ',holdout),
cex.lab = 2,
cex.axis = 1.3,
cex.main = 1.4,
mgp = c(2.4,1,0),
ylim = c(min(as.numeric(pred_Y) - sd_mult*sqrt(pred_err_Y)),
max(as.numeric(pred_Y) + sd_mult*sqrt(pred_err_Y))))
arrows(as.numeric(test_Y), as.numeric(pred_Y) - sd_mult*sqrt(pred_err_Y),
as.numeric(test_Y), as.numeric(pred_Y) + sd_mult*sqrt(pred_err_Y),
length=0, angle=90, code=3)
abline(a = 0,b = 1)
}
good_pred_95 <- test_Y < as.numeric(pred_Y) + 2*sqrt(pred_err_Y) &
test_Y > as.numeric(pred_Y) - 2*sqrt(pred_err_Y)
good_pred_60 <- test_Y < as.numeric(pred_Y) + sqrt(pred_err_Y) &
test_Y > as.numeric(pred_Y) - sqrt(pred_err_Y)
return(list(mean_cover_95 = mean(good_pred_95), mean_cover_60 = mean(good_pred_60),
mean_width = mean(4*sqrt(pred_err_Y))))
}
collect_all_holdouts <- function(D_c_theta = comp_mod$all_data$scaled_d,
Y_scaled = comp_mod$Y_final,
#Y_orig = Y,
y_sd_cur = comp_mod$y_sd,
y_mean_cur = comp_mod$y_means,
q = length(comp_mod$train_mods)){
n_holdouts <- nrow(D_c_theta)
cover_95 <- cover_60 <- widths <- numeric(n_holdouts)
for(h in 1:n_holdouts){
invisible(pred_hold_h <- predict_holdout(holdout = h,
design = D_c_theta,
Y_scaled = Y_scaled,
y_sd_cur = y_sd_cur,
y_mean_cur = y_mean_cur,
q = q))
cover_95[h] <- pred_hold_h$mean_cover_95
cover_60[h] <- pred_hold_h$mean_cover_60
widths[h] <- pred_hold_h$mean_width
flush.console()
cat("\r FINISHED HOLDOUT ", h ,'/',n_holdouts)
}
return(list(cover_95 = cover_95, cover_60 = cover_60, widths = widths))
}
|
/outdated_scripts/assess_concat_analysis.R
|
no_license
|
jake-coleman32/JETSCAPE-STAT-Rpkg
|
R
| false | false | 35,900 |
r
|
library(MASS)
library(cluster)
library(emdbook)
library(dplyr)
library(reshape2)
library(RobustGaSP)
library(mvtnorm)
library(Matrix)
library(stringr)
library(GGally)
col_alpha <- function(col_str, alpha){
if(alpha<0|alpha>1){
stop('Alpha must be within (0,1)')
}
rgb_mat <- col2rgb(col_str,alpha = TRUE)
return(rgb(rgb_mat[1],rgb_mat[2],rgb_mat[3],alpha = 255*alpha, maxColorValue = 255))
}
# base_save_path = "/Users/Jake/Box Sync/Research/Computer_Emulation/Shanshan_Project/Results/"
# save_folder = ""
#
# save_path = paste0(base_save_path,save_folder)
##########################################################################
####These are plots to assess the sampler from jetscape_2_concatenate.R
##########################################################################
#Test for Normality
#None are <0.05 even without accounting for multiple testing
normality_test <- function(Y_final){
for(j in 1:dim(Y_final)[2]){
print(shapiro.test(Y_final[,j])$p.value)
}
}
#How many PCs?
calc_Vq <- function(Y_svd,save_pics = FALSE, plotit = FALSE){
eigs <- Y_svd$d^2
(V_q <- cumsum(eigs)/sum(eigs))
if(plotit){
if(save_pics) pdf(paste0(save_path,'var_explained.pdf'))
plot(V_q[1:6],type = 'o',
xlab = "Total Number of PCs R",
main = "Fraction of Variance Explained",
ylab = expression(F[R]),
cex.lab = 2,
cex.axis = 1.3,
cex.main = 1.4,
mgp = c(2.3,1,0),
pch = 19)
if(save_pics) dev.off()
}
return(V_q)
}
###########
##Results
#########
plot_heatmaps <- function(param_plot,ranges,
param_names = colnames(param_plot),
pairs = NULL,
save_param_names = NULL,
save_pics = FALSE,save_path = "", save_end = "", title = 'Posterior Heatmap',
plt_points = FALSE,design = NULL, ...){
if(save_pics & save_path==""){
print('Warning: saving plots to working directory')
}
#If you don't specify pairs, just do all of them
if(is.null(pairs)){
num_params = dim(param_plot)[2]
num_pairs = sum(1:(num_params-1))
i = 0
pairs = vector('list',num_pairs)
for(k in 1:(num_params-1)){
for(l in k:num_params)
i = i + 1
pairs[[i]] <- c(k,l)
}
}
num_pairs = length(pairs)
if(is.null(save_param_names)){
save_param_names <- vector('list',num_pairs)
}
for(i in 1:num_pairs){
(k = pairs[[i]][1])
(l = pairs[[i]][2])
f1 <- kde2d(param_plot[,k], (param_plot[,l]), n = 100,
lims = c(ranges[[k]],ranges[[l]]))
if(is.null(save_param_names[[i]])){
save_param_names[[i]] <- paste0(param_names[k],'_',param_names[l])
print(save_param_names[[i]])
}
if(save_pics) pdf(paste0(save_path,'heatmap_',save_param_names[[i]],save_end,'.pdf'))
par(mar=c(5.1, 5.1, 4.1, 2.1))
image(f1,
xlab = param_names[k],
ylab = "",
main = title,
cex.lab = 2,
cex.axis = 1.3,
cex.main = 1.4,
mgp = c(3,1,0), ...#,
# xlim = ranges[[1]],
# ylim = ranges[[2]]
)
title(ylab = param_names[l],
mgp = c(2.1,1,0),
cex.lab = 2,
cex.axis = 1.3)
perc_lvl = c(.6,.75,.9)
HPDregionplot(param_plot[,c(k,l)], prob = perc_lvl,
col=c("black"), lty = c(1,5,3), add=TRUE)
if(plt_points){
points(design[,k],design[,l],pch = 19, cex = 0.7)
}
# legend('topright',paste0(perc_lvl*100,"%"),title = "Highest Density Kernel Estimate",
# lty = c(1,5,3),
# bty="n")
if(save_pics) dev.off()
}
}
make_param_plot <- function(res_params, ranges){
param_plot <- matrix(0,dim(res_params)[1],dim(res_params)[2])
for(j in 1:dim(param_plot)[2]){
param_plot[,j] <- res_params[,j]*(ranges[[j]][2] - ranges[[j]][1]) + ranges[[j]][1]
}
colnames(param_plot) <- names(ranges)
return(param_plot)
}
unmake_param_plot <- function(param_plot, ranges){
res_params <- matrix(0,dim(param_plot)[1], dim(param_plot)[2])
for(j in 1:dim(res_params)[2]){
res_params[,j] <- (param_plot[,j] - ranges[[j]][1])/(ranges[[j]][2] - ranges[[j]][1])
}
colnames(res_params) <- names(ranges)
return(res_params)
}
transform_draws <- function(param_plot,cols_to_transform = c(1,2),
new_names = c('A','C')){
X = param_plot[,cols_to_transform[1]]
Y = param_plot[,cols_to_transform[2]]
new_param_plot = param_plot
new_param_plot[,1] = X*Y
new_param_plot[,2] = X*(1-Y)
colnames(new_param_plot)[cols_to_transform] = new_names
return(new_param_plot)
}
transform_ranges <- function(ranges,vars_to_transform = c(1,2),
new_names = c('A','C')){
ranges_t <- ranges
ranges_t[[1]] <- c(0,ranges[[1]][2])
ranges_t[[2]] <- c(0, ranges[[1]][2])
names(ranges_t)[1:2] <- new_names
return(ranges_t)
}
heat_pairplot <- function(param_plot,
ranges,
split_num = dim(param_plot)[1],
param_names = colnames(param_plot),
title = 'Calibration Pairplot',save_pics = FALSE,
save_path = "",
save_end = "",
include_legend = FALSE,
legend_names = NULL,
legend_cols = NULL,
off_diag = "heatmap"){
panel.hist <- function(x,col_hist,split_num, ...)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x[1:split_num], plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y,xlim = c(0,0.35),col = rgb(1,0,0,.1), ...)
if(split_num<length(x)){
h <- hist(x[(split_num+1):length(x)], plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y,xlim = c(0,0.35),col = rgb(0,0,1,.1), ...)
}
}
panel.dens <- function(x,split_num,...){
usr <- par("usr"); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
plot(density(x[1:split_num],))
h <- hist(x[1:split_num], plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y,xlim = c(0,0.35),col = rgb(1,0,0,.1), ...)
if(split_num<length(x)){
h <- hist(x[(split_num+1):length(x)], plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y,xlim = c(0,0.35),col = rgb(0,0,1,.1), ...)
}
}
panel.scatter <- function(x,y,split_num,...){
first_pts = 1:split_num
points(x[first_pts],y[first_pts],#pch = 19,
col = rgb(1,0,0,.01),cex=.7,
...)
#If you actually want to split
if(split_num<length(x)){
second_pts <- (split_num+1):length(x)
points(x[second_pts],y[second_pts],#pch =19,
col = rgb(0,0,1,.01), cex = 0.7,
...)
}
}
panel.image <- function(x,y,split_num,...){
f1 <- kde2d(x, y, n = 100)
image(f1,add = TRUE)
}
if(off_diag=="scatter"){
panel_off = panel.scatter
}else if(off_diag=="heatmap"){
if(split_num<dim(param_plot)[1]){
stop('Heatmap only appropriate for one set of calibration results')
}
panel_off = panel.image
}else{stop("Whoops off_diag parameter is wrong")}
if(save_pics){ pdf(paste0(save_path,'calibration_pairplot',save_end,".pdf"))}
pairs(param_plot,
panel = panel_off,
diag.panel = panel.hist,
#pch = 19,
#cex = .3,
# col = rgb(1,0,0,.1),
labels = param_names,
upper.panel = NULL,
cex.lab = 1.5,
cex.axis = 1.3,
cex.main = 1.4,
mgp = c(2.3,1,0),
#col_hist = 'green',
las = 2,
main = title,
split_num = split_num)
if(include_legend){
legend('topright',legend_names,col = legend_cols,
pch = 19,inset = .13)
}
if(save_pics) dev.off()
}
gg_heat_pairs <- function(all_data,
ranges,
cols_to_use = 1:4,
scatter_alpha = 0.1,
col_names = c('A','B','C','D')){
both_col <- 'red3'
lhc_col <- 'deepskyblue'
rhic_col <- 'forestgreen'
my_dens <- function(data, mapping, ...) {
ggplot(data = data, mapping=mapping) +
geom_density(..., alpha = 0.7, size = 1.1) +
scale_fill_manual(values=c( both_col, lhc_col, rhic_col )) +
scale_color_manual(values=c( both_col,lhc_col, rhic_col))
}
my_scatter <- function(data,mapping,...){
ggplot(data = data, mapping = mapping) +
geom_jitter(alpha = scatter_alpha,size = .3) +
guides(col = guide_legend(override.aes = list(shape = 15,
size = 8,
alpha = 1),
label.theme = element_text(size = 12))) +
scale_fill_manual(values=c( both_col, lhc_col, rhic_col )) +
scale_color_manual(values=c( both_col,lhc_col, rhic_col)) +
theme(legend.title=element_blank())
}
p <- ggpairs(all_data, mapping = aes(color = Collider), columns = cols_to_use,
lower = list(continuous = my_scatter),
diag = list(continuous = my_dens),
upper = list(continuous = 'blank'),
columnLabels = col_names,
labeller = 'label_parsed'
#,legend = c(2,1)
)
for(i in 1:length(cols_to_use)){
for(j in 1:length(cols_to_use)){
if(j<=i){
p[i,j] <- p[i,j] + xlim(ranges[[j]][1],ranges[[j]][2]) +
theme(axis.text = element_text(size = 11.5))
}
}
}
#p <- p + theme(strip.text = element_text(size = 15))
#Remove yaxis on first plot, to remove confusion
#p[1,1] <- p[1,1] + theme(axis.text.y = element_blank(),
# axis.ticks = element_blank())
points_legend <- gglegend(my_scatter)
p[1,p$ncol] <- points_legend(all_data, ggplot2::aes(all_data[1,cols_to_use[1]],
all_data[1,cols_to_use[2]],
color = Collider))
#p[1,p$ncol-1] <- points_legend(all_data, ggplot2::aes(all_data[1,cols_to_use[1]],
# all_data[1,cols_to_use[2]],
# color = Collider))
#print(p + theme(strip.text = element_text(size = 15)))
print(p)
}
make_combined_pairplot <- function(save_lists,
labels,
ranges,
save_pics = FALSE,
save_path = '',
save_end = '',
cols_to_use = 1:5,
scatter_alpha = 0.1,
num_samples = 1E4,
str_param_to_use = 'transformed_params',
col_names = c('A','B','C','D'),
make_pdf = FALSE){
load(save_lists[1])
data_to_plot <- matrix(,0,ncol(save_list[[str_param_to_use]]))
if(length(save_lists)!=length(labels)){
stop('lists and labels not same length')
}
for(i in 1:length(save_lists)){
load(save_lists[i])
cur_data <- as.data.frame(save_list[[str_param_to_use]]) %>%
dplyr::sample_n(.,num_samples)
cur_data$Collider <- labels[i]
data_to_plot <- rbind(data_to_plot, cur_data)
}
if(save_pics){
if(make_pdf){
pdf(paste0(save_path,'combined_pairplot',save_end,'.pdf'))
}else{
png(paste0(save_path,'combined_pairplot',save_end,'.png'))
}
}
gg_heat_pairs(data_to_plot,
ranges = ranges,
cols_to_use = cols_to_use,
col_names = col_names,
scatter_alpha = scatter_alpha)
if(save_pics) dev.off()
}
plot_lines <- function(pred_dat,exp_dat,
med_pred_vec = NULL,
pT_col = 'pT',
exp_col = 'RAA_exp',
err_col_stat = 'exp_err_stat',
err_col_sys = 'exp_err_sys',
col_str = 'red',
col_pts = 'darkred',
all_col_pts = c('darkred','darkblue'),
all_col_strs = c('red','blue'),
alpha_val = 0.01,
delta = rep(0,dim(exp_dat)[1]),
include_legend = TRUE,
legend_loc = 'top_left',
include_med = FALSE,
add = FALSE,
include_arrows = TRUE,
cen_labels = NULL,
plot_title = "",
data_loc_name = "",
...){
if(!add){
#xlimits
pt_lim = min(exp_dat[,pT_col])
if(str_detect(plot_title,'AuAu200')){
pt_lim = c(pt_lim,20)
txt_x = 11.7
}else if(str_detect(plot_title,'PbPb2760')){
pt_lim = c(pt_lim,90)
#txt_x = 45 #for MATTER (?why)
txt_x = 27
}else if(str_detect(plot_title,'PbPb5020')){
pt_lim = c(pt_lim,100)
#txt_x = 45 #for MATTER (?why)
txt_x = 30
}else{
print('Warning: Using auto-generated tick marks for pT')
pt_lim = c(pt_lim,max(exp_dat[,pT_col]))
}
plot(exp_dat[,pT_col],pred_dat[,1],
cex = 0.5,
col = col_alpha(col_str,alpha_val),
type = 'l',
# ylim = c(min(exp_dat[,exp_col] - 2*exp_dat[,err_col_sys],
# exp_dat[,exp_col] - 2*exp_dat[,err_col_stat]),
# max(exp_dat[,exp_col] + 2*exp_dat[,err_col_sys],
# exp_dat[,exp_col] + 2*exp_dat[,err_col_stat])),
ylim = c(0,1.8),
xlim = pt_lim,
xlab = expression(p[T]~"(GeV)"),
ylab = expression(R[AA]),
cex.lab = 2,
mgp = c(2.3,1,0),
pch = 15,
yaxt = 'n',
xaxt = 'n',
...)
title(main = plot_title, line = -1.5, cex.main = 1.3, adj = .97)
box(lwd = 3)
#Add data location labels
text(txt_x, 1.47, paste0('Data from ',data_loc_name),cex = 1, font = 2)
#y axis ticks and labels
axis(side = 2, at = seq(0,1.5,by = 0.3), las = 1, lwd.ticks = 3, labels = NA)
axis(side = 2, at = seq(0,1.5,by = 0.3), las = 1,
cex.axis = 1.2, lwd = 0, line = -0.4)
#x axis ticks and labels
if(str_detect(plot_title,'AuAu')){
pt_ticks = seq(10,20,by = 2)
}else if(str_detect(plot_title,'PbPb')){
pt_ticks = seq(10,100, by = 10)
}else{
print('Warning: Using auto-generated tick marks for pT')
pt_ticks = seq(min(exp_dat[,pT_col]),max(exp_dat[,pT_col]), length.out = 5)
}
axis(side = 1, at = pt_ticks, lwd.ticks = 3, labels = NA)
axis(side = 1, at = pt_ticks, cex.axis = 1.2, lwd = 0, line = -0.4)
}else{
lines(exp_dat[,pT_col],pred_dat[,1],
cex = 0.5,
col = col_alpha(col_str,alpha_val),type = 'l')
}
#x_range = max(exp_dat[,pT_col]) - min(exp_dat[,pT_col])
#arrow_offset = x_range/130
arrow_offset = 0
for(i in 2:dim(pred_dat)[2]){
lines(exp_dat[,pT_col],pred_dat[,i] + delta,cex = 0.5,
col = col_alpha(col_str,alpha_val))
}
if(include_med){
lines(exp_dat[,pT_col], med_pred_vec,lty = 2, lwd = 2)
}
##############
### DEPRECIATED: NOW WE PUT THE POINTS AND ARROWS IN AFTER ALL LINES
###############
# if(!add){
# exp_pch = 15
# }else{
# exp_pch = 17
# }
#
# ## Add experimental points
# points(exp_dat[,pT_col],exp_dat[,exp_col],
# pch = exp_pch, col = col_pts)
#
# #ylim = c(0,1))
# if(include_arrows){
# arrow_stat_col = col_pts
# arrow_sys_col = col_pts
#
# #Statistical errors
# arrows(exp_dat[,pT_col]-arrow_offset, exp_dat[,exp_col] - exp_dat[,err_col_stat],
# exp_dat[,pT_col]-arrow_offset, exp_dat[,exp_col] + exp_dat[,err_col_stat],
# length=0.05, angle=90, code=3,col = arrow_stat_col)
#
# #Systematic errors
# arrows(exp_dat[,pT_col]+arrow_offset, exp_dat[,exp_col] - exp_dat[,err_col_sys],
# exp_dat[,pT_col]+arrow_offset, exp_dat[,exp_col] + exp_dat[,err_col_sys],
# length=0.05, angle=90, code=3, col = arrow_sys_col)
# }
if(include_legend){
legend_cex = 1.1
if(include_med){
legend(legend_loc, c(paste(cen_labels,'Centrality'),'Median Predictions'),
lty = c(1,1,2),lwd = c(2,2,2),col = c(all_col_strs,'black'),
box.lwd = 2, cex = legend_cex)
#Put the point shapes on top
legend(legend_loc, c(rep("",length(cen_labels)),""), col = all_col_pts,
pch = c(15,17,NA), bty='n', cex = legend_cex)
}else{
legend(legend_loc, paste(cen_labels,'Centrality'),lwd = 2, col = all_col_strs,
box.lwd = 2, cex = legend_cex)
#Put the point shapes on top
legend(legend_loc, rep("",length(cen_labels)), col = all_col_pts,
pch = c(15, 17), bty='n', box.lwd = 2, cex = legend_cex)
}
}
}
plot_draws_together <- function(draws,
train_mod,
rot_mat,
original_dset_list,
q_index = NA,
condition_on_q = FALSE,
dset_labels = NULL,
data_orig_labels = NULL,
cen_groups = NULL,
cen_labels = NULL,
RAA_med_vals = NULL,
med_pred_type = 'RAA',
col_str_vec = c('red','blue'),
col_pts_vec = c('darkred','darkblue'),
include_med = FALSE,
scale_mean_vec = NULL, #sd_vec
scale_sd_vec = NULL, #y_means
delta_list = NULL,
num_samples = 1E3,
title_end = "",
alpha_val = 0.01,
save_pics = FALSE,
save_path = "",
save_end = "",
include_legend = TRUE,
include_arrows = TRUE,
legend_spots = replicate(J,'top_left')){
if(is.null(scale_sd_vec)|is.null(scale_mean_vec)){
stop('Must set values for scale_sd_vec and scale_mean_vec. Try sd_vec and y_means')
}else if(dim(draws)[1]<num_samples){
stop('Num samples bigger than number of draws')
}else if(sum(do.call(c,lapply(cen_groups,length)) - length(col_str_vec))){
print('Centrality Groups:')
print(cen_group)
print('Color vector:')
print(col_str_vec)
stop("Color vec and centrality group lengths not all the same")
}
Y_pred = exp_predictions(theta = draws,
model_list = train_mod,
y_sd = scale_sd_vec,
y_means = scale_mean_vec,
Y_svd = comp_mod$Y_svd,
cov_extra = comp_mod$cov_extra,
q = length(train_mod))$mean %>% t()
Y_pred <- Y_pred[sample(1:dim(Y_pred)[1],num_samples),]
if(include_med){
if(med_pred_type=='emulator'){
if(condition_on_q){
q_mode <- density(draws[,q_index])$x[which.max(density(draws[,q_index])$y)]
q_width = 0.3
good_q <- which(draws[,q_index] > (q_mode - q_width) &
draws[,q_index] < (q_mode + q_width) )
draws <- draws[good_q, ]
}
(param_meds <- apply(draws,2,median) %>% t())
(med_pred <- exp_predictions(theta = param_meds,
model_list = train_mod,
y_sd = scale_sd_vec,
y_means = scale_mean_vec,
Y_svd = comp_mod$Y_svd,
cov_extra = comp_mod$cov_extra,
q = length(train_mod))$mean %>% t())
}else if(med_pred_type=='RAA'){
(med_pred = RAA_med_vals)
}else{
stop('med_pred_type must be one of "emulator", "RAA"')
}
print(paste0('Median prediction values are ',med_pred_type))
}else{
med_pred <- matrix(0,1,length(scale_mean_vec))
}
cur_col <- 1
for(j in 1:length(cen_groups)){
(cur_cen_labels = cen_labels[[j]])
(cur_group = names(cen_groups)[j])
(K_j <- length(cen_groups[[j]]))
(cur_data_loc_name = data_orig_labels[j])
exp_dset_list_j = vector('list',K_j)
for(k in 1:K_j){
(dset_index = cen_groups[[j]][k])
(cur_color <- col_str_vec[k])
(cur_pts_col <- col_pts_vec[k])
(num_pT <- dim(original_dset_list[[dset_index]])[1])
cur_dset <- t(Y_pred[,cur_col:(cur_col + num_pT - 1)])
if(med_pred_type=='emulator'){
(cur_med_pred <- med_pred[,cur_col:(cur_col + num_pT - 1)])
}else{
(cur_med_pred <- RAA_med_vals[[j]][[k]])
}
exp_dset <- cbind('pT' = original_dset_list[[dset_index]]$pT,
'RAA_exp' = original_dset_list[[dset_index]]$RAA_exp,
'exp_err_stat' = original_dset_list[[dset_index]]$Stat_err,
'exp_err_sys' = original_dset_list[[dset_index]]$Sys_err)
exp_dset_list_j[[k]] <- exp_dset
(cur_col = cur_col + num_pT)
#Plot 'em
if(k==1){
if(save_pics) pdf(paste0(save_path,dset_labels[j],save_end,'.pdf'))
plot_lines(pred_dat = cur_dset,
exp_dat = exp_dset,
med_pred_vec = cur_med_pred,
delta = delta_list[[dset_index]],
alpha_val = alpha_val,
include_legend = include_legend,
legend_loc = legend_spots[dset_index],
include_med = include_med,
col_str = cur_color,
all_col_strs = col_str_vec,
col_pts = cur_pts_col,
all_col_pts = col_pts_vec,
cen_labels = cur_cen_labels,
include_arrows = include_arrows,
plot_title = paste(cur_group,title_end),
data_loc_name = cur_data_loc_name
)
}else{
plot_lines(pred_dat = cur_dset,
exp_dat = exp_dset,
med_pred_vec = cur_med_pred,
delta = delta_list[[dset_index]],
alpha_val = alpha_val,
include_legend = include_legend,
legend_loc = legend_spots[dset_index],
include_med = include_med,
col_str = cur_color,
all_col_strs = col_str_vec,
col_pts = cur_pts_col,
all_col_pts = col_pts_vec,
cen_labels = cur_cen_labels,
include_arrows = include_arrows,
add = TRUE)
}
if(k==K_j){
#########
## Plot all the points on top of the lines
#########
pT_col = 'pT'
exp_col = 'RAA_exp'
err_col_stat = 'exp_err_stat'
err_col_sys = 'exp_err_sys'
#Loop over the datasets in this collision system
for(i in 1:K_j){
(col_pts <- col_pts_vec[i])
exp_dat = exp_dset_list_j[[i]]
if(i < K_j){
exp_pch = 15
}else{
exp_pch = 17
}
## Add experimental points
points(exp_dat[,pT_col],exp_dat[,exp_col],
pch = exp_pch, col = col_pts)
#ylim = c(0,1))
if(include_arrows){
arrow_stat_col = col_pts
arrow_sys_col = col_pts
#x_range = max(exp_dat[,pT_col]) - min(exp_dat[,pT_col])
#arrow_offset = x_range/130
arrow_offset = 0
#Statistical errors
arrows(exp_dat[,pT_col]-arrow_offset, exp_dat[,exp_col] - exp_dat[,err_col_stat],
exp_dat[,pT_col]-arrow_offset, exp_dat[,exp_col] + exp_dat[,err_col_stat],
length=0.05, angle=90, code=3,col = arrow_stat_col)
#Systematic errors
arrows(exp_dat[,pT_col]+arrow_offset, exp_dat[,exp_col] - exp_dat[,err_col_sys],
exp_dat[,pT_col]+arrow_offset, exp_dat[,exp_col] + exp_dat[,err_col_sys],
length=0.05, angle=90, code=3, col = arrow_sys_col)
}
}
if(save_pics) dev.off()
}
}#k loop
}#j loop
}
#Predict PCA vals
###DEPRECIATED######
plot_draws <- function(draws,
train_mod,
rot_mat,
original_dset_list,
include_med = FALSE,
dset_labels = NULL,
scale_mean_vec = NULL, #sd_vec
scale_sd_vec = NULL, #y_means
delta_list = NULL,
num_samples = 1E3,
title_end = "",
alpha_val = 0.01,
save_pics = FALSE,
save_path = "",
save_end = "",
include_legend = TRUE,
include_arrows = TRUE,
legend_spots = replicate(J,'top_left')){
if(is.null(scale_sd_vec)|is.null(scale_mean_vec)){
stop('Must set values for scale_sd_vec and scale_mean_vec. Try sd_vec and y_means')
}else if(dim(draws)[1]<num_samples){
stop('Num samples bigger than number of draws')
}
Y_pred = exp_predictions(theta = draws,
model_list = train_mod,
y_sd = scale_sd_vec,
y_means = scale_mean_vec,
Y_svd = comp_mod$Y_svd,
cov_extra = comp_mod$cov_extra,
q = length(train_mod))$mean %>% t() #%>%
#dplyr::slice(sample(1:dim(Y_pred)[1],num_samples))
# ##Predict the mu_Z, collect them
# colnames(draws) <- colnames(ranges)
# post_pred_mod <-lapply(train_mod, RobustGaSP::predict,
# # testing_trend = as.data.frame(cbind(rep(1,dim(draws)[1]),draws)),
# testing_input = as.data.frame(draws))
#
# pred_means <- lapply(post_pred_mod,function(x)x$mean) %>%
# do.call(cbind,.)
#
#
# #Rotate
# Y_pred_scaled <- pred_means %*%t(rot_mat)
#
# #Scale
#
# Y_pred <- sweep(Y_pred_scaled,2,scale_sd_vec,FUN = "*") %>%
# sweep(2,scale_mean_vec,FUN = '+')
# #Y_pred <- sweep(Y_pred_scaled,2,apply(scale_mat,2,mean),FUN = '+')
#
Y_pred <- Y_pred[sample(1:dim(Y_pred)[1],num_samples),]
if(include_med){
(param_meds <- apply(draws,2,median) %>% t())
(med_pred <- exp_predictions(theta = param_meds,
model_list = train_mod,
y_sd = scale_sd_vec,
y_means = scale_mean_vec,
Y_svd = comp_mod$Y_svd,
cov_extra = comp_mod$cov_extra,
q = length(train_mod))$mean %>% t())
}else{
med_pred <- matrix(0,1,length(scale_mean_vec))
}
cur_col <- 1
for(j in 1:length(original_dset_list)){
(num_pT <- dim(original_dset_list[[j]])[1])
cur_dset <- t(Y_pred[,cur_col:(cur_col + num_pT - 1)])
cur_med_pred <- med_pred[,cur_col:(cur_col + num_pT - 1)]
exp_dset <- cbind('pT' = original_dset_list[[j]]$pT,
'RAA_exp' = original_dset_list[[j]]$RAA_exp,
'exp_err_stat' = original_dset_list[[j]]$Stat_err,
'exp_err_sys' = original_dset_list[[j]]$Sys_err)
(cur_col = cur_col + num_pT)
#Plot 'em
if(save_pics) pdf(paste0(save_path,dset_labels[j],save_end,'.pdf'))
plot_lines(pred_dat = cur_dset,
exp_dat = exp_dset,
med_pred_vec = cur_med_pred,
delta = delta_list[[j]],
main = paste(dset_labels[j],title_end),
alpha_val = alpha_val,
include_legend = include_legend,
legend_loc = legend_spots[j],
include_med = include_med)
if(save_pics) dev.off()
}
}
plot_discrep_fun <- function(discrep_list,
ell_mat,
lambda_mat,
pT_train,
pT_pred,
alpha = 2,
include_pts = TRUE,
dset_labels = dset_strings,
save_pics = FALSE,
save_path = "",
save_suffix = ""){
mu_delt <- sig_delt <- vector('list',length(discrep_list))
for(j in 1:length(discrep_list)){
y = discrep_list[[j]]
x_train = matrix(pT_train[[j]])
x_pred = matrix(sort(c(pT_train[[j]],pT_pred[[j]])))
ell_j = mean(ell_mat[,j])
lambda_j = mean(lambda_mat[,j])
sig_22 = cov_mat_multi(x_train,
x_train,
ell_vec = ell_j,
lambda = lambda_j,
alpha_vec = alpha,
nugget = 1E-6)
sig_22_inv <- chol2inv(chol(sig_22))
sig_12 = cov_mat_multi(x_pred,
x_train,
ell_vec = ell_j,
lambda = lambda_j,
alpha_vec = alpha,
nugget = 1E-6)
sig_21 = t(sig_12)
sig_11 = cov_mat_multi(x_pred,
x_pred,
ell_vec = ell_j,
lambda = lambda_j,
alpha_vec = alpha,
nugget = 1E-6)
mu_delt[[j]] <- sig_12%*%sig_22_inv%*%y
sig_delt[[j]] <- diag(sig_11 - sig_12%*%sig_22_inv%*%sig_21)
if(save_pics)pdf(paste0(save_path,"discrep_",dset_labels[j],save_suffix,".pdf"))
plot(x_pred,mu_delt[[j]],type = 'l',
# ylim = c(min(mu_delt[[j]] - 2*sqrt(sig_delt[[j]])),
# max(mu_delt[[j]] + 2*sqrt(sig_delt[[j]]))))
ylim = c(-.23,.23),
xlab = expression(p[T]~"(GeV)"),
#ylab = expression(delta[j]),
main = paste0("Discrepancy for ",dset_labels[j]),
cex.lab = 2,
cex.main = 2,
ylab = "")
title(ylab = expression(delta[j]),cex.lab = 2,mgp=c(2,1,0))
lines(x_pred,mu_delt[[j]] - 2*sqrt(sig_delt[[j]]),col = 'blue',
lty = 2)
lines(x_pred,mu_delt[[j]] + 2*sqrt(sig_delt[[j]]),col = 'blue',
lty = 2)
abline(h=0)
if(include_pts){
points(x_train,y)
}
if(save_pics) dev.off()
}
invisible(list(mu_delt = mu_delt,sig_delt = sig_delt))
}
###########
##Validation Plot
##########
##Requires:
#Holdout point
#Design
#Train dset
# V
# y_sd
# y_mean
##Ugh...should change this to use the new prediction function
predict_holdout <- function(holdout,
design = comp_mod$all_data$scaled_d,
Y_scaled = comp_mod$Y_final,
#Y_orig = Y,
y_sd_cur = comp_mod$y_sd,
y_mean_cur = comp_mod$y_means,
q = length(comp_mod$train_mods), plotit = FALSE,
verbose = FALSE,
cover_level = 95){
train_d <- design[-holdout, ]
test_d <- design[holdout, ]
train_Y <- Y_scaled[-holdout, ]
Y_orig <- sweep(Y_scaled,2,y_sd_cur,"*")%>%
sweep(2,y_mean_cur, "+")
test_Y <- Y_orig[holdout, ]
V_train <- svd(train_Y)$v[ , 1:q]
train_Z = as.matrix(train_Y)%*%V_train
sink("NUL")
train_mod <- lapply(as.data.frame(train_Z),rgasp,design = train_d,
kernel_type = 'pow_exp')
sink()
test_mod <- lapply(train_mod,RobustGaSP::predict,testing_input = test_d)
pred_Z <- lapply(test_mod,function(x)x$mean) %>%
do.call(c,.) %>%
t()
pred_err_Z <- lapply(test_mod,function(x)x$sd^2) %>%
do.call(c,.)
pred_Y <- pred_Z %*% t(V_train) %*% diag(y_sd_cur) + y_mean_cur
pred_err_Y <- diag(y_sd_cur) %*% V_train %*% diag(pred_err_Z) %*% t(V_train) %*% diag(y_sd_cur) %>%
diag()
if(cover_level == 95){
sd_mult = 2
}else if(cover_level == 60){
sd_mult = 1
}else{
stop('Must pick 95% or 60% for cover level')
}
if(plotit){
plot(as.numeric(test_Y),as.numeric(pred_Y),pch = 19,cex = .5,
xlab = 'Holdout Values',
ylab = 'Predicted Values',
main = paste0('Emulator Prediction, Holdout ',holdout),
cex.lab = 2,
cex.axis = 1.3,
cex.main = 1.4,
mgp = c(2.4,1,0),
ylim = c(min(as.numeric(pred_Y) - sd_mult*sqrt(pred_err_Y)),
max(as.numeric(pred_Y) + sd_mult*sqrt(pred_err_Y))))
arrows(as.numeric(test_Y), as.numeric(pred_Y) - sd_mult*sqrt(pred_err_Y),
as.numeric(test_Y), as.numeric(pred_Y) + sd_mult*sqrt(pred_err_Y),
length=0, angle=90, code=3)
abline(a = 0,b = 1)
}
good_pred_95 <- test_Y < as.numeric(pred_Y) + 2*sqrt(pred_err_Y) &
test_Y > as.numeric(pred_Y) - 2*sqrt(pred_err_Y)
good_pred_60 <- test_Y < as.numeric(pred_Y) + sqrt(pred_err_Y) &
test_Y > as.numeric(pred_Y) - sqrt(pred_err_Y)
return(list(mean_cover_95 = mean(good_pred_95), mean_cover_60 = mean(good_pred_60),
mean_width = mean(4*sqrt(pred_err_Y))))
}
collect_all_holdouts <- function(D_c_theta = comp_mod$all_data$scaled_d,
Y_scaled = comp_mod$Y_final,
#Y_orig = Y,
y_sd_cur = comp_mod$y_sd,
y_mean_cur = comp_mod$y_means,
q = length(comp_mod$train_mods)){
n_holdouts <- nrow(D_c_theta)
cover_95 <- cover_60 <- widths <- numeric(n_holdouts)
for(h in 1:n_holdouts){
invisible(pred_hold_h <- predict_holdout(holdout = h,
design = D_c_theta,
Y_scaled = Y_scaled,
y_sd_cur = y_sd_cur,
y_mean_cur = y_mean_cur,
q = q))
cover_95[h] <- pred_hold_h$mean_cover_95
cover_60[h] <- pred_hold_h$mean_cover_60
widths[h] <- pred_hold_h$mean_width
flush.console()
cat("\r FINISHED HOLDOUT ", h ,'/',n_holdouts)
}
return(list(cover_95 = cover_95, cover_60 = cover_60, widths = widths))
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sc_workflow.R
\name{create_report}
\alias{create_report}
\title{create_report}
\usage{
create_report(sample_name, outdir, r1 = "NA", r2 = "NA", outfq = "NA",
read_structure = list(bs1 = 0, bl1 = 0, bs2 = 0, bl2 = 0, us = 0, ul = 0),
filter_settings = list(rmlow = TRUE, rmN = TRUE, minq = 20, numbq = 2),
align_bam = "NA", genome_index = "NA", map_bam = "NA",
exon_anno = "NA", stnd = TRUE, fix_chr = FALSE, barcode_anno = "NA",
max_mis = 1, UMI_cor = 1, gene_fl = FALSE, organism, gene_id_type)
}
\arguments{
\item{sample_name}{sample name}
\item{outdir}{output folder}
\item{r1}{file path of read1}
\item{r2}{file path of read2 default to be NULL}
\item{outfq}{file path of the output of \code{sc_trim_barcode}}
\item{read_structure}{a list contains read structure configuration. For more help see `?sc_trim_barcode`}
\item{filter_settings}{a list contains read filter settings for more help see `?sc_trim_barcode`}
\item{align_bam}{the aligned bam file}
\item{genome_index}{genome index used for alignment}
\item{map_bam}{the mapped bam file}
\item{exon_anno}{the gff exon annotation used. Can have multiple files}
\item{stnd}{whether to perform strand specific mapping}
\item{fix_chr}{add `chr` to chromosome names, fix inconsistant names.}
\item{barcode_anno}{cell barcode annotation file path.}
\item{max_mis}{maximum mismatch allowed in barcode. Default to be 1}
\item{UMI_cor}{correct UMI sequence error: 0 means no correction, 1 means simple correction and merge UMI with distance 1.}
\item{gene_fl}{whether to remove low abundant gene count. Low abundant is defined as only one copy of one UMI for this gene}
\item{organism}{the organism of the data. List of possible names can be retrieved using the function
`listDatasets`from `biomaRt` package. (i.e `mmusculus_gene_ensembl` or `hsapiens_gene_ensembl`)}
\item{gene_id_type}{gene id type of the data A possible list of ids can be retrieved using the function `listAttributes` from `biomaRt` package.
the commonly used id types are `external_gene_name`, `ensembl_gene_id` or `entrezgene`}
}
\value{
no return
}
\description{
create an HTML report using data generated by proprocessing step.
}
\examples{
\dontrun{
create_report(sample_name="sample_001",
outdir="output_dir_of_scPipe",
r1="read1.fq",
r2="read2.fq",
outfq="trim.fq",
read_structure=list(bs1=-1, bl1=2, bs2=6, bl2=8, us=0, ul=6),
filter_settings=list(rmlow=TRUE, rmN=TRUE, minq=20, numbq=2),
align_bam="align.bam",
genome_index="mouse.index",
map_bam="aligned.mapped.bam",
exon_anno="exon_anno.gff3",
stnd=TRUE,
fix_chr=FALSE,
barcode_anno="cell_barcode.csv",
max_mis=1,
UMI_cor=1,
gene_fl=FALSE,
organism="mmusculus_gene_ensembl",
gene_id_type="ensembl_gene_id")
}
}
|
/man/create_report.Rd
|
no_license
|
Bioinfomatics/scPipe
|
R
| false | true | 2,928 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sc_workflow.R
\name{create_report}
\alias{create_report}
\title{create_report}
\usage{
create_report(sample_name, outdir, r1 = "NA", r2 = "NA", outfq = "NA",
read_structure = list(bs1 = 0, bl1 = 0, bs2 = 0, bl2 = 0, us = 0, ul = 0),
filter_settings = list(rmlow = TRUE, rmN = TRUE, minq = 20, numbq = 2),
align_bam = "NA", genome_index = "NA", map_bam = "NA",
exon_anno = "NA", stnd = TRUE, fix_chr = FALSE, barcode_anno = "NA",
max_mis = 1, UMI_cor = 1, gene_fl = FALSE, organism, gene_id_type)
}
\arguments{
\item{sample_name}{sample name}
\item{outdir}{output folder}
\item{r1}{file path of read1}
\item{r2}{file path of read2 default to be NULL}
\item{outfq}{file path of the output of \code{sc_trim_barcode}}
\item{read_structure}{a list contains read structure configuration. For more help see `?sc_trim_barcode`}
\item{filter_settings}{a list contains read filter settings for more help see `?sc_trim_barcode`}
\item{align_bam}{the aligned bam file}
\item{genome_index}{genome index used for alignment}
\item{map_bam}{the mapped bam file}
\item{exon_anno}{the gff exon annotation used. Can have multiple files}
\item{stnd}{whether to perform strand specific mapping}
\item{fix_chr}{add `chr` to chromosome names, fix inconsistant names.}
\item{barcode_anno}{cell barcode annotation file path.}
\item{max_mis}{maximum mismatch allowed in barcode. Default to be 1}
\item{UMI_cor}{correct UMI sequence error: 0 means no correction, 1 means simple correction and merge UMI with distance 1.}
\item{gene_fl}{whether to remove low abundant gene count. Low abundant is defined as only one copy of one UMI for this gene}
\item{organism}{the organism of the data. List of possible names can be retrieved using the function
`listDatasets`from `biomaRt` package. (i.e `mmusculus_gene_ensembl` or `hsapiens_gene_ensembl`)}
\item{gene_id_type}{gene id type of the data A possible list of ids can be retrieved using the function `listAttributes` from `biomaRt` package.
the commonly used id types are `external_gene_name`, `ensembl_gene_id` or `entrezgene`}
}
\value{
no return
}
\description{
create an HTML report using data generated by proprocessing step.
}
\examples{
\dontrun{
create_report(sample_name="sample_001",
outdir="output_dir_of_scPipe",
r1="read1.fq",
r2="read2.fq",
outfq="trim.fq",
read_structure=list(bs1=-1, bl1=2, bs2=6, bl2=8, us=0, ul=6),
filter_settings=list(rmlow=TRUE, rmN=TRUE, minq=20, numbq=2),
align_bam="align.bam",
genome_index="mouse.index",
map_bam="aligned.mapped.bam",
exon_anno="exon_anno.gff3",
stnd=TRUE,
fix_chr=FALSE,
barcode_anno="cell_barcode.csv",
max_mis=1,
UMI_cor=1,
gene_fl=FALSE,
organism="mmusculus_gene_ensembl",
gene_id_type="ensembl_gene_id")
}
}
|
# analysis of cortex transcripts
# 2017-06-06
# load data ####
# load libraries
library(tidyverse)
library(tidytext)
library(stringr)
library(igraph)
library(ggraph)
# load transcript data
cortex <- read_rds("data/cortex.rda")
# clean data ####
# get count of words by episode
cortex_words <- cortex %>%
unnest_tokens(word, line) %>%
anti_join(stop_words) %>%
count(ep_num, ep_title, word) %>%
ungroup()
# count total words per podcast
total_words <- cortex_words %>%
group_by(ep_num) %>%
summarise(total = sum(n))
# join individual word count and total word count
# get tf_idf by podcast
cortex_words <- left_join(cortex_words, total_words) %>%
bind_tf_idf(word, ep_num, n)
cortex_word_plot <- cortex_words %>%
arrange(desc(tf_idf)) %>%
mutate(word = factor(word, levels = rev(unique(word))),
ep_title = factor(ep_title, levels = ep_title))
cortex_word_plot %>%
group_by(ep_num) %>%
top_n(10) %>%
ungroup() %>%
ggplot(aes(x = word, y=tf_idf)) +
geom_col(show.legend=FALSE) +
coord_flip() +
labs(x = "tf-idf",
title = "Most Important Words by Cortex Episode",
subtitle = "Measured by tf-idf") +
facet_wrap(~ep_title, scales = "free_y")+
theme(legend.position = "none",
axis.title.y = element_blank())
# sentiment analysis ####
cortex_ep <- cortex %>%
group_by(ep_num, ep_title) %>%
unnest_tokens(word, line) %>%
ungroup() %>%
select(-line_num)
bingnegative <- get_sentiments("bing") %>%
filter(sentiment == "negative")
bingpositive <- get_sentiments("bing") %>%
filter(sentiment == "positive")
cortex_ep_wordcounts <- cortex_ep %>%
group_by(ep_num, ep_title) %>%
summarise(words = n())
cortex_negative <- cortex_ep %>%
semi_join(bingnegative) %>%
group_by(ep_num, ep_title) %>%
summarize(negativewords = n()) %>%
left_join(cortex_ep_wordcounts, by = c("ep_num", "ep_title")) %>%
ungroup() %>%
mutate(ratio = negativewords/words,
relative_neg = mean(ratio) - ratio) %>%
ungroup()
cortex_positive <- cortex_ep %>%
semi_join(bingpositive) %>%
group_by(ep_num, ep_title) %>%
summarize(positivewords = n()) %>%
left_join(cortex_ep_wordcounts, by = c("ep_num", "ep_title")) %>%
ungroup() %>%
mutate(ratio = positivewords/words,
relative_pos = mean(ratio) - ratio) %>%
ungroup()
ggplot(cortex_negative, aes(x = ep_num, y = ratio))+
geom_bar(stat = "identity", fill = "firebrick")+
geom_smooth(span = .3, se = FALSE, color = "grey64")
ggplot(cortex_positive, aes(x = ep_num, y = ratio))+
geom_bar(stat = "identity", fill = "green4")+
geom_smooth(span = .3, se = FALSE, color = "grey64")
# alternative sentiment #####
cortex_sentiment <- cortex %>%
unnest_tokens(word, line) %>%
inner_join(get_sentiments("bing")) %>%
group_by(ep_num, ep_title, line_num, sentiment) %>%
summarise(total = n()) %>%
spread(sentiment, total, fill = 0) %>%
ungroup() %>%
group_by(ep_num, ep_title) %>%
mutate(sentiment = positive - negative,
relative = sentiment - mean(sentiment),
line_group = ntile(line_num, 100)) %>%
group_by(ep_num, ep_title, line_group) %>%
summarise(sentiment = sum(sentiment)) %>%
ungroup() %>%
mutate(index = row_number(),
ep_title = factor(ep_title, levels = cortex_ep_wordcounts$ep_title))
ggplot(cortex_sentiment, aes(x = index, y = sentiment, fill = ep_title))+
geom_col()+
#geom_smooth(se = FALSE, span = .3)+
theme(legend.position = "none") +
facet_wrap(~ep_title, scales = "free_x")
# ngrams ####
cortex_bigrams <- cortex %>%
group_by(ep_num, ep_title) %>%
summarise(text = paste(line, collapse = "")) %>%
unnest_tokens(bigram, text, token = "ngrams", n = 2) %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(word1 != "yeah" & word2 != "yeah") %>%
filter(word1 != "code" & word2 != "cortex") %>%
filter(word1 != "blue" & word2 != "apron") %>%
filter(word1 != "ten" & word2 != "percent") %>%
unite(bigram, word1, word2, sep = " ") %>%
mutate(ep_title = factor(ep_title, levels = cortex_ep_wordcounts$ep_title)) %>%
count(ep_title, bigram, sort = TRUE)
total_bigrams <- cortex_bigrams %>%
group_by(ep_title) %>%
summarise(total = sum(n))
# join individual word count and total word count
# get tf_idf by podcast
cortex_bigram_tf_idf <- left_join(cortex_bigrams, total_bigrams) %>%
bind_tf_idf(bigram, ep_title, n)
cortex_bigram_plot <- cortex_bigrams_tf_idf %>%
arrange(desc(tf_idf)) %>%
mutate(bigram = factor(bigram, levels = rev(unique(bigram))))
cortex_bigram_plot %>%
group_by(ep_title) %>%
top_n(10) %>%
ungroup() %>%
ggplot(aes(x = bigram, y=tf_idf)) +
geom_col(show.legend=FALSE) +
coord_flip() +
labs(x = "tf-idf",
title = "Most Important Bigrams by Cortex Episode",
subtitle = "Measured by tf-idf") +
facet_wrap(~ep_title, scales = "free_y")+
theme(legend.position = "none",
axis.title.y = element_blank())
bigram_graph <- cortex %>%
group_by(ep_num, ep_title) %>%
summarise(text = paste(line, collapse = "")) %>%
unnest_tokens(bigram, text, token = "ngrams", n = 2) %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(word1 != "yeah" & word2 != "yeah") %>%
filter(word1 != "code" & word2 != "cortex") %>%
filter(word1 != "blue" & word2 != "apron") %>%
filter(word1 != "ten" & word2 != "percent") %>%
filter(word1 != "credit" & word2 != "card") %>%
filter(word1 != "card" & word2 != "required") %>%
filter(word1 != "real" & word2 != "afm") %>%
filter(word1 != "text" & word2 != "expander") %>%
filter(word1 != "pdf" & word2 != "pen") %>%
filter(word1 != "ten" & word2 != "percent") %>%
filter(word1 != "30" & word2 != "day") %>%
filter(word1 != "offer" & word2 != "code") %>%
filter(word1 != "fresh" & word2 != "books") %>%
filter(word1 != "free" & word2 != "trial") %>%
filter(word1 != "uni" & word2 != "box") %>%
filter(word1 != "risk" & word2 != "free") %>%
filter(word1 != "completely" & word2 != "risk") %>%
filter(word1 != "movement" & word2 != "watches") %>%
filter(word1 != "incredible" & word2 != "home") %>%
filter(word1 != "home" & word2 != "cooked") %>%
filter(word1 != "cooked" & word2 != "meals") %>%
filter(word1 != "super" & word2 != "fast") %>%
filter(word1 != "super" & word2 != "easy") %>%
filter(word1 != "super" & word2 != "simple") %>%
filter(word1 != "freshbooks" & word2 != "customers") %>%
filter(word1 != "conditions" & word2 != "apply") %>%
filter(word1 != "professionally" & word2 != "designed") %>%
filter(word1 != "start" & word2 != "building") %>%
filter(word1 != "live" & word2 != "chat") %>%
filter(word1 != "continued" & word2 != "support") %>%
filter(word1 != "fifty" & word2 != "dollars") %>%
filter(word1 != "hundred" & word2 != "dollars") %>%
filter(word1 != "perfect" & word2 != "domain") %>%
filter(word1 != "domain" & word2 != "dames") %>%
filter(word2 != "minutes") %>%
filter(word1 != "10" & word2 != "people") %>%
filter(word1 != "week's" & word2 != "episode") %>%
filter(word1 != "today's" & word2 != "episode") %>%
filter(word1 != "highly" & word2 != "recommend") %>%
filter(word1 != "commerce" & word2 != "platform") %>%
filter(word1 != "relay" & word2 != "fm") %>%
count(word1, word2, sort = TRUE) %>%
filter(n > 10) %>%
graph_from_data_frame()
set.seed(51)
a <- grid::arrow(type = "closed", length = unit(.15, "inches"))
ggraph(bigram_graph, layout = "fr") +
geom_edge_link(aes(edge_alpha = n), show.legend = FALSE,
arrow = a, end_cap = circle(.03, 'inches')) +
geom_node_point(how.legend = FALSE) +
geom_node_point(aes(size = n), color = "lightblue", size = 3) +
geom_node_text(aes(label = name), vjust = 1, hjust = 1) +
theme_void()
|
/R/cortex.R
|
no_license
|
alspur/tidy_pod
|
R
| false | false | 7,935 |
r
|
# analysis of cortex transcripts
# 2017-06-06
# load data ####
# load libraries
library(tidyverse)
library(tidytext)
library(stringr)
library(igraph)
library(ggraph)
# load transcript data
cortex <- read_rds("data/cortex.rda")
# clean data ####
# get count of words by episode
cortex_words <- cortex %>%
unnest_tokens(word, line) %>%
anti_join(stop_words) %>%
count(ep_num, ep_title, word) %>%
ungroup()
# count total words per podcast
total_words <- cortex_words %>%
group_by(ep_num) %>%
summarise(total = sum(n))
# join individual word count and total word count
# get tf_idf by podcast
cortex_words <- left_join(cortex_words, total_words) %>%
bind_tf_idf(word, ep_num, n)
cortex_word_plot <- cortex_words %>%
arrange(desc(tf_idf)) %>%
mutate(word = factor(word, levels = rev(unique(word))),
ep_title = factor(ep_title, levels = ep_title))
cortex_word_plot %>%
group_by(ep_num) %>%
top_n(10) %>%
ungroup() %>%
ggplot(aes(x = word, y=tf_idf)) +
geom_col(show.legend=FALSE) +
coord_flip() +
labs(x = "tf-idf",
title = "Most Important Words by Cortex Episode",
subtitle = "Measured by tf-idf") +
facet_wrap(~ep_title, scales = "free_y")+
theme(legend.position = "none",
axis.title.y = element_blank())
# sentiment analysis ####
cortex_ep <- cortex %>%
group_by(ep_num, ep_title) %>%
unnest_tokens(word, line) %>%
ungroup() %>%
select(-line_num)
bingnegative <- get_sentiments("bing") %>%
filter(sentiment == "negative")
bingpositive <- get_sentiments("bing") %>%
filter(sentiment == "positive")
cortex_ep_wordcounts <- cortex_ep %>%
group_by(ep_num, ep_title) %>%
summarise(words = n())
cortex_negative <- cortex_ep %>%
semi_join(bingnegative) %>%
group_by(ep_num, ep_title) %>%
summarize(negativewords = n()) %>%
left_join(cortex_ep_wordcounts, by = c("ep_num", "ep_title")) %>%
ungroup() %>%
mutate(ratio = negativewords/words,
relative_neg = mean(ratio) - ratio) %>%
ungroup()
cortex_positive <- cortex_ep %>%
semi_join(bingpositive) %>%
group_by(ep_num, ep_title) %>%
summarize(positivewords = n()) %>%
left_join(cortex_ep_wordcounts, by = c("ep_num", "ep_title")) %>%
ungroup() %>%
mutate(ratio = positivewords/words,
relative_pos = mean(ratio) - ratio) %>%
ungroup()
ggplot(cortex_negative, aes(x = ep_num, y = ratio))+
geom_bar(stat = "identity", fill = "firebrick")+
geom_smooth(span = .3, se = FALSE, color = "grey64")
ggplot(cortex_positive, aes(x = ep_num, y = ratio))+
geom_bar(stat = "identity", fill = "green4")+
geom_smooth(span = .3, se = FALSE, color = "grey64")
# alternative sentiment #####
cortex_sentiment <- cortex %>%
unnest_tokens(word, line) %>%
inner_join(get_sentiments("bing")) %>%
group_by(ep_num, ep_title, line_num, sentiment) %>%
summarise(total = n()) %>%
spread(sentiment, total, fill = 0) %>%
ungroup() %>%
group_by(ep_num, ep_title) %>%
mutate(sentiment = positive - negative,
relative = sentiment - mean(sentiment),
line_group = ntile(line_num, 100)) %>%
group_by(ep_num, ep_title, line_group) %>%
summarise(sentiment = sum(sentiment)) %>%
ungroup() %>%
mutate(index = row_number(),
ep_title = factor(ep_title, levels = cortex_ep_wordcounts$ep_title))
ggplot(cortex_sentiment, aes(x = index, y = sentiment, fill = ep_title))+
geom_col()+
#geom_smooth(se = FALSE, span = .3)+
theme(legend.position = "none") +
facet_wrap(~ep_title, scales = "free_x")
# ngrams ####
cortex_bigrams <- cortex %>%
group_by(ep_num, ep_title) %>%
summarise(text = paste(line, collapse = "")) %>%
unnest_tokens(bigram, text, token = "ngrams", n = 2) %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(word1 != "yeah" & word2 != "yeah") %>%
filter(word1 != "code" & word2 != "cortex") %>%
filter(word1 != "blue" & word2 != "apron") %>%
filter(word1 != "ten" & word2 != "percent") %>%
unite(bigram, word1, word2, sep = " ") %>%
mutate(ep_title = factor(ep_title, levels = cortex_ep_wordcounts$ep_title)) %>%
count(ep_title, bigram, sort = TRUE)
total_bigrams <- cortex_bigrams %>%
group_by(ep_title) %>%
summarise(total = sum(n))
# join individual word count and total word count
# get tf_idf by podcast
cortex_bigram_tf_idf <- left_join(cortex_bigrams, total_bigrams) %>%
bind_tf_idf(bigram, ep_title, n)
cortex_bigram_plot <- cortex_bigrams_tf_idf %>%
arrange(desc(tf_idf)) %>%
mutate(bigram = factor(bigram, levels = rev(unique(bigram))))
cortex_bigram_plot %>%
group_by(ep_title) %>%
top_n(10) %>%
ungroup() %>%
ggplot(aes(x = bigram, y=tf_idf)) +
geom_col(show.legend=FALSE) +
coord_flip() +
labs(x = "tf-idf",
title = "Most Important Bigrams by Cortex Episode",
subtitle = "Measured by tf-idf") +
facet_wrap(~ep_title, scales = "free_y")+
theme(legend.position = "none",
axis.title.y = element_blank())
bigram_graph <- cortex %>%
group_by(ep_num, ep_title) %>%
summarise(text = paste(line, collapse = "")) %>%
unnest_tokens(bigram, text, token = "ngrams", n = 2) %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(word1 != "yeah" & word2 != "yeah") %>%
filter(word1 != "code" & word2 != "cortex") %>%
filter(word1 != "blue" & word2 != "apron") %>%
filter(word1 != "ten" & word2 != "percent") %>%
filter(word1 != "credit" & word2 != "card") %>%
filter(word1 != "card" & word2 != "required") %>%
filter(word1 != "real" & word2 != "afm") %>%
filter(word1 != "text" & word2 != "expander") %>%
filter(word1 != "pdf" & word2 != "pen") %>%
filter(word1 != "ten" & word2 != "percent") %>%
filter(word1 != "30" & word2 != "day") %>%
filter(word1 != "offer" & word2 != "code") %>%
filter(word1 != "fresh" & word2 != "books") %>%
filter(word1 != "free" & word2 != "trial") %>%
filter(word1 != "uni" & word2 != "box") %>%
filter(word1 != "risk" & word2 != "free") %>%
filter(word1 != "completely" & word2 != "risk") %>%
filter(word1 != "movement" & word2 != "watches") %>%
filter(word1 != "incredible" & word2 != "home") %>%
filter(word1 != "home" & word2 != "cooked") %>%
filter(word1 != "cooked" & word2 != "meals") %>%
filter(word1 != "super" & word2 != "fast") %>%
filter(word1 != "super" & word2 != "easy") %>%
filter(word1 != "super" & word2 != "simple") %>%
filter(word1 != "freshbooks" & word2 != "customers") %>%
filter(word1 != "conditions" & word2 != "apply") %>%
filter(word1 != "professionally" & word2 != "designed") %>%
filter(word1 != "start" & word2 != "building") %>%
filter(word1 != "live" & word2 != "chat") %>%
filter(word1 != "continued" & word2 != "support") %>%
filter(word1 != "fifty" & word2 != "dollars") %>%
filter(word1 != "hundred" & word2 != "dollars") %>%
filter(word1 != "perfect" & word2 != "domain") %>%
filter(word1 != "domain" & word2 != "dames") %>%
filter(word2 != "minutes") %>%
filter(word1 != "10" & word2 != "people") %>%
filter(word1 != "week's" & word2 != "episode") %>%
filter(word1 != "today's" & word2 != "episode") %>%
filter(word1 != "highly" & word2 != "recommend") %>%
filter(word1 != "commerce" & word2 != "platform") %>%
filter(word1 != "relay" & word2 != "fm") %>%
count(word1, word2, sort = TRUE) %>%
filter(n > 10) %>%
graph_from_data_frame()
set.seed(51)
a <- grid::arrow(type = "closed", length = unit(.15, "inches"))
ggraph(bigram_graph, layout = "fr") +
geom_edge_link(aes(edge_alpha = n), show.legend = FALSE,
arrow = a, end_cap = circle(.03, 'inches')) +
geom_node_point(how.legend = FALSE) +
geom_node_point(aes(size = n), color = "lightblue", size = 3) +
geom_node_text(aes(label = name), vjust = 1, hjust = 1) +
theme_void()
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{sumboxmktCnt_trade}
\alias{sumboxmktCnt_trade}
\title{Number of Monte Carlo Simulations Performed in "Summary" Tab for Tariffs}
\format{
A data frame with 24 rows and 3 variables
\describe{
\item{Outcome}{Consumer Harm, Domestic Firm Benefit, Foreign Firm Harm, Industry Price Change, Net Domestic Harm, Net Total Harm, Domestic Firm Price Change, Foreign Firm Price Change}
\item{Cnt}{number of tariffs simulated}
\item{tariffThresh}{tariff threshold in percent (10--30)}
}
}
\usage{
sumboxmktCnt_trade
}
\description{
A dataset containing the information necessary to calculate the number of tariffs
used to generate the plots for the Summary tab of Numerical Simulations for Tariffs.
}
\references{
Taragin, C., & Loudermilk, M. (2019). Using measures of competitive harm for optimal screening of horizontal mergers. mimeo.\doi{10.13140/RG.2.2.30872.85760/1}.
}
\keyword{datasets}
|
/man/sumboxmktCnt_trade.Rd
|
no_license
|
cran/competitiontoolbox
|
R
| false | true | 1,021 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{sumboxmktCnt_trade}
\alias{sumboxmktCnt_trade}
\title{Number of Monte Carlo Simulations Performed in "Summary" Tab for Tariffs}
\format{
A data frame with 24 rows and 3 variables
\describe{
\item{Outcome}{Consumer Harm, Domestic Firm Benefit, Foreign Firm Harm, Industry Price Change, Net Domestic Harm, Net Total Harm, Domestic Firm Price Change, Foreign Firm Price Change}
\item{Cnt}{number of tariffs simulated}
\item{tariffThresh}{tariff threshold in percent (10--30)}
}
}
\usage{
sumboxmktCnt_trade
}
\description{
A dataset containing the information necessary to calculate the number of tariffs
used to generate the plots for the Summary tab of Numerical Simulations for Tariffs.
}
\references{
Taragin, C., & Loudermilk, M. (2019). Using measures of competitive harm for optimal screening of horizontal mergers. mimeo.\doi{10.13140/RG.2.2.30872.85760/1}.
}
\keyword{datasets}
|
## File Name: plot.lsdm.R
## File Version: 0.02
plot.lsdm <- function(x, ...)
{
graphics::matplot( x=x$theta, y=t(x$attr.curves), xlab=expression(theta),
ylab="Attribute response curve", ylim=c(0,1), ... )
}
|
/R/plot.lsdm.R
|
no_license
|
cran/sirt
|
R
| false | false | 231 |
r
|
## File Name: plot.lsdm.R
## File Version: 0.02
plot.lsdm <- function(x, ...)
{
graphics::matplot( x=x$theta, y=t(x$attr.curves), xlab=expression(theta),
ylab="Attribute response curve", ylim=c(0,1), ... )
}
|
#for loop for changing MDA coverage in the first village
#MDA_eff_hm
#MDA success on homogeniety vs coverage
#20190405
#outcome is the achievement of <1/1000/year in a year starting with the end of the 3rd MDA round
#see #scenario tag for things to change in each scenario
#x axis variable: homogen, [0 to 100] %
#y axis variable: cmda_2, [0 to 80] %, coverage of MDA in second village
#z: cmda_1, [70, 80, 90] %
setwd("~/OneDrive/MORU/Projects/TCE_MDA effect/MDA_eff_hm/") #mac
library(deSolve)
library(shiny)
library(TSA)
library(Rcpp)
library(stringr)
library(lattice)
#new formula with Ricardo 20190805
sourceCpp("functions_latest/modGMS_seas.cpp", rebuild = TRUE)
source("functions_latest/no longer app.R")
# sourceCpp("functions/modGMS_seas.cpp")
# source("functions/no longer app.R")
timeVector <- read.csv('parameters/times.csv') #to figure out when the MDA finishes
#scenario
##initialize input and output storage####
testfor2j <- rep(0:100,100) #homogen
testfor2i <- rep(0:99,each=101) #cmda_2
testfor2 <- cbind(testfor2j,testfor2i)
# colnames(testfor2) <- c('homogen','cmda_2')
colnames(testfor2) <- NULL
#scenario
####for template####
# for(i in 1:81){
# for(j in 1:101){
# homogen <- testfor2[((i-1)*101)+j,1]
# cmda_2 <- testfor2[((i-1)*101)+j,2]
# ###other codes for running the model
# result[[((i-1)*101)+j]] <- successMDA
# }
# }
####non-reactive parameters####
####interventions####
EDATon = TRUE
ITNon = TRUE
IRSon = FALSE
MDAon = TRUE
primon = TRUE #FALSE
MSATon = TRUE
VACon = FALSE
####non-reactive functions####
#got from the "parameters" folder #scenario
#cmda_1Loop <- seq(70, by=10, to=90) #1st batch 20190407
# cmda_1Loop <- seq(30, by=10, to=60) #2nd batch 20190409
#cmda_1Loop <- seq(10, by=10, to=20) #3rd batch 20190409
#cmda_1Loop <- seq(70, by=10, to=90) #20190522
cmda_1Loop <- 81.56 #TOT
for(loop in 1:length(cmda_1Loop)){
result <- list()
API <- 2.5
eta <- 30
covEDAT0 <- 25
covITN0 <- 70
effITN <- 30
covIRS0 <- 0
effIRS <- 15
muC <- 1
muA <- 1
muU <- 1
percfail2018 <- 5
percfail2019 <- 15
percfail2020 <- 30
bh_max0 <- 16
bh_max1 <- 16 #20 #12 #20 is next #8 #24 #16
rhoa <- 55
rhou <- 17
EDATscale <- 1
covEDATi <- 70
ITNscale <- 1
covITNi <- 90
IRSscale <- 1
covIRSi <- 90
lossd <- 30
dm0 <- 3
dm1 <- 3
cmda_1 <- cmda_1Loop[loop]#80 #90
#cmda_2 <- 50 #scenario
#homogen <- 0 #scenario
tm_1 <- 9
tm_2 <- 9
p1v <- 0.5
effv_1 <- 75
effv_2 <- 80
vh <- 90
MSATscale <- 1
covMSATi <- 90
MSATsensC <- 99
MSATsensA <- 87
MSATsensU <- 44
#non-reactive parameters
# define the number of weeks to run the model
dt<-1/12
startyear<-2007
stopyear<-2023
maxt<-stopyear-startyear
times <- seq(0, maxt, by = dt)
tsteps<-length(times)
# initial prevalence
initprevR <- (0.001*API)
#ParLabel <- read.table('functions/ParLabel.csv', sep=",", as.is=TRUE)
# scenario_0<-c(EDATon = 0,
# ITNon = 0,
# IRSon = 0,
# MDAon = 0,
# primon = 0,
# MSATon = 0,
# VACon = 0)
####for loop#####
for(i in 1:100){
for(j in 1:101){
homogen <- testfor2[((i-1)*101)+j,1]
cmda_2 <- testfor2[((i-1)*101)+j,2]
###other codes for running the model
scenario_iR<-(c(EDATon = EDATon,
ITNon = ITNon,
IRSon = IRSon,
MDAon = MDAon,
primon = primon,
MSATon = MSATon,
VACon = as.numeric(VACon)))
parametersR <- (c(
bh_max0 = bh_max0, # bites per human per night
bh_max1 = bh_max1,
eta = eta,
covEDAT0 = covEDAT0,
covITN0 = covITN0,
effITN = effITN,
covIRS0 = covIRS0,
effIRS = effIRS,
muC = muC,
muA = muA,
muU = muU,
percfail2018 = percfail2018,
percfail2019 = percfail2019,
percfail2020 = percfail2020,
EDATscale = EDATscale,
covEDATi = covEDATi,
ITNscale = ITNscale,
covITNi = covITNi,
IRSscale = IRSscale,
covIRSi = covIRSi,
cmda_1 = cmda_1,
cmda_2 = cmda_2,
tm_1 = tm_1, # timing of 1st round [2018 to 2021 - 1 month steps]
tm_2 = tm_2, # timing of 2nd round [2018+(1/12) to 2021 - 1 month steps]
dm0 = dm0,
dm1 = dm1,
lossd = lossd,
MSATscale = MSATscale,
covMSATi = covMSATi,
MSATsensC = MSATsensC,
MSATsensA = MSATsensA,
MSATsensU = MSATsensU,
effv_1 = effv_1,
effv_2 = effv_2,
vh = vh,
homogen = homogen,
p1v = p1v,
rhoa=rhoa,
rhou=rhou
))
#GMSout0R <- (runGMS(initprevR, scenario_0,parametersR))
GMSoutiR <- (runGMS(initprevR, scenario_iR,parametersR))
#labeling the columns
outLab <- c("year","detectedIncidence1","totalIncidence1","prevalence1","detectedIncidence2","totalIncidence2","prevalence2")
colnames(GMSoutiR) <- outLab
#grabbing the time of MDA success
#GMSoutiR[GMSoutiR[,3]<(1/12),1]
# MDAsuccessV1 <- GMSoutiR[,3]<(1/12)
# MDAsuccessV2 <- GMSoutiR[,6]<(1/12)
# successMDA <- cbind(MDAsuccessV1, MDAsuccessV2)
#calculating one year incidence per 1000 immediately after the end of MDA
MDAendsV1 <- which(timeVector==(2018+(tm_1+dm0)/12))
MDAendsV2 <- which(timeVector==(2018+(tm_2+dm1)/12))
DecOneYrIncV1 <- sum(GMSoutiR[MDAendsV1:(MDAendsV1+12),2])
TotOneYrIncV1 <- sum(GMSoutiR[MDAendsV1:(MDAendsV1+12),3])
DecOneYrIncV2 <- sum(GMSoutiR[MDAendsV2:(MDAendsV2+12),5])
TotOneYrIncV2 <- sum(GMSoutiR[MDAendsV2:(MDAendsV2+12),6])
OneYrInc <- cbind(DecOneYrIncV1, TotOneYrIncV1, DecOneYrIncV2, TotOneYrIncV2)
result[[((i-1)*101)+j]] <- OneYrInc #successMDA
}
}
#time component #scenario
#outside of 'for' loop
#write.table(GMSoutiR[,1],'parameters/times.csv', col.names = 'time', row.names = FALSE)
#saveRDS(result, paste('results_homo_cov/results_loop_',loop,"_",gsub("\\:","",Sys.time()),'.rds',sep=''))
#saveRDS(result, paste('results_homo_cov_start0_seas/results_loop_',loop,"_",gsub("\\:","",Sys.time()),'.rds',sep='')) #default HBR_MAX: 16
#saveRDS(result, paste('results_homo_cov_start0_seas_village2highAPI/results_loop_',loop,"_",gsub("\\:","",Sys.time()),'.rds',sep='')) #hbr_max2: 24, highAPI
#saveRDS(result, paste('results_homo_cov_start0_seas_village2lowAPI/results_loop_',loop,"_",gsub("\\:","",Sys.time()),'.rds',sep='')) #hbr_max2: 8, lowAPI
#with seasonality on [switch is inside modGMS.cpp]
#saveRDS(result, paste('results_homo_cov_start0_seas/results_loop_',loop,"_",gsub("\\:","",Sys.time()),'.rds',sep=''))
saveRDS(result, paste('results_TOT/results_loop_',loop,"_",gsub("\\:","",Sys.time()),'.rds',sep='')) #hbr_max2: 24, highAPI
#scenario
#Analysing the data list 'results_.rds'####
#in different plotting file
}
|
/new versions/TOT.R
|
no_license
|
SaiTheinThanTun/MDA_eff_hm
|
R
| false | false | 7,224 |
r
|
#for loop for changing MDA coverage in the first village
#MDA_eff_hm
#MDA success on homogeniety vs coverage
#20190405
#outcome is the achievement of <1/1000/year in a year starting with the end of the 3rd MDA round
#see #scenario tag for things to change in each scenario
#x axis variable: homogen, [0 to 100] %
#y axis variable: cmda_2, [0 to 80] %, coverage of MDA in second village
#z: cmda_1, [70, 80, 90] %
setwd("~/OneDrive/MORU/Projects/TCE_MDA effect/MDA_eff_hm/") #mac
library(deSolve)
library(shiny)
library(TSA)
library(Rcpp)
library(stringr)
library(lattice)
#new formula with Ricardo 20190805
sourceCpp("functions_latest/modGMS_seas.cpp", rebuild = TRUE)
source("functions_latest/no longer app.R")
# sourceCpp("functions/modGMS_seas.cpp")
# source("functions/no longer app.R")
timeVector <- read.csv('parameters/times.csv') #to figure out when the MDA finishes
#scenario
##initialize input and output storage####
testfor2j <- rep(0:100,100) #homogen
testfor2i <- rep(0:99,each=101) #cmda_2
testfor2 <- cbind(testfor2j,testfor2i)
# colnames(testfor2) <- c('homogen','cmda_2')
colnames(testfor2) <- NULL
#scenario
####for template####
# for(i in 1:81){
# for(j in 1:101){
# homogen <- testfor2[((i-1)*101)+j,1]
# cmda_2 <- testfor2[((i-1)*101)+j,2]
# ###other codes for running the model
# result[[((i-1)*101)+j]] <- successMDA
# }
# }
####non-reactive parameters####
####interventions####
EDATon = TRUE
ITNon = TRUE
IRSon = FALSE
MDAon = TRUE
primon = TRUE #FALSE
MSATon = TRUE
VACon = FALSE
####non-reactive functions####
#got from the "parameters" folder #scenario
#cmda_1Loop <- seq(70, by=10, to=90) #1st batch 20190407
# cmda_1Loop <- seq(30, by=10, to=60) #2nd batch 20190409
#cmda_1Loop <- seq(10, by=10, to=20) #3rd batch 20190409
#cmda_1Loop <- seq(70, by=10, to=90) #20190522
cmda_1Loop <- 81.56 #TOT
for(loop in 1:length(cmda_1Loop)){
result <- list()
API <- 2.5
eta <- 30
covEDAT0 <- 25
covITN0 <- 70
effITN <- 30
covIRS0 <- 0
effIRS <- 15
muC <- 1
muA <- 1
muU <- 1
percfail2018 <- 5
percfail2019 <- 15
percfail2020 <- 30
bh_max0 <- 16
bh_max1 <- 16 #20 #12 #20 is next #8 #24 #16
rhoa <- 55
rhou <- 17
EDATscale <- 1
covEDATi <- 70
ITNscale <- 1
covITNi <- 90
IRSscale <- 1
covIRSi <- 90
lossd <- 30
dm0 <- 3
dm1 <- 3
cmda_1 <- cmda_1Loop[loop]#80 #90
#cmda_2 <- 50 #scenario
#homogen <- 0 #scenario
tm_1 <- 9
tm_2 <- 9
p1v <- 0.5
effv_1 <- 75
effv_2 <- 80
vh <- 90
MSATscale <- 1
covMSATi <- 90
MSATsensC <- 99
MSATsensA <- 87
MSATsensU <- 44
#non-reactive parameters
# define the number of weeks to run the model
dt<-1/12
startyear<-2007
stopyear<-2023
maxt<-stopyear-startyear
times <- seq(0, maxt, by = dt)
tsteps<-length(times)
# initial prevalence
initprevR <- (0.001*API)
#ParLabel <- read.table('functions/ParLabel.csv', sep=",", as.is=TRUE)
# scenario_0<-c(EDATon = 0,
# ITNon = 0,
# IRSon = 0,
# MDAon = 0,
# primon = 0,
# MSATon = 0,
# VACon = 0)
####for loop#####
for(i in 1:100){
for(j in 1:101){
homogen <- testfor2[((i-1)*101)+j,1]
cmda_2 <- testfor2[((i-1)*101)+j,2]
###other codes for running the model
scenario_iR<-(c(EDATon = EDATon,
ITNon = ITNon,
IRSon = IRSon,
MDAon = MDAon,
primon = primon,
MSATon = MSATon,
VACon = as.numeric(VACon)))
parametersR <- (c(
bh_max0 = bh_max0, # bites per human per night
bh_max1 = bh_max1,
eta = eta,
covEDAT0 = covEDAT0,
covITN0 = covITN0,
effITN = effITN,
covIRS0 = covIRS0,
effIRS = effIRS,
muC = muC,
muA = muA,
muU = muU,
percfail2018 = percfail2018,
percfail2019 = percfail2019,
percfail2020 = percfail2020,
EDATscale = EDATscale,
covEDATi = covEDATi,
ITNscale = ITNscale,
covITNi = covITNi,
IRSscale = IRSscale,
covIRSi = covIRSi,
cmda_1 = cmda_1,
cmda_2 = cmda_2,
tm_1 = tm_1, # timing of 1st round [2018 to 2021 - 1 month steps]
tm_2 = tm_2, # timing of 2nd round [2018+(1/12) to 2021 - 1 month steps]
dm0 = dm0,
dm1 = dm1,
lossd = lossd,
MSATscale = MSATscale,
covMSATi = covMSATi,
MSATsensC = MSATsensC,
MSATsensA = MSATsensA,
MSATsensU = MSATsensU,
effv_1 = effv_1,
effv_2 = effv_2,
vh = vh,
homogen = homogen,
p1v = p1v,
rhoa=rhoa,
rhou=rhou
))
#GMSout0R <- (runGMS(initprevR, scenario_0,parametersR))
GMSoutiR <- (runGMS(initprevR, scenario_iR,parametersR))
#labeling the columns
outLab <- c("year","detectedIncidence1","totalIncidence1","prevalence1","detectedIncidence2","totalIncidence2","prevalence2")
colnames(GMSoutiR) <- outLab
#grabbing the time of MDA success
#GMSoutiR[GMSoutiR[,3]<(1/12),1]
# MDAsuccessV1 <- GMSoutiR[,3]<(1/12)
# MDAsuccessV2 <- GMSoutiR[,6]<(1/12)
# successMDA <- cbind(MDAsuccessV1, MDAsuccessV2)
#calculating one year incidence per 1000 immediately after the end of MDA
MDAendsV1 <- which(timeVector==(2018+(tm_1+dm0)/12))
MDAendsV2 <- which(timeVector==(2018+(tm_2+dm1)/12))
DecOneYrIncV1 <- sum(GMSoutiR[MDAendsV1:(MDAendsV1+12),2])
TotOneYrIncV1 <- sum(GMSoutiR[MDAendsV1:(MDAendsV1+12),3])
DecOneYrIncV2 <- sum(GMSoutiR[MDAendsV2:(MDAendsV2+12),5])
TotOneYrIncV2 <- sum(GMSoutiR[MDAendsV2:(MDAendsV2+12),6])
OneYrInc <- cbind(DecOneYrIncV1, TotOneYrIncV1, DecOneYrIncV2, TotOneYrIncV2)
result[[((i-1)*101)+j]] <- OneYrInc #successMDA
}
}
#time component #scenario
#outside of 'for' loop
#write.table(GMSoutiR[,1],'parameters/times.csv', col.names = 'time', row.names = FALSE)
#saveRDS(result, paste('results_homo_cov/results_loop_',loop,"_",gsub("\\:","",Sys.time()),'.rds',sep=''))
#saveRDS(result, paste('results_homo_cov_start0_seas/results_loop_',loop,"_",gsub("\\:","",Sys.time()),'.rds',sep='')) #default HBR_MAX: 16
#saveRDS(result, paste('results_homo_cov_start0_seas_village2highAPI/results_loop_',loop,"_",gsub("\\:","",Sys.time()),'.rds',sep='')) #hbr_max2: 24, highAPI
#saveRDS(result, paste('results_homo_cov_start0_seas_village2lowAPI/results_loop_',loop,"_",gsub("\\:","",Sys.time()),'.rds',sep='')) #hbr_max2: 8, lowAPI
#with seasonality on [switch is inside modGMS.cpp]
#saveRDS(result, paste('results_homo_cov_start0_seas/results_loop_',loop,"_",gsub("\\:","",Sys.time()),'.rds',sep=''))
saveRDS(result, paste('results_TOT/results_loop_',loop,"_",gsub("\\:","",Sys.time()),'.rds',sep='')) #hbr_max2: 24, highAPI
#scenario
#Analysing the data list 'results_.rds'####
#in different plotting file
}
|
# R code no2
#set working directory EN
setwd("/Users/samuelepapiccio/lab/EN/")
library(raster)
library(RStoolbox)
# Import first image (single band)
# We will select band 1, but the raster function enables to select other single band
EN01<- raster("EN_0001.png")
# Plot the first image with the preferred Color Ramp Palette
cls<- colorRampPalette(c("red","pink","orange","yellow"))(200)
plot(EN01,col=cls)
# Import the last image and plot it with the previous color ramp palette
EN13<- raster("EN_0013.png")
cls<- colorRampPalette(c("red","pink","orange","yellow"))(200)
plot(EN13,col=cls)
# Make the difference between the two images and plot it
ENdif<- EN01-EN13
plot(ENdif,col=cls)
#plot everything altoghether
par(mfrow=c(3,1))
plot(EN01,col=cls, main="NO2 in January")
plot(EN13,col=cls, main="NO2 in March")
plot(ENdif,col=cls, main="NO2 difference (January - March)")
# Import the whole set
#list of files
rlist<- list.files(pattern="EN")
rlist
import<-lapply(rlist,raster)
import
EN<- stack(import)
plot(EN, col=cls)
# Replicate the plot of images 01 and 13 using the stack
par(mfrow=c(3,1))
plot(EN$EN_0001,col=cls,main="NO2 in January")
plot(EN$EN_0013,col=cls, main="NO2 in March")
# Compute a PCA over the 13 images
ENpca<- rasterPCA(EN)
summary(ENpca$model)
dev.off()
plotRGB(ENpca$map, r=1, g=2, b=3, stretch="lin")
# Compute the local variability (local SD) of the first PC
PC1sd<- focal(ENpca$map$PC1, w=matrix(1/9, nrow=3, ncol=3), fun=sd)
plot(PC1sd, col=cls)
|
/R_code_no2.r
|
no_license
|
samuelepapiccio/telerilevamento_2021
|
R
| false | false | 1,504 |
r
|
# R code no2
#set working directory EN
setwd("/Users/samuelepapiccio/lab/EN/")
library(raster)
library(RStoolbox)
# Import first image (single band)
# We will select band 1, but the raster function enables to select other single band
EN01<- raster("EN_0001.png")
# Plot the first image with the preferred Color Ramp Palette
cls<- colorRampPalette(c("red","pink","orange","yellow"))(200)
plot(EN01,col=cls)
# Import the last image and plot it with the previous color ramp palette
EN13<- raster("EN_0013.png")
cls<- colorRampPalette(c("red","pink","orange","yellow"))(200)
plot(EN13,col=cls)
# Make the difference between the two images and plot it
ENdif<- EN01-EN13
plot(ENdif,col=cls)
#plot everything altoghether
par(mfrow=c(3,1))
plot(EN01,col=cls, main="NO2 in January")
plot(EN13,col=cls, main="NO2 in March")
plot(ENdif,col=cls, main="NO2 difference (January - March)")
# Import the whole set
#list of files
rlist<- list.files(pattern="EN")
rlist
import<-lapply(rlist,raster)
import
EN<- stack(import)
plot(EN, col=cls)
# Replicate the plot of images 01 and 13 using the stack
par(mfrow=c(3,1))
plot(EN$EN_0001,col=cls,main="NO2 in January")
plot(EN$EN_0013,col=cls, main="NO2 in March")
# Compute a PCA over the 13 images
ENpca<- rasterPCA(EN)
summary(ENpca$model)
dev.off()
plotRGB(ENpca$map, r=1, g=2, b=3, stretch="lin")
# Compute the local variability (local SD) of the first PC
PC1sd<- focal(ENpca$map$PC1, w=matrix(1/9, nrow=3, ncol=3), fun=sd)
plot(PC1sd, col=cls)
|
# Munge student and mailer data
student_addr<-stus %>%
select(first=first_name,
last=last_name,
grade=grade_level,
address=street,
lat=lat.x,
long=lon.x
) %>%
mutate(type="KIPPster",
cohort=NA)
mailer_addr_selected<-mailer_addr %>%
select(first,
last,
address,
lat,
long) %>%
mutate(grade=NA,
cohort=NA,
type="Postcard")
alumni_addr_selected<-alumni_addr %>%
select(first=FirstName,
last=LastName,
address=complete_address,
lat,
long,
cohort=Cohort) %>%
mutate(grade=NA,
type="Alumni")
combined_addresses<-rbind_list(student_addr, mailer_addr_selected, alumni_addr_selected) %>%
filter(!is.na(lat), !is.na(long))
# load shapefiles ####
# community areas ####
# get cummunity areas ####
community_areas_shapefile<-readOGR("shapefiles/CommAreas/",
"CommAreas")
community_areas_shapefile<-spTransform(community_areas_shapefile,
CRS("+proj=longlat +datum=WGS84"))
# prep community areas for ggplot
community_areas_shapefile@data$id <- rownames(community_areas_shapefile@data)
community_areas <- fortify(community_areas_shapefile, region="id")
community_areas.df<-merge(community_areas,
community_areas_shapefile,
by="id") %>%
arrange(id, order) %>%
as.data.frame
# add_municipalities
municipalities_shapefile<-readOGR("shapefiles/Municipalities/",
"Municipalities")
municipalities_shapefile<-spTransform(municipalities_shapefile,
CRS("+proj=longlat +datum=WGS84"))
# prep community areas for ggplot
municipalities_shapefile@data$id <- rownames(municipalities_shapefile@data)
municipalities <- fortify(municipalities_shapefile, region="id")
municipalities.df<-merge(municipalities,
municipalities_shapefile,
by="id") %>%
arrange(id, order) %>%
as.data.frame
# assign addresses to community areas to aid in subsetting
combined_sp <- combined_addresses %>% select(long, lat)%>% as.data.frame
coordinates(combined_sp) <- ~long+lat
proj4string(combined_sp)<- CRS("+proj=longlat +datum=WGS84")
cas_overlaid<-over(combined_sp, community_areas_shapefile)
combined_addresses$community_area<- cas_overlaid$COMMUNITY
|
/Student Recruiting/munge/01-A.R
|
no_license
|
kippchicago/Data_Analysis
|
R
| false | false | 2,463 |
r
|
# Munge student and mailer data
student_addr<-stus %>%
select(first=first_name,
last=last_name,
grade=grade_level,
address=street,
lat=lat.x,
long=lon.x
) %>%
mutate(type="KIPPster",
cohort=NA)
mailer_addr_selected<-mailer_addr %>%
select(first,
last,
address,
lat,
long) %>%
mutate(grade=NA,
cohort=NA,
type="Postcard")
alumni_addr_selected<-alumni_addr %>%
select(first=FirstName,
last=LastName,
address=complete_address,
lat,
long,
cohort=Cohort) %>%
mutate(grade=NA,
type="Alumni")
combined_addresses<-rbind_list(student_addr, mailer_addr_selected, alumni_addr_selected) %>%
filter(!is.na(lat), !is.na(long))
# load shapefiles ####
# community areas ####
# get cummunity areas ####
community_areas_shapefile<-readOGR("shapefiles/CommAreas/",
"CommAreas")
community_areas_shapefile<-spTransform(community_areas_shapefile,
CRS("+proj=longlat +datum=WGS84"))
# prep community areas for ggplot
community_areas_shapefile@data$id <- rownames(community_areas_shapefile@data)
community_areas <- fortify(community_areas_shapefile, region="id")
community_areas.df<-merge(community_areas,
community_areas_shapefile,
by="id") %>%
arrange(id, order) %>%
as.data.frame
# add_municipalities
municipalities_shapefile<-readOGR("shapefiles/Municipalities/",
"Municipalities")
municipalities_shapefile<-spTransform(municipalities_shapefile,
CRS("+proj=longlat +datum=WGS84"))
# prep community areas for ggplot
municipalities_shapefile@data$id <- rownames(municipalities_shapefile@data)
municipalities <- fortify(municipalities_shapefile, region="id")
municipalities.df<-merge(municipalities,
municipalities_shapefile,
by="id") %>%
arrange(id, order) %>%
as.data.frame
# assign addresses to community areas to aid in subsetting
combined_sp <- combined_addresses %>% select(long, lat)%>% as.data.frame
coordinates(combined_sp) <- ~long+lat
proj4string(combined_sp)<- CRS("+proj=longlat +datum=WGS84")
cas_overlaid<-over(combined_sp, community_areas_shapefile)
combined_addresses$community_area<- cas_overlaid$COMMUNITY
|
# plot
#' outer2
#' The outer product of the arrays `X` and `Y` is the array `A` with dimension
#' `c(dim(X), dim(Y))` where element `A[c(arrayindex.x, arrayindex.y)] =
#' FUN(X[arrayindex.x], Y[arrayindex.y], ...)`. This is a patch to the [outer]
#' function that can handle non-vectorized functions as well as vectorized ones.
#' @param X,Y First and second arguments for function `FUN.`
#' Typically a vector or array.
#' @param FUN a function to use on the outer products, found via [match.fun]
#' (except for the special case "`*`").
#' @param ... optional arguments to be passed to `FUN`.
#' @param Vectorized
#' @return A matrix
#' @seealso [outer]
#' @source https://tolstoy.newcastle.edu.au/R/help/05/10/14725.html
#' @author Tony Plate
#' @examples
outer2 <- function(X, Y, FUN = "*", ..., Vectorized = FALSE) {
no.nx <- is.null(nx <- dimnames(X <- as.array(X)))
dX <- dim(X)
no.ny <- is.null(ny <- dimnames(Y <- as.array(Y)))
dY <- dim(Y)
if (is.character(FUN) && FUN == "*") {
robj <- as.vector(X) %*% t(as.vector(Y))
dim(robj) <- c(dX, dY)
} else {
FUN <- match.fun(FUN)
Y <- rep(Y, rep.int(length(X), length(Y)))
if (length(X) > 0)
X <- rep(X, times = ceiling(length(Y)/length(X)))
if (Vectorized)
robj <- FUN(X, Y, ...)
else
robj <- mapply(FUN, X, Y, MoreArgs=list(...))
dim(robj) <- c(dX, dY)
}
if (no.nx)
nx <- vector("list", length(dX))
else if (no.ny)
ny <- vector("list", length(dY))
if (!(no.nx && no.ny))
dimnames(robj) <- c(nx, ny)
robj
}
#'plotf1
#'
#' @param f
#' @param from
#' @param to
#' @param ...
#'
#' @return Directly plots the function results
#' @export
#' @examples
plotf1 <- function(f, from = -5, to = 5, ...) {
steps <- 200
#
se <- c(from, to)
dim(se) <- c(length(from), 2)
# Create a n-dim plane
x <- apply(se, 1, function(i) seq(i[1], i[2], length.out = steps))
# Feed the plane into the function
fx <- apply(x, 1, f, ...)
# Plot the results
plot(1:length(fx), fx, type = "l")
}
# source("./R/Haystack.R")
# p <- plotf1(function(x) 10*length(x) + sum((x)^2 - 10*cos(1*pi*(x))),
# from = rep(-10,2), to = rep(10,2))
#' plotf2
#'
#' @param f
#' @param from Numeric vector with 2 positions
#' @param to Numeric vector with 2 positions
#' @param ...
#'
#' @return
#' @export
#'
#' @examples
plotf2 <- function(f, from = c(-5, -5), to = c(5, 5), ...) {
# PRECONDITIONS
require(plot3D)
steps <- 200
# Run
x <- seq(from[1], to[1], length.out = steps)
y <- seq(from[2], to[2], length.out = steps)
z <- outer2(x, y, function(a, b) f(c(a, b)), ...)
persp3D(x, y, z = z)
}
p <- plotf2(function(x) 10*length(x) + sum((x)^2 - 10*cos(1*pi*(x))))
plotfc <- function(f, from = c(-5, -5), to = c(5, 5), ...) {
# PRECONDITIONS
require(plot3D)
steps <- 200
# Run
x <- seq(from[1], to[1], length.out = steps)
y <- seq(from[2], to[2], length.out = steps)
z <- outer2(x, y, function(a, b) f(c(a, b)), ...)
contour(z = z)
}
#p <- plotfc(function(x) 10*length(x) + sum((x)^2 - 10*cos(1*pi*(x))))
plotf <- function(f, type = c("one", "two", "contour"), plane = NULL,
range = 5, center = 0,
...) {
type <- match.arg(type)
switch(type,
one = plotf1(x, ...),
two = plotf2(x, ...),
contour = plotfc(x, ...))
}
|
/R/plotf.R
|
permissive
|
SigurdJanson/OptimTestLib
|
R
| false | false | 3,368 |
r
|
# plot
#' outer2
#' The outer product of the arrays `X` and `Y` is the array `A` with dimension
#' `c(dim(X), dim(Y))` where element `A[c(arrayindex.x, arrayindex.y)] =
#' FUN(X[arrayindex.x], Y[arrayindex.y], ...)`. This is a patch to the [outer]
#' function that can handle non-vectorized functions as well as vectorized ones.
#' @param X,Y First and second arguments for function `FUN.`
#' Typically a vector or array.
#' @param FUN a function to use on the outer products, found via [match.fun]
#' (except for the special case "`*`").
#' @param ... optional arguments to be passed to `FUN`.
#' @param Vectorized
#' @return A matrix
#' @seealso [outer]
#' @source https://tolstoy.newcastle.edu.au/R/help/05/10/14725.html
#' @author Tony Plate
#' @examples
outer2 <- function(X, Y, FUN = "*", ..., Vectorized = FALSE) {
no.nx <- is.null(nx <- dimnames(X <- as.array(X)))
dX <- dim(X)
no.ny <- is.null(ny <- dimnames(Y <- as.array(Y)))
dY <- dim(Y)
if (is.character(FUN) && FUN == "*") {
robj <- as.vector(X) %*% t(as.vector(Y))
dim(robj) <- c(dX, dY)
} else {
FUN <- match.fun(FUN)
Y <- rep(Y, rep.int(length(X), length(Y)))
if (length(X) > 0)
X <- rep(X, times = ceiling(length(Y)/length(X)))
if (Vectorized)
robj <- FUN(X, Y, ...)
else
robj <- mapply(FUN, X, Y, MoreArgs=list(...))
dim(robj) <- c(dX, dY)
}
if (no.nx)
nx <- vector("list", length(dX))
else if (no.ny)
ny <- vector("list", length(dY))
if (!(no.nx && no.ny))
dimnames(robj) <- c(nx, ny)
robj
}
#'plotf1
#'
#' @param f
#' @param from
#' @param to
#' @param ...
#'
#' @return Directly plots the function results
#' @export
#' @examples
plotf1 <- function(f, from = -5, to = 5, ...) {
steps <- 200
#
se <- c(from, to)
dim(se) <- c(length(from), 2)
# Create a n-dim plane
x <- apply(se, 1, function(i) seq(i[1], i[2], length.out = steps))
# Feed the plane into the function
fx <- apply(x, 1, f, ...)
# Plot the results
plot(1:length(fx), fx, type = "l")
}
# source("./R/Haystack.R")
# p <- plotf1(function(x) 10*length(x) + sum((x)^2 - 10*cos(1*pi*(x))),
# from = rep(-10,2), to = rep(10,2))
#' plotf2
#'
#' @param f
#' @param from Numeric vector with 2 positions
#' @param to Numeric vector with 2 positions
#' @param ...
#'
#' @return
#' @export
#'
#' @examples
plotf2 <- function(f, from = c(-5, -5), to = c(5, 5), ...) {
# PRECONDITIONS
require(plot3D)
steps <- 200
# Run
x <- seq(from[1], to[1], length.out = steps)
y <- seq(from[2], to[2], length.out = steps)
z <- outer2(x, y, function(a, b) f(c(a, b)), ...)
persp3D(x, y, z = z)
}
p <- plotf2(function(x) 10*length(x) + sum((x)^2 - 10*cos(1*pi*(x))))
plotfc <- function(f, from = c(-5, -5), to = c(5, 5), ...) {
# PRECONDITIONS
require(plot3D)
steps <- 200
# Run
x <- seq(from[1], to[1], length.out = steps)
y <- seq(from[2], to[2], length.out = steps)
z <- outer2(x, y, function(a, b) f(c(a, b)), ...)
contour(z = z)
}
#p <- plotfc(function(x) 10*length(x) + sum((x)^2 - 10*cos(1*pi*(x))))
plotf <- function(f, type = c("one", "two", "contour"), plane = NULL,
range = 5, center = 0,
...) {
type <- match.arg(type)
switch(type,
one = plotf1(x, ...),
two = plotf2(x, ...),
contour = plotfc(x, ...))
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/merTools-package.r
\docType{data}
\name{hsb}
\alias{hsb}
\title{A subset of data from the 1982 High School and Beyond survey used as examples for HLM software}
\format{A data frame with 7,185 observations on the following 8 variables.
\describe{
\item{\code{schid}}{a numeric vector, 160 unique values}
\item{\code{mathach}}{a numeric vector for the performance on a standardized math assessment}
\item{\code{female}}{a numeric vector coded 0 for male and 1 for female}
\item{\code{ses}}{a numeric measure of student socio-economic status}
\item{\code{minority}}{a numeric vector coded 0 for white and 1 for non-white students}
\item{\code{schtype}}{a numeric vector coded 0 for public and 1 for private schools}
\item{\code{meanses}}{a numeric, the average SES for each school in the data set}
\item{\code{size}}{a numeric for the number of students in the school}
}}
\source{
Data made available by UCLA Institute for Digital Research and Education
(IDRE) online: \url{http://www.ats.ucla.edu/stat/hlm/seminars/hlm6/mlm_hlm6_seminar.htm}
}
\usage{
hsb
}
\description{
A key example dataset used for examples in the HLM software manual.
Included here for use in replicating HLM analyses in R.
}
\details{
The data file used for this presentation is a subsample from the
1982 High School and Beyond Survey and is used extensively in
Hierarchical Linear Models by Raudenbush and Bryk. It consists of 7,185 students
nested in 160 schools.
}
\examples{
data(hsb)
head(hsb)
}
\references{
Stephen W. Raudenbush and Anthony S. Bryk (2002). Hierarchical
Linear Models: Applications and Data Analysis Methods (2nd ed.). SAGE.
}
\keyword{datasets}
|
/man/hsb.Rd
|
no_license
|
jamesonquinn/merTools
|
R
| false | true | 1,727 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/merTools-package.r
\docType{data}
\name{hsb}
\alias{hsb}
\title{A subset of data from the 1982 High School and Beyond survey used as examples for HLM software}
\format{A data frame with 7,185 observations on the following 8 variables.
\describe{
\item{\code{schid}}{a numeric vector, 160 unique values}
\item{\code{mathach}}{a numeric vector for the performance on a standardized math assessment}
\item{\code{female}}{a numeric vector coded 0 for male and 1 for female}
\item{\code{ses}}{a numeric measure of student socio-economic status}
\item{\code{minority}}{a numeric vector coded 0 for white and 1 for non-white students}
\item{\code{schtype}}{a numeric vector coded 0 for public and 1 for private schools}
\item{\code{meanses}}{a numeric, the average SES for each school in the data set}
\item{\code{size}}{a numeric for the number of students in the school}
}}
\source{
Data made available by UCLA Institute for Digital Research and Education
(IDRE) online: \url{http://www.ats.ucla.edu/stat/hlm/seminars/hlm6/mlm_hlm6_seminar.htm}
}
\usage{
hsb
}
\description{
A key example dataset used for examples in the HLM software manual.
Included here for use in replicating HLM analyses in R.
}
\details{
The data file used for this presentation is a subsample from the
1982 High School and Beyond Survey and is used extensively in
Hierarchical Linear Models by Raudenbush and Bryk. It consists of 7,185 students
nested in 160 schools.
}
\examples{
data(hsb)
head(hsb)
}
\references{
Stephen W. Raudenbush and Anthony S. Bryk (2002). Hierarchical
Linear Models: Applications and Data Analysis Methods (2nd ed.). SAGE.
}
\keyword{datasets}
|
#Nested looping
for(i in seq(1,3)){
#for every single run in outside loop inside loop gets executed 10 times
for(j in seq(i,i+10L)){
cat(j," ")
}
}
|
/DAY - 2/Assignment/Q5 - NestedLoops.R
|
no_license
|
Sajeth-s-a/Data-Science---R-programming-
|
R
| false | false | 173 |
r
|
#Nested looping
for(i in seq(1,3)){
#for every single run in outside loop inside loop gets executed 10 times
for(j in seq(i,i+10L)){
cat(j," ")
}
}
|
# Loading Data RDS
require(ggplot2)
NEI <- readRDS("data/summarySCC_PM25.rds")
SCC <- readRDS("data/Source_Classification_Code.rds")
# Coal-related SCC
SCC.coal = SCC[grepl("coal", SCC$Short.Name, ignore.case = TRUE), ]
# Merges two data sets
merge <- merge(x = NEI, y = SCC.coal, by = 'SCC')
merge.sum <- aggregate(merge[, 'Emissions'], by = list(merge$year), sum)
colnames(merge.sum) <- c('Year', 'Emissions')
# Generating Graph
png(filename = 'plot4.png')
ggplot(data = merge.sum, aes(x = Year, y = Emissions / 1000)) + geom_line(aes(group = 1, col = Emissions)) + geom_point(aes(size = 2, col = Emissions)) + ggtitle(expression('Total Emissions of PM'[2.5])) + ylab(expression(paste('PM', ''[2.5], ' in kilotons'))) + geom_text(aes(label = round(Emissions / 1000, digits = 2), size = 2, hjust = 1.5, vjust = 1.5)) + theme(legend.position = 'none') + scale_colour_gradient(low = 'black', high = 'red')
dev.off()
|
/plot4.R
|
no_license
|
coolbhatt/Project-2
|
R
| false | false | 918 |
r
|
# Loading Data RDS
require(ggplot2)
NEI <- readRDS("data/summarySCC_PM25.rds")
SCC <- readRDS("data/Source_Classification_Code.rds")
# Coal-related SCC
SCC.coal = SCC[grepl("coal", SCC$Short.Name, ignore.case = TRUE), ]
# Merges two data sets
merge <- merge(x = NEI, y = SCC.coal, by = 'SCC')
merge.sum <- aggregate(merge[, 'Emissions'], by = list(merge$year), sum)
colnames(merge.sum) <- c('Year', 'Emissions')
# Generating Graph
png(filename = 'plot4.png')
ggplot(data = merge.sum, aes(x = Year, y = Emissions / 1000)) + geom_line(aes(group = 1, col = Emissions)) + geom_point(aes(size = 2, col = Emissions)) + ggtitle(expression('Total Emissions of PM'[2.5])) + ylab(expression(paste('PM', ''[2.5], ' in kilotons'))) + geom_text(aes(label = round(Emissions / 1000, digits = 2), size = 2, hjust = 1.5, vjust = 1.5)) + theme(legend.position = 'none') + scale_colour_gradient(low = 'black', high = 'red')
dev.off()
|
context("query_building - prep")
mysql_cred <- function(){
json_string <- Sys.getenv("DBEZR_TEST_MYSQL")
if (json_string != ""){
tryCatch({
.cred <- cred_json(json_string)
tryCatch({
disconnect_con(create_con(.cred))
},
error = function(e){
skip(
paste0("Unable to connect. ", capture.output(print(.cred)))
)
})
},
error = function(e){
skip("Malformed JSON, can't extract credentials")
})
} else{
skip("No MySQL test database provided")
}
.cred
}
pgsql_cred <- function(){
json_string <- Sys.getenv("DBEZR_TEST_POSTGRES")
if (json_string != ""){
tryCatch({
.cred <- cred_json(json_string)
tryCatch({
disconnect_con(create_con(.cred))
},
error = function(e){
skip(
paste0("Unable to connect. ", capture.output(print(.cred)))
)
})
},
error = function(e){
skip("Malformed JSON, can't extract credentials")
})
} else{
skip("No Postgres test database provided")
}
.cred
}
test_that("Character column is cleaned", {
rm_db_all()
db_cred(pgsql_cred())
# Single Value
expect_equal(prep("Bob"), "'Bob'")
# Vector
expect_equal(prep(c("Bob", "George")), c("'Bob'", "'George'"))
})
test_that("Character column is cleaned properly with NULL values", {
rm_db_all()
db_cred(pgsql_cred())
# Single Null Values
expect_equal(prep(c(NA, "George", "Fred")), c("NULL", "'George'", "'Fred'"))
expect_equal(prep(c("Bob", NA, "Fred")), c("'Bob'", "NULL", "'Fred'"))
expect_equal(prep(c("Bob", "George", NA)), c("'Bob'", "'George'", "NULL"))
# Whole Vector NULL
expect_equal(prep(c(NA, NA)), c("NULL", "NULL"))
})
test_that("Character column is cleaned for possible Injection attacks", {
rm_db_all()
db_cred(pgsql_cred())
expect_equal(prep("Robert'); DROP TABLE Students;--"),
"'Robert''); DROP TABLE Students;--'")
})
test_that("Date column is cleaned", {
today <- parse_date("2016-01-02")
today_s <- "'2016-01-02 00:00:00'"
now <- parse_date_time("2016-01-03 04:05:06")
now_s <- "'2016-01-03 04:05:06'"
# Single Value
expect_equal(prep(today), today_s)
# Vector
t_vect <- lubridate::with_tz(c(today, now), "UTC")
expect_equal(prep(t_vect), c(today_s, now_s))
})
test_that("Date column is cleaned properly with NULL values", {
today <- parse_date("2016-01-02")
today_s <- "'2016-01-02 00:00:00'"
now <- parse_date_time("2016-01-03 04:05:06")
now_s <- "'2016-01-03 04:05:06'"
# Single Null Values
first_val <- lubridate::with_tz(c(now, today, now), "UTC")
first_val[1] <- NA
secnd_val <- lubridate::with_tz(c(today, NA, now), "UTC")
third_val <- lubridate::with_tz(c(today, now, NA), "UTC")
expect_equal(prep(first_val), c("NULL", today_s, now_s))
expect_equal(prep(secnd_val), c(today_s, "NULL", now_s))
expect_equal(prep(third_val), c(today_s, now_s, "NULL"))
# Whole Vector NULL
expect_equal(prep(c(NA, NA)), c("NULL", "NULL"))
})
test_that("Numeric column is cleaned", {
# Single Value
expect_equal(prep(1), "1")
# Vector
expect_equal(prep(c(1, 2, 3)), c("1", "2", "3"))
})
test_that("Double column is cleaned", {
# Single Value
expect_equal(prep(as.double(1)), "1")
# Vector
expect_equal(prep(as.double(c(1, 2, 3))), c("1", "2", "3"))
})
test_that("Integer column is cleaned", {
# Single Value
expect_equal(prep(1L), "1")
# Vector
expect_equal(prep(c(1L, 2L, 3L)), c("1", "2", "3"))
})
test_that("Numeric column is cleaned properly with NULL values", {
# Single Null Values
expect_equal(prep(c(NA, 2, 3)), c("NULL", "2", "3"))
expect_equal(prep(c(1, NA, 3)), c("1", "NULL", "3"))
expect_equal(prep(c(1, 2, NA)), c("1", "2", "NULL"))
# Whole Vector NULL
expect_equal(prep(c(NA, NA)), c("NULL", "NULL"))
})
test_that("Logical column is cleaned", {
# Single Value
expect_equal(prep(TRUE), "1")
expect_equal(prep(FALSE), "0")
# Vector
expect_equal(prep(c(TRUE, FALSE)), c("1", "0"))
})
test_that("Logical column is cleaned properly with NULL values", {
# Single Null Values
expect_equal(prep(c(NA, FALSE)), c("NULL", "0"))
expect_equal(prep(c(TRUE, NA)), c("1", "NULL"))
# Whole Vector NULL
expect_equal(prep(c(NA, NA)), c("NULL", "NULL"))
})
|
/tests/testthat/test_prep.R
|
permissive
|
acjackman/dbezr
|
R
| false | false | 4,641 |
r
|
context("query_building - prep")
mysql_cred <- function(){
json_string <- Sys.getenv("DBEZR_TEST_MYSQL")
if (json_string != ""){
tryCatch({
.cred <- cred_json(json_string)
tryCatch({
disconnect_con(create_con(.cred))
},
error = function(e){
skip(
paste0("Unable to connect. ", capture.output(print(.cred)))
)
})
},
error = function(e){
skip("Malformed JSON, can't extract credentials")
})
} else{
skip("No MySQL test database provided")
}
.cred
}
pgsql_cred <- function(){
json_string <- Sys.getenv("DBEZR_TEST_POSTGRES")
if (json_string != ""){
tryCatch({
.cred <- cred_json(json_string)
tryCatch({
disconnect_con(create_con(.cred))
},
error = function(e){
skip(
paste0("Unable to connect. ", capture.output(print(.cred)))
)
})
},
error = function(e){
skip("Malformed JSON, can't extract credentials")
})
} else{
skip("No Postgres test database provided")
}
.cred
}
test_that("Character column is cleaned", {
rm_db_all()
db_cred(pgsql_cred())
# Single Value
expect_equal(prep("Bob"), "'Bob'")
# Vector
expect_equal(prep(c("Bob", "George")), c("'Bob'", "'George'"))
})
test_that("Character column is cleaned properly with NULL values", {
rm_db_all()
db_cred(pgsql_cred())
# Single Null Values
expect_equal(prep(c(NA, "George", "Fred")), c("NULL", "'George'", "'Fred'"))
expect_equal(prep(c("Bob", NA, "Fred")), c("'Bob'", "NULL", "'Fred'"))
expect_equal(prep(c("Bob", "George", NA)), c("'Bob'", "'George'", "NULL"))
# Whole Vector NULL
expect_equal(prep(c(NA, NA)), c("NULL", "NULL"))
})
test_that("Character column is cleaned for possible Injection attacks", {
rm_db_all()
db_cred(pgsql_cred())
expect_equal(prep("Robert'); DROP TABLE Students;--"),
"'Robert''); DROP TABLE Students;--'")
})
test_that("Date column is cleaned", {
today <- parse_date("2016-01-02")
today_s <- "'2016-01-02 00:00:00'"
now <- parse_date_time("2016-01-03 04:05:06")
now_s <- "'2016-01-03 04:05:06'"
# Single Value
expect_equal(prep(today), today_s)
# Vector
t_vect <- lubridate::with_tz(c(today, now), "UTC")
expect_equal(prep(t_vect), c(today_s, now_s))
})
test_that("Date column is cleaned properly with NULL values", {
today <- parse_date("2016-01-02")
today_s <- "'2016-01-02 00:00:00'"
now <- parse_date_time("2016-01-03 04:05:06")
now_s <- "'2016-01-03 04:05:06'"
# Single Null Values
first_val <- lubridate::with_tz(c(now, today, now), "UTC")
first_val[1] <- NA
secnd_val <- lubridate::with_tz(c(today, NA, now), "UTC")
third_val <- lubridate::with_tz(c(today, now, NA), "UTC")
expect_equal(prep(first_val), c("NULL", today_s, now_s))
expect_equal(prep(secnd_val), c(today_s, "NULL", now_s))
expect_equal(prep(third_val), c(today_s, now_s, "NULL"))
# Whole Vector NULL
expect_equal(prep(c(NA, NA)), c("NULL", "NULL"))
})
test_that("Numeric column is cleaned", {
# Single Value
expect_equal(prep(1), "1")
# Vector
expect_equal(prep(c(1, 2, 3)), c("1", "2", "3"))
})
test_that("Double column is cleaned", {
# Single Value
expect_equal(prep(as.double(1)), "1")
# Vector
expect_equal(prep(as.double(c(1, 2, 3))), c("1", "2", "3"))
})
test_that("Integer column is cleaned", {
# Single Value
expect_equal(prep(1L), "1")
# Vector
expect_equal(prep(c(1L, 2L, 3L)), c("1", "2", "3"))
})
test_that("Numeric column is cleaned properly with NULL values", {
# Single Null Values
expect_equal(prep(c(NA, 2, 3)), c("NULL", "2", "3"))
expect_equal(prep(c(1, NA, 3)), c("1", "NULL", "3"))
expect_equal(prep(c(1, 2, NA)), c("1", "2", "NULL"))
# Whole Vector NULL
expect_equal(prep(c(NA, NA)), c("NULL", "NULL"))
})
test_that("Logical column is cleaned", {
# Single Value
expect_equal(prep(TRUE), "1")
expect_equal(prep(FALSE), "0")
# Vector
expect_equal(prep(c(TRUE, FALSE)), c("1", "0"))
})
test_that("Logical column is cleaned properly with NULL values", {
# Single Null Values
expect_equal(prep(c(NA, FALSE)), c("NULL", "0"))
expect_equal(prep(c(TRUE, NA)), c("1", "NULL"))
# Whole Vector NULL
expect_equal(prep(c(NA, NA)), c("NULL", "NULL"))
})
|
# Server Output Functions for Visualize Tab
#### Main Tab Output Functions ####
# Controls for the whole set of tabs
output$Visualize_Tab_Controls_1 <- renderUI({
selectizeInput(
"VisualizeEventMapInputID",
label = h4("Choose POV:"),
get_POV_names()
)
})
output$Visualize_Tab_Controls_2 <- renderUI({
req(input$VisualizeEventMapInputID)
zoom_limit = zoom_upper_limit(get_POV(input$VisualizeEventMapInputID))
if(zoom_limit == 1) {
tags$h4("Zooming not available for this POV")
} else {
tags$div(
sliderInput(
"VisualizeTabZoomID",
label = h4("Zoom in and out by event similarity:"),
1, zoom_limit, zoom_limit, step = 1, ticks = FALSE
),
helpText('Zooming does not apply to some visualizations.'))
}
})
output$Visualize_Tab_Controls_3 = renderUI({
nThreads = numThreads(threadedEventsViz_ALL(),'threadNum')
sliderInput("VisualizeRangeID",
label=h4("Range of threads to include:"),
1, nThreads, c(1,nThreads),step = 1, ticks=FALSE)
})
#### N-Grams sub-tab ####
# controls for ngrams display
output$nGramControls <- renderUI({
tagList(
sliderInput("nGramLengthID","nGram Size", 1,10,2,step = 1,ticks = FALSE),
sliderInput("nGramDisplayThresholdID","Display threshold", 1,50,1,step = 1,ticks = FALSE),
radioButtons(
"Label_or_Zoom_3",
"Do you prefer:",
choices = c('Labels','Zooming'),
selected = 'Labels',
inline = TRUE)
)
})
# NGRAMdisplay
output$nGramBarchart <- renderPlotly({
req(input$nGramLengthID)
if (input$Label_or_Zoom_3 == 'Labels')
ng_bar_chart(
threadedEventsViz(),
"threadNum",
'label',
input$nGramLengthID,
input$nGramDisplayThresholdID)
else
ng_bar_chart(
threadedEventsViz(),
"threadNum",
get_Zoom_VIZ(),
input$nGramLengthID,
input$nGramDisplayThresholdID)
})
#### Whole Sequences sub-tab ####
# Whole sequence display -- allow alternatives
output$WholeSequenceThreadMap_Sequence <- renderPlotly({ threadMap(threadedEventsViz(), "threadNum", "seqNum", get_Zoom_VIZ(), 15)})
output$WholeSequenceThreadMap_ActualTime <- renderPlotly({ threadMap(threadedEventsViz(), "threadNum", "tStamp", get_Zoom_VIZ(), 15)})
output$WholeSequenceThreadMap_RelativeTime <- renderPlotly({ threadMap(threadedEventsViz(), "threadNum", "relativeTime", get_Zoom_VIZ(), 15)})
#### Event Network (circle) sub-tab ####
# use this to select how to color the nodes in force layout
output$Circle_Network_Tab_Controls <- renderUI({
tags$div(
downloadButton('downloadNetwork', 'Export this Network', class="dlButton"),
sliderInput("circleEdgeTheshold","Display edges above", 0,1,0,step = 0.01,ticks = FALSE ),
radioButtons(
"Label_or_Zoom_1",
"Do you prefer:",
choices = c('Labels','Zooming'),
selected = 'Labels',
inline = TRUE)
)
})
# Create the network to be exported and also displayed
viz_net <<- reactive({
req(input$circleEdgeTheshold)
# first convert the threads to the network
if (input$Label_or_Zoom_1 == 'Labels')
{ n <- threads_to_network_original(threadedEventsViz(), "threadNum", 'label') }
else
{ n <- threads_to_network_original(threadedEventsViz(), "threadNum", get_Zoom_VIZ()) }
# filter out the edges if desired
n <- filter_network_edges(n,input$circleEdgeTheshold)
n
})
output$circleVisNetwork <- renderVisNetwork({
req(input$circleEdgeTheshold)
circleVisNetwork(viz_net(), 'directed', TRUE)
})
# output$circleVisNetwork <- renderVisNetwork({
# req(input$circleEdgeTheshold)
#
# # first convert the threads to the network
# if (input$Label_or_Zoom_1 == 'Labels')
# { n <- threads_to_network_original(threadedEventsViz(), "threadNum", 'label') }
# else
# { n <- threads_to_network_original(threadedEventsViz(), "threadNum", get_Zoom_VIZ()) }
#
# # filter out the edges if desired
# n <- filter_network_edges(n,input$circleEdgeTheshold)
# circleVisNetwork(n, 'directed', TRUE)
# })
#### Other Networks sub-tab ####
# use this to select how to color the nodes in force layout
output$Other_Network_Tab_Controls <- renderUI({
button_choices <- get_POV_EVENT_CF( input$VisualizeEventMapInputID )
tags$div(
radioButtons(
"OtherNetworkCF",
"Graph co-occurrence relation between:",
choices = button_choices,
selected = button_choices[1], # always start with the first one
inline = TRUE
),
sliderInput("otherEdgeTheshold","Display edges above",0,1,0,step = 0.01,ticks = FALSE)
)
})
output$otherVisNetwork <- renderVisNetwork({
req(input$otherEdgeTheshold)
# first convert the threads to the network
n <- normalNetwork(threadedEventsViz(), selectOccFilter(), input$OtherNetworkCF)
n <- filter_network_edges(n,input$otherEdgeTheshold)
circleVisNetwork(n, 'nondirected')
})
#### Event Network (force) sub-tab ####
# use this to select how to color the nodes in force layout
output$Force_Network_Tab_Controls <- renderUI({
button_choices <- intersect(colnames(threadedEventsViz()), cfnames(selectOccFilter()))
tags$div(
radioButtons(
"NetworkGroupID",
"Select a dimension for coloring nodes:",
choices = button_choices,
selected = button_choices[1], # always start with the first one
inline=TRUE
),
sliderInput("forceEdgeTheshold","Display edges above",0,1,0,step = 0.01,ticks = FALSE),
radioButtons(
"Label_or_Zoom_2",
"Do you prefer:",
choices = c('Labels','Zooming'),
selected = 'Labels',
inline = TRUE)
)
})
output$forceNetworkD3 <- renderForceNetwork({
req(input$forceEdgeTheshold)
if (input$Label_or_Zoom_2 == 'Labels')
{ n <- threads_to_network_original(threadedEventsViz(), 'threadNum', 'label', input$NetworkGroupID) }
else
{ n <- threads_to_network_original(threadedEventsViz(), 'threadNum', get_Zoom_VIZ(), input$NetworkGroupID) }
n <- filter_network_edges(n,input$forceEdgeTheshold)
forceNetworkD3(n)
})
output$networkPie <- renderPlotly({
req(input$Group)
get_group <- input$Group
CF_multi_pie_event(threadedOcc(), threadedEventsViz(), get_POV_EVENT_CF( input$VisualizeEventMapInputID ), get_group, get_Zoom_VIZ())
})
#### View Events sub-tab ####
output$VisualizeCustomNetwork_Controls_0 <- renderUI({
radioButtons("Timesplit2", "Time Measure:", choices = c('seqNum'='seqNum','timeGap'='timeGap'), selected = "seqNum", inline = TRUE)
})
output$VisualizeCustomNetwork_Controls_1 <- renderUI({
selectizeInput("Event",label = "Choose Event:",get_POV_EVENT_CF( input$VisualizeEventMapInputID ) )
})
output$VisualizeCustomNetwork <- renderPlotly({
req(input$Timesplit2, input$Event)
eventNetwork(threadedEventsViz(), "threadNum", input$Event, input$Timesplit2)
})
output$hover <- renderPrint({
d <- event_data("plotly_hover")
if (is.null(d)) "Hover events appear here (unhover to clear)" else d
})
output$click <- renderPrint({
d <- event_data("plotly_click")
if (is.null(d)) "Click events appear here" else d
})
#### Role Maps sub-tab ####
output$Role_map_controls <- renderUI({checkboxGroupInput("Role_map_CFs","Pick Two:", get_POV_EVENT_CF( input$VisualizeEventMapInputID ) )})
output$Role_map_output <- renderPlotly({role_map(threadedEventsViz(), selectOccFilter(), input$Role_map_CFs)})
#### Thread Trajectories sub-tab ####
output$ThreadTrajectoriesOutput <- renderPlotly({threadTrajectory(threadedEventsViz())})
##### pie chart display #####
output$visualizePieCharts = renderPlotly({
CF_multi_pie(threadedEventsViz(), get_POV_EVENT_CF( input$VisualizeEventMapInputID ) )
})
# provide a data table view, as well
output$visualizePOVData <-DT::renderDataTable(
threadedEventsViz(),
options = list(autoWidth = TRUE))
|
/Inst/ThreadNet/server/visualize.R
|
no_license
|
noelcarroll1/ThreadNet
|
R
| false | false | 7,594 |
r
|
# Server Output Functions for Visualize Tab
#### Main Tab Output Functions ####
# Controls for the whole set of tabs
output$Visualize_Tab_Controls_1 <- renderUI({
selectizeInput(
"VisualizeEventMapInputID",
label = h4("Choose POV:"),
get_POV_names()
)
})
output$Visualize_Tab_Controls_2 <- renderUI({
req(input$VisualizeEventMapInputID)
zoom_limit = zoom_upper_limit(get_POV(input$VisualizeEventMapInputID))
if(zoom_limit == 1) {
tags$h4("Zooming not available for this POV")
} else {
tags$div(
sliderInput(
"VisualizeTabZoomID",
label = h4("Zoom in and out by event similarity:"),
1, zoom_limit, zoom_limit, step = 1, ticks = FALSE
),
helpText('Zooming does not apply to some visualizations.'))
}
})
output$Visualize_Tab_Controls_3 = renderUI({
nThreads = numThreads(threadedEventsViz_ALL(),'threadNum')
sliderInput("VisualizeRangeID",
label=h4("Range of threads to include:"),
1, nThreads, c(1,nThreads),step = 1, ticks=FALSE)
})
#### N-Grams sub-tab ####
# controls for ngrams display
output$nGramControls <- renderUI({
tagList(
sliderInput("nGramLengthID","nGram Size", 1,10,2,step = 1,ticks = FALSE),
sliderInput("nGramDisplayThresholdID","Display threshold", 1,50,1,step = 1,ticks = FALSE),
radioButtons(
"Label_or_Zoom_3",
"Do you prefer:",
choices = c('Labels','Zooming'),
selected = 'Labels',
inline = TRUE)
)
})
# NGRAMdisplay
output$nGramBarchart <- renderPlotly({
req(input$nGramLengthID)
if (input$Label_or_Zoom_3 == 'Labels')
ng_bar_chart(
threadedEventsViz(),
"threadNum",
'label',
input$nGramLengthID,
input$nGramDisplayThresholdID)
else
ng_bar_chart(
threadedEventsViz(),
"threadNum",
get_Zoom_VIZ(),
input$nGramLengthID,
input$nGramDisplayThresholdID)
})
#### Whole Sequences sub-tab ####
# Whole sequence display -- allow alternatives
output$WholeSequenceThreadMap_Sequence <- renderPlotly({ threadMap(threadedEventsViz(), "threadNum", "seqNum", get_Zoom_VIZ(), 15)})
output$WholeSequenceThreadMap_ActualTime <- renderPlotly({ threadMap(threadedEventsViz(), "threadNum", "tStamp", get_Zoom_VIZ(), 15)})
output$WholeSequenceThreadMap_RelativeTime <- renderPlotly({ threadMap(threadedEventsViz(), "threadNum", "relativeTime", get_Zoom_VIZ(), 15)})
#### Event Network (circle) sub-tab ####
# use this to select how to color the nodes in force layout
output$Circle_Network_Tab_Controls <- renderUI({
tags$div(
downloadButton('downloadNetwork', 'Export this Network', class="dlButton"),
sliderInput("circleEdgeTheshold","Display edges above", 0,1,0,step = 0.01,ticks = FALSE ),
radioButtons(
"Label_or_Zoom_1",
"Do you prefer:",
choices = c('Labels','Zooming'),
selected = 'Labels',
inline = TRUE)
)
})
# Create the network to be exported and also displayed
viz_net <<- reactive({
req(input$circleEdgeTheshold)
# first convert the threads to the network
if (input$Label_or_Zoom_1 == 'Labels')
{ n <- threads_to_network_original(threadedEventsViz(), "threadNum", 'label') }
else
{ n <- threads_to_network_original(threadedEventsViz(), "threadNum", get_Zoom_VIZ()) }
# filter out the edges if desired
n <- filter_network_edges(n,input$circleEdgeTheshold)
n
})
output$circleVisNetwork <- renderVisNetwork({
req(input$circleEdgeTheshold)
circleVisNetwork(viz_net(), 'directed', TRUE)
})
# output$circleVisNetwork <- renderVisNetwork({
# req(input$circleEdgeTheshold)
#
# # first convert the threads to the network
# if (input$Label_or_Zoom_1 == 'Labels')
# { n <- threads_to_network_original(threadedEventsViz(), "threadNum", 'label') }
# else
# { n <- threads_to_network_original(threadedEventsViz(), "threadNum", get_Zoom_VIZ()) }
#
# # filter out the edges if desired
# n <- filter_network_edges(n,input$circleEdgeTheshold)
# circleVisNetwork(n, 'directed', TRUE)
# })
#### Other Networks sub-tab ####
# use this to select how to color the nodes in force layout
output$Other_Network_Tab_Controls <- renderUI({
button_choices <- get_POV_EVENT_CF( input$VisualizeEventMapInputID )
tags$div(
radioButtons(
"OtherNetworkCF",
"Graph co-occurrence relation between:",
choices = button_choices,
selected = button_choices[1], # always start with the first one
inline = TRUE
),
sliderInput("otherEdgeTheshold","Display edges above",0,1,0,step = 0.01,ticks = FALSE)
)
})
output$otherVisNetwork <- renderVisNetwork({
req(input$otherEdgeTheshold)
# first convert the threads to the network
n <- normalNetwork(threadedEventsViz(), selectOccFilter(), input$OtherNetworkCF)
n <- filter_network_edges(n,input$otherEdgeTheshold)
circleVisNetwork(n, 'nondirected')
})
#### Event Network (force) sub-tab ####
# use this to select how to color the nodes in force layout
output$Force_Network_Tab_Controls <- renderUI({
button_choices <- intersect(colnames(threadedEventsViz()), cfnames(selectOccFilter()))
tags$div(
radioButtons(
"NetworkGroupID",
"Select a dimension for coloring nodes:",
choices = button_choices,
selected = button_choices[1], # always start with the first one
inline=TRUE
),
sliderInput("forceEdgeTheshold","Display edges above",0,1,0,step = 0.01,ticks = FALSE),
radioButtons(
"Label_or_Zoom_2",
"Do you prefer:",
choices = c('Labels','Zooming'),
selected = 'Labels',
inline = TRUE)
)
})
output$forceNetworkD3 <- renderForceNetwork({
req(input$forceEdgeTheshold)
if (input$Label_or_Zoom_2 == 'Labels')
{ n <- threads_to_network_original(threadedEventsViz(), 'threadNum', 'label', input$NetworkGroupID) }
else
{ n <- threads_to_network_original(threadedEventsViz(), 'threadNum', get_Zoom_VIZ(), input$NetworkGroupID) }
n <- filter_network_edges(n,input$forceEdgeTheshold)
forceNetworkD3(n)
})
output$networkPie <- renderPlotly({
req(input$Group)
get_group <- input$Group
CF_multi_pie_event(threadedOcc(), threadedEventsViz(), get_POV_EVENT_CF( input$VisualizeEventMapInputID ), get_group, get_Zoom_VIZ())
})
#### View Events sub-tab ####
output$VisualizeCustomNetwork_Controls_0 <- renderUI({
radioButtons("Timesplit2", "Time Measure:", choices = c('seqNum'='seqNum','timeGap'='timeGap'), selected = "seqNum", inline = TRUE)
})
output$VisualizeCustomNetwork_Controls_1 <- renderUI({
selectizeInput("Event",label = "Choose Event:",get_POV_EVENT_CF( input$VisualizeEventMapInputID ) )
})
output$VisualizeCustomNetwork <- renderPlotly({
req(input$Timesplit2, input$Event)
eventNetwork(threadedEventsViz(), "threadNum", input$Event, input$Timesplit2)
})
output$hover <- renderPrint({
d <- event_data("plotly_hover")
if (is.null(d)) "Hover events appear here (unhover to clear)" else d
})
output$click <- renderPrint({
d <- event_data("plotly_click")
if (is.null(d)) "Click events appear here" else d
})
#### Role Maps sub-tab ####
output$Role_map_controls <- renderUI({checkboxGroupInput("Role_map_CFs","Pick Two:", get_POV_EVENT_CF( input$VisualizeEventMapInputID ) )})
output$Role_map_output <- renderPlotly({role_map(threadedEventsViz(), selectOccFilter(), input$Role_map_CFs)})
#### Thread Trajectories sub-tab ####
output$ThreadTrajectoriesOutput <- renderPlotly({threadTrajectory(threadedEventsViz())})
##### pie chart display #####
output$visualizePieCharts = renderPlotly({
CF_multi_pie(threadedEventsViz(), get_POV_EVENT_CF( input$VisualizeEventMapInputID ) )
})
# provide a data table view, as well
output$visualizePOVData <-DT::renderDataTable(
threadedEventsViz(),
options = list(autoWidth = TRUE))
|
library(shiny)
library(r2d3)
ui <- fluidPage(
inputPanel(
shiny::selectInput("category", label = "Category:",
choices = c("A","B"), selected = "A")
),
d3Output("d3")
)
server <- function(input, output) {
output$d3 <- renderD3({
test_data <- data.frame(colour = c("red","red","blue","blue"), category = c("A","B","A","B"), number = c(1,2,3,4))
r2d3(
test_data[test_data$category==input$category,],
script = "bars.js",
options = list(x = 'colour', y = 'number')
)
})
}
shinyApp(ui = ui, server = server)
|
/app.R
|
no_license
|
willpratt/r2d3practice
|
R
| false | false | 564 |
r
|
library(shiny)
library(r2d3)
ui <- fluidPage(
inputPanel(
shiny::selectInput("category", label = "Category:",
choices = c("A","B"), selected = "A")
),
d3Output("d3")
)
server <- function(input, output) {
output$d3 <- renderD3({
test_data <- data.frame(colour = c("red","red","blue","blue"), category = c("A","B","A","B"), number = c(1,2,3,4))
r2d3(
test_data[test_data$category==input$category,],
script = "bars.js",
options = list(x = 'colour', y = 'number')
)
})
}
shinyApp(ui = ui, server = server)
|
## ━━━━━━━━━━━━━━━━━━━
## INTRODUCTION TO R
## Ista Zahn
## ━━━━━━━━━━━━━━━━━━━
## Table of Contents
## ─────────────────
## Workshop Materials and Introduction
## Graphical User Interfaces
## Data and Functions
## R packages
## Getting data into R
## Asking R for help
## Data Manipulation
## Basic Statistics and Graphs
## Wrap-up
## Exercise solutions
## Workshop Materials and Introduction
## ═══════════════════════════════════
## Materials and setup
## ───────────────────
## Lab computer users: Log in using the user name and password on the
## board to your left.
## Laptop users: You should have R installed –if not:
## • Open a web browser and go to [http://cran.r-project.org] and
## download and install it
## • Also helpful to install RStudio (download from [http://rstudio.com])
## Everyone: Download workshop materials:
## • Download materials from
## [http://tutorials.iq.harvard.edu/R/Rintro.zip]
## • Extract the zip file containing the materials to your desktop
## What is R?
## ──────────
## R is a /programming language designed for statistical computing/.
## Notable characteristics include:
## • Vast capabilities, wide range of statistical and graphical
## techniques
## • Very popular in academia, growing popularity in business:
## [http://r4stats.com/articles/popularity/]
## • Written primarily by statisticians
## • FREE (no cost, open source)
## • Excellent community support: mailing list, blogs, tutorials
## • Easy to extend by writing new functions
## InspiRation
## ───────────
## OK, it's free and popular, but what makes R worth learning? In a word,
## "packages". If you have a data manipulation, analysis or visualization
## task, chances are good that there is an R package for that. For
## example:
## • Want to interactively explore the shape of the Churyumov–Gerasimenko
## comet?
library(rgl)
open3d()
comet <- readOBJ(url("http://sci.esa.int/science-e/www/object/doc.cfm?fobjectid=54726"))
shade3d(comet, col="gray")
writeWebGL(dir="images", filename = "images/comet.html")
## • Want to find out where we are?
library(ggmap)
nwbuilding <- geocode("52 Oxford Street, Cambridge, MA")
ggmap(get_map("Cambridge, MA", zoom = 15)) +
geom_point(data=nwbuilding, size = 7, shape = 13, color = "red")
## • Want to forecast the population of Australia?
library(forecast)
fit <- auto.arima(austres)
## Projected numbers (in thousands) of Australian residents
plot(forecast(fit))
## Whatever you're trying to do, you're probably not the first to try
## doing it R. Chances are good that someone has already written a
## package for that.
## Coming to R
## ───────────
## Coming from…
## Stata: [http://www.princeton.edu/~otorres/RStata.pdf]
## SAS/SPSS: [http://www.et.bs.ehu.es/~etptupaf/pub/R/RforSAS&SPSSusers.pdf]
## matlab: [http://www.math.umaine.edu/~hiebeler/comp/matlabR.pdf]
## Python: [http://mathesaurus.sourceforge.net/matlab-python-xref.pdf]
## Graphical User Interfaces
## ═════════════════════════
## R GUI alternatives (no GUI)
## ───────────────────────────
## The old-school way is to run R directly in a terminal
## But hardly anybody does it that way anymore!
## R GUI alternatives (Windows default)
## ────────────────────────────────────
## The default windows GUI is not very good
## • No parentheses matching or syntax highlighting
## • No work-space browser
## R GUI Alternatives (Rstudio on Mac)
## ───────────────────────────────────
## Rstudio has many useful features, including parentheses matching and
## auto-completion
## R GUI Alternatives (Emacs with ESS)
## ───────────────────────────────────
## Emacs + ESS is a very powerful combination, but can be difficult to
## set up
## Launch RStudio :labsetup:
## ──────────────
## • Open the RStudio program
## • Open up today's R script
## • In RStudio, Go to *File => Open Script*
## • Locate and open the `Rintro.R' script in the Rintro folder on your
## desktop
## • Go to *Tools => Set working directory => To source file location*
## (more on the working directory later)
## • I encourage you to add your own notes to this file! Every line that
## starts with `#' is a comment that will be ignored by R. My comments
## all start with `##'; you can add your own, possibly using `#' or
## `###' to distinguish your comments from mine.
## Exercise 0
## ──────────
## The purpose of this exercise is mostly to give you an opportunity to
## explore the interface provided by RStudio (or whichever GUI you've
## decided to use). You may not know how to do these things; that's fine!
## This is an opportunity to learn. If you don't know how to do something
## you can can use internet search engines, search on [StackOverflow], or
## ask the person next to you.
## Also keep in mind that we are living in a golden age of tab
## completion. If you don't know the name of an R function, try guessing
## the first two or three letters and pressing TAB. If you guessed
## correctly the function you are looking for should appear in a pop up!
## 1. Try to get R to add 2 plus 2.
## 2. Try to calculate the square root of 10.
## 3. There is an R package named `car' (Companion to Applied
## Regression). Try to install this package.
## 4. R includes extensive documentation, including a file named "An
## introduction to R". Try to find this help file.
## 5. Go to [http://cran.r-project.org/web/views/] and skim the topic
## closest to your field/interests.
## [StackOverflow] http:stackoverflow.com
## Data and Functions
## ══════════════════
## Assignment
## ──────────
## Values can be assigned names and used in subsequent operations
## • The `<-' operator (less than followed by a dash) is used to save
## values
## • The name on the left gets the value on the right.
x <- 10 # Assign the value 10 to a variable named x
x + 1 # Add 1 to x
y <- x + 1 # Assign y the value x + 1
y
## Saved variables can be listed, overwritten and deleted
ls() # List variables in workspace
x # Print the value of x
x <- 100 # Overwrite x. Note that no warning is given!
x
rm(x) # Delete x
ls()
## Functions
## ─────────
## Using R is mostly about applying *functions* to *variables*. Functions
## • take *variable(s)* as input *argument(s)*
## • perform operations
## • *return* values which can be *assigned*
## • optionally perform side-effects such as writing a file to disk or
## opening a graphics window
## The general form for calling R functions is `FunctionName(arg.1,
## arg.2, ... arg.n)'
## Arguments can be matched by position or name
## Examples:
#?sqrt
a <- sqrt(y) # Call the sqrt function with argument x=y
round(a, digits = 2) # Call round() with arguments x=x and digits=2
# Functions can be nested so an alternative is
round(sqrt(y), digits = 5) # Take sqrt of a and round
## R packages
## ══════════
## There are thousands of R packages that extend R's capabilities.
## • To see what packages are loaded:
## `search()'
## • To view available packages:
## `library()'
## • To load a package:
## `library("car")'
## • Install new package:
## `install.packages("stringdist")'
## Getting data into R
## ═══════════════════
## The gss dataset
## ───────────────
## The next few examples use a subset of the General Social Survey data
## set. The variables in this subset include
head(read.csv("dataSets/gssInfo.csv"))
#see gssInfo.csv for rest of the variable descriptions
## The "working directory" and listing files
## ─────────────────────────────────────────
## R knows the directory it was started in, and refers to this as the
## "working directory". Since our workshop examples are in the Rintro
## folder on the desktop, we should all take a moment to set that as our
## working directory:
setwd("~/Desktop/Rintro")
## We can also set the working directory using paths relative to the
## current working directory:
getwd() # get the current working directory
setwd("dataSets") # set wd to the dataSets folder
getwd()
setwd("..") # set wd to enclosing folder ("up")
## It can be convenient to list files in a directory without leaving R
list.files("dataSets") # list files in the dataSets folder
# list.files("dataSets", pattern = ".csv") # restrict to .csv files
## Importing data from files the easy way
## ──────────────────────────────────────
## In order to read data from a file, you usually have to know what kind
## of file it is. The table below lists some common data types.
## ━━━━━━━━━━━━━━━━━━━━━━━━
## Common data types
## ────────────────────────
## comma separated (.csv)
## Stata (.dta)
## SPSS (.sav)
## SAS (.sas7bdat)
## Excel (.xls, .xlsx)
## ━━━━━━━━━━━━━━━━━━━━━━━━
## R is smart enough to recognize most common file formats for us using
## the `import()' function. To use this functionality we first need to
## install and attache the `rio' package.
## install and load the rio package
# install.packages("rio")
library(rio)
## import data from a variety of formats
# read gss data from the gss.rds R file
datGSS <- import("dataSets/gss.rds")
# read gss data from the gss.csv comma separated file
gss.data <- import("dataSets/gss.csv")
# read a Stata dataset from gss.dta
datGSS <- import("dataSets/gss.dta")
## Importing data from files the hard way
## ──────────────────────────────────────
## In order to read data from a file, you have to know what kind of file
## it is. The table below lists the functions needed to import data from
## common file formats.
## ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## data type function package
## ──────────────────────────────────────────────────────────────────────
## comma separated (.csv) read.csv() utils (default)
## other delimited formats read.table() utils (default)
## Stata version 7-12 (.dta) read.dta() foreign
## Stata version 13-14 (.dta) read_dta() haven
## SPSS (.sav) read.spss() foreign
## SAS (.sas7bdat) read.sas7bdat() sas7bdat
## Excel (.xls, .xlsx) readWorksheetFromFile() XLConnect
## ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## Examples:
# read gss data from the gss.rds R file
datGSS <- readRDS("dataSets/gss.rds")
# read gss data from the gss.csv comma separated file
gss.data <- read.csv("dataSets/gss.csv") # read gss data
# read a Stata dataset from gss.dta
library(foreign) # load foreign data functions
datGSS <- read.dta(file="dataSets/gss.dta")
## Checking imported data
## ──────────────────────
## Always a good idea to examine the imported data set–usually we want
## the results to be a `data.frame'
class(datGSS) # check to see that test is what we expect it to be
dim(datGSS) # how many rows and columns?
names(datGSS) # or colnames(datGSS)
str(datGSS[1:10]) # more details about the first 10 columns
## Saving and loading R workspaces
## ────────────────────────────────
## In addition to importing individual datasets, R can save and load
## entire workspaces
## • Save our entire workspace
ls() # list objects in our workspace
save.image(file="myWorkspace.RData") # save workspace
rm(list=ls()) # remove all objects from our workspace
ls() # list stored objects to make sure they are deleted
## • Load the "myWorkspace.RData" file and check that it is restored
load("myWorkspace.RData") # load myWorkspace.RData
ls() # list objects
## When you close R you will be asked if you want to save your workspace
## – if you choose yes then your workspace will be restored next time you
## start R
## Exercise 1
## ──────────
## 1. Install and attach the `rio' package if you haven't already done so
## 2. Read the SPSS data set in dataSets/gss.sav and assign the result to
## an R data object named GSS.sav
## 3. Make sure that the data loaded in step 2 is a data.frame
## 4. Display the dimensions of the GSS.sav
## 5. BONUS: figure out how to read the Excel file "gss.xlsx" into R
## Asking R for help
## ═════════════════
## R has extensive built-in documentation that can be accessed through R
## commands or through the GUI.
## • Start html help, search/browse using web browser
## • at the R console:
## `help.start()'
## • or use the help menu from you GUI
## • Look up the documentation for a function
## `help(plot)'
## `?kmeans'
## • Look up documentation for a package
## `help(package="stats")'
## • Search documentation from R (not always the best way… google often
## works better)
## `help.search("classification")'
## Data Manipulation
## ═════════════════
## data.frame objects
## ──────────────────
## • Usually data read into R will be stored as a *data.frame*
## • A data.frame is a list of vectors of equal length
## • Each vector in the list forms a column
## • Each column can be a differnt type of vector
## • Often the columns are variables and the rows are observations
## • A data.frame has two dimensions corresponding the number of rows and
## the number of columns (in that order)
## Extracting subsets of data.frames
## ─────────────────────────────────
## You can extract subsets of data.frames using the `subset()'
## function[1]. Use the `select' argument to select columns:
# selecting specifig columns
head(# first n rows
subset(datGSS,
select = 1:4 # column 1 to 5
),
n = 10# show first 10 rows
)
## and the `subset' argument to select rows:
subset(datGSS,
# rows where age > 90
subset = age > 90,
## sex and age columns
select = c("sex", "age"))
## the $ operator can be used to extract a single column
str(datGSS$age)
## In the previous example we used `>' to select rows where age was
## greater than 90. Other relational and logical operators are listed
## below.
## ==: equal to
## !=: not equal to
## >: greater than
## <: less than
## >=: greater than or equal to
## <=: less than or equal to
## &: and
## |: or
## Transforming data.frames
## ────────────────────────
## You can modify data.frames using the `transform()' function.
# creating new variable mean centered age
datGSS <- transform(datGSS,
ageC = age - mean(age))
#education difference between wifes and husbands
datGSS <- transform(datGSS,
educ.diff = wifeduc - husbeduc)
datGSS$wifeduc_comp <- ifelse(
is.na(datGSS$wifeduc), #condition
mean(datGSS$wifeduc, na.rm=TRUE), # value if condition met
datGSS$wifeduc) # value otherwise
## examine our newly created variables
head(subset(datGSS,
select = c("age", "ageC", "wifeduc", "wifeduc_comp",
"husbeduc", "educ.diff")), n = 8)
## Note that `transform' is a convenience function; see `?Extract' for a
## more powerful way to modify data.frames.
## Exporting Data
## ──────────────
## Now that we have made some changes to our GSS data set, we might want
## to save those changes to a file. Everything we have done so far has
## only modified the data in R; the files have remained unchanged.
# write data to a .csv file
write.csv(datGSS, file = "gss.csv")
# write data to a Stata file
write.dta(datGSS, file = "gss.dta")
# write data to an R file
saveRDS(datGSS, file = "gss.rds")
## Exercise 2: Data manipulation
## ─────────────────────────────
## Use the gss.rds data set
## 1. Generate the following variables:
## • "rich" equal to 0 if rincdol is less than 100000, and 1 otherwise
## • "sinc" equal to incomdol - rincdol
## 2. Create a subset of the data containing only rows where "usecomp" =
## "Yes"
## 3. Examine the data.frame created in step 2, and answer the following
## questions:
## • How many rows does it have?
## • How many columns does it have?
## • What is the class of the "satjob" variable?
## 4. BONUS (hard): Generate a variable named "dual.earn" equal to 1 if
## both wkftwife = 1 and wkfthusb = 1, and zero otherwise
## Basic Statistics and Graphs
## ═══════════════════════════
## Basic statistics
## ────────────────
## Descriptive statistics of single variables are straightforward:
mean(datGSS$educ) # calculate mean value of education
sd(datGSS$educ) # calculate standard deviation of x
# calculate min, max, quantiles, mean of educ, age, and ageC
summary(subset(datGSS, select = c("educ", "age", "ageC")))
## Some of these functions (e.g., summary) will also work with
## data.frames and other types of objects, others (such as `sd') will
## not.
## Counts and proportions
## ──────────────────────
## Start by using the `table()' function to tabulate counts, then perform
## additional computations if needed
sex.counts <- table(datGSS$sex) # tabulate sex categories
sex.counts
prop.table(sex.counts) # convert to proportions
## Add variables for crosstabs
table(subset(datGSS, select = c("sex", "happy"))) # crosstab marital X happy
## Statistics by classification factors
## ────────────────────────────────────
## The `by()' function can be used to perform a calculation separately
## for each level of a classifying variable
by(subset(datGSS, select = c("income", "educ")),
INDICES=datGSS["sex"],
FUN=summary)
## Correlations
## ────────────
## Let's look at correlations among between age, income, and education
cor(subset(datGSS, select = c("age", "incomdol", "educ")))
## For significance tests, use cor.test()
with(datGSS,
cor.test(age, educ))
## Multiple regression
## ───────────────────
## Modeling functions generally use the /formula/ interface whith DV on
## left followed by "~" followed by predictors–for details see
## `help("formula")'
## • Predict the number of hours individuals spend on email (emailhrs)
m1 <- lm(educ ~ sex + age, data = datGSS)
summary(m1)
## Save R output to a file
## ───────────────────────
## Earlier we learned how to write a data set to a file. But what if we
## want to write something that isn't in a nice rectangular format, like
## the results of our regression model? For that we can use the `sink()'
## function:
sink(file="output.txt", split=TRUE) # start logging
print("This is the result from model 1\n")
print(summary(m1))
sink() ## sink with no arguments turns logging off
## Basic graphics: Frequency bars
## ──────────────────────────────
## Thanks to classes and methods, you can `plot()' many kinds of objects:
plot(datGSS$marital) # Plot a factor
## Basic graphics: Boxplots by group
## ─────────────────────────────────
## Thanks to classes and methods, you can `plot()' many kinds of objects:
with(datGSS,
plot(marital, educ)) # Plot ordinal by numeric
## Basic graphics: Mosaic chart
## ────────────────────────────
## Thanks to classes and methods, you can `plot()' many kinds of objects:
with(datGSS, # Plot factor X factor
plot(marital, happy))
## Exercise 3
## ──────────
## Using the datGSS data.frame
## 1. Cross-tabulate sex and emailhrs
## 2. Calculate the mean and standard deviation of incomdol by sex
## 3. Save the results of the previous two calculations to a file
## 4. Create a scatter plot with educ on the x-axis and incomdol on the
## y-axis
## Wrap-up
## ═══════
## Help us make this workshop better!
## ──────────────────────────────────
## • Please take a moment to fill out a very short feedback form
## • These workshops exist for you – tell us what you need!
## • [http://tinyurl.com/R-intro-feedback]
## Additional resources
## ────────────────────
## • IQSS workshops:
## [http://projects.iq.harvard.edu/rtc/filter_by/workshops]
## • IQSS statistical consulting: [http://rtc.iq.harvard.edu]
## • Software (all free!):
## • R and R package download: [http://cran.r-project.org]
## • Rstudio download: [http://rstudio.org]
## • ESS (emacs R package): [http://ess.r-project.org/]
## • Online tutorials
## • [http://www.codeschool.com/courses/try-r]
## • [http://www.datamind.org]
## • Getting help:
## • Documentation and tutorials:
## [http://cran.r-project.org/other-docs.html]
## • Recommended R packages by topic:
## [http://cran.r-project.org/web/views/]
## • Mailing list: [https://stat.ethz.ch/mailman/listinfo/r-help]
## • StackOverflow: [http://stackoverflow.com/questions/tagged/r]
## Exercise solutions
## ══════════════════
## Exercise 0 solution :prototype:
## ───────────────────
## 1) ] Try to get R to add 2 plus 2.
2 + 2
## 2) Try to figure out how evaluate lines directly from your R script.
## `In Rstudo this is 'Control-Enter'; may be different in another GUI'
## 3) R includes extensive documentation, including a file named "An
## introduction to R". Try to find this help file.
## `Go to the main help page by running 'help.start() or using the GUI
## menu, find and click on the link to "An Introduction to R".'
## 4) Go to [http://cran.r-project.org/web/views/] and skim the topic
## closest to your field/interests.
## `I like the machine learning topic!'
## Exercise 1 solution :prototype:
## ───────────────────
## 1) Attach the `rio' package if you haven't already done so
## install.packages("rio")
library(rio)
## 2) Read the SPSS data set in dataSets/gss.sav and assign the result to
## an R data object named GSS.sav
gss.data <- import("dataSets/gss.sav")
## 3) Make sure that the data loaded in step 2 is a data.frame (hint:
## check the arguments documented in the help page)
class(gss.data)
## 4) Display the dimensions of the GSS.sav.
dim(gss.data)
nrow(gss.data)
ncol(gss.data)
## 6) BONUS: figure out how to read the Excel file "gss.xlsx" into R
dat <- import("dataSets/gss.xlsx")
class(dat); dim(dat)
## Exercise 2 solution :prototype:
## ───────────────────
## Use the gss.rds data set
gss <- import("dataSets/gss.rds")
## 1) Create a subset of the data containing only rows where "usecomp" =
## "Yes". How many computer users are there?
gss.usecomp <- subset(gss, usecomp == "Yes")
nrow(gss.usecomp)
## 2) Generate the following variables:
## • "rich" equal to 0 if rincdol is less than 100000, and 1 otherwise
## • "sinc" equal to incomdol - rincdol
gss <- transform(gss,
rich = ifelse(rincdol < 100000, 0, 1),
sinc = incomdol - rincdol)
head(subset(gss, select = c("rincdol", "incomdol", "rich", "sinc")))
## 3) Generate a variable named "dual.earn" equal to 1 if both wkftwife =
## 1 and wkfthusb = 1, and zero otherwise. How many dual earners are
## there?
gss$dual.earn <- ifelse(gss$wkftwife == 1 & gss$wkfthusb == 1, 1, 0)
nrow(subset(gss, dual.earn == 1))
## Exercise 3 solution :prototype:
## ───────────────────
## Using the datGSS data.frame
## 1) Cross-tabulate sex and emailhrs
with(datGSS, table(emailhrs, sex))
## 2) Calculate the mean and standard deviation of incomdol by sex
by(datGSS$incomdol, datGSS$sex, mean)
by(datGSS$incomdol, datGSS$sex, sd)
## 3) Create a scatter plot with educ on the x-axis and incomdol on the
## y-axis
plot(subset(datGSS, select = c("educ", "incomdol")))
## `That first attempt is pretty ugly, let's clean it up.'
plot(log(incomdol) ~ jitter(educ, 8), # formula interface with log an jitter
data = subset(datGSS, # subsetting data as part of the plot call
subset = educ < 97),
cex = .6 # make points smaller
)
## Footnotes
## ─────────
## [1] Note that `subset()' is a convenience function; see `?Extract' for a
## more powerful (and complicated) way to subset data.
|
/R/Rintro/Rintro.R
|
no_license
|
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|
R
| false | false | 27,622 |
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|
## ━━━━━━━━━━━━━━━━━━━
## INTRODUCTION TO R
## Ista Zahn
## ━━━━━━━━━━━━━━━━━━━
## Table of Contents
## ─────────────────
## Workshop Materials and Introduction
## Graphical User Interfaces
## Data and Functions
## R packages
## Getting data into R
## Asking R for help
## Data Manipulation
## Basic Statistics and Graphs
## Wrap-up
## Exercise solutions
## Workshop Materials and Introduction
## ═══════════════════════════════════
## Materials and setup
## ───────────────────
## Lab computer users: Log in using the user name and password on the
## board to your left.
## Laptop users: You should have R installed –if not:
## • Open a web browser and go to [http://cran.r-project.org] and
## download and install it
## • Also helpful to install RStudio (download from [http://rstudio.com])
## Everyone: Download workshop materials:
## • Download materials from
## [http://tutorials.iq.harvard.edu/R/Rintro.zip]
## • Extract the zip file containing the materials to your desktop
## What is R?
## ──────────
## R is a /programming language designed for statistical computing/.
## Notable characteristics include:
## • Vast capabilities, wide range of statistical and graphical
## techniques
## • Very popular in academia, growing popularity in business:
## [http://r4stats.com/articles/popularity/]
## • Written primarily by statisticians
## • FREE (no cost, open source)
## • Excellent community support: mailing list, blogs, tutorials
## • Easy to extend by writing new functions
## InspiRation
## ───────────
## OK, it's free and popular, but what makes R worth learning? In a word,
## "packages". If you have a data manipulation, analysis or visualization
## task, chances are good that there is an R package for that. For
## example:
## • Want to interactively explore the shape of the Churyumov–Gerasimenko
## comet?
library(rgl)
open3d()
comet <- readOBJ(url("http://sci.esa.int/science-e/www/object/doc.cfm?fobjectid=54726"))
shade3d(comet, col="gray")
writeWebGL(dir="images", filename = "images/comet.html")
## • Want to find out where we are?
library(ggmap)
nwbuilding <- geocode("52 Oxford Street, Cambridge, MA")
ggmap(get_map("Cambridge, MA", zoom = 15)) +
geom_point(data=nwbuilding, size = 7, shape = 13, color = "red")
## • Want to forecast the population of Australia?
library(forecast)
fit <- auto.arima(austres)
## Projected numbers (in thousands) of Australian residents
plot(forecast(fit))
## Whatever you're trying to do, you're probably not the first to try
## doing it R. Chances are good that someone has already written a
## package for that.
## Coming to R
## ───────────
## Coming from…
## Stata: [http://www.princeton.edu/~otorres/RStata.pdf]
## SAS/SPSS: [http://www.et.bs.ehu.es/~etptupaf/pub/R/RforSAS&SPSSusers.pdf]
## matlab: [http://www.math.umaine.edu/~hiebeler/comp/matlabR.pdf]
## Python: [http://mathesaurus.sourceforge.net/matlab-python-xref.pdf]
## Graphical User Interfaces
## ═════════════════════════
## R GUI alternatives (no GUI)
## ───────────────────────────
## The old-school way is to run R directly in a terminal
## But hardly anybody does it that way anymore!
## R GUI alternatives (Windows default)
## ────────────────────────────────────
## The default windows GUI is not very good
## • No parentheses matching or syntax highlighting
## • No work-space browser
## R GUI Alternatives (Rstudio on Mac)
## ───────────────────────────────────
## Rstudio has many useful features, including parentheses matching and
## auto-completion
## R GUI Alternatives (Emacs with ESS)
## ───────────────────────────────────
## Emacs + ESS is a very powerful combination, but can be difficult to
## set up
## Launch RStudio :labsetup:
## ──────────────
## • Open the RStudio program
## • Open up today's R script
## • In RStudio, Go to *File => Open Script*
## • Locate and open the `Rintro.R' script in the Rintro folder on your
## desktop
## • Go to *Tools => Set working directory => To source file location*
## (more on the working directory later)
## • I encourage you to add your own notes to this file! Every line that
## starts with `#' is a comment that will be ignored by R. My comments
## all start with `##'; you can add your own, possibly using `#' or
## `###' to distinguish your comments from mine.
## Exercise 0
## ──────────
## The purpose of this exercise is mostly to give you an opportunity to
## explore the interface provided by RStudio (or whichever GUI you've
## decided to use). You may not know how to do these things; that's fine!
## This is an opportunity to learn. If you don't know how to do something
## you can can use internet search engines, search on [StackOverflow], or
## ask the person next to you.
## Also keep in mind that we are living in a golden age of tab
## completion. If you don't know the name of an R function, try guessing
## the first two or three letters and pressing TAB. If you guessed
## correctly the function you are looking for should appear in a pop up!
## 1. Try to get R to add 2 plus 2.
## 2. Try to calculate the square root of 10.
## 3. There is an R package named `car' (Companion to Applied
## Regression). Try to install this package.
## 4. R includes extensive documentation, including a file named "An
## introduction to R". Try to find this help file.
## 5. Go to [http://cran.r-project.org/web/views/] and skim the topic
## closest to your field/interests.
## [StackOverflow] http:stackoverflow.com
## Data and Functions
## ══════════════════
## Assignment
## ──────────
## Values can be assigned names and used in subsequent operations
## • The `<-' operator (less than followed by a dash) is used to save
## values
## • The name on the left gets the value on the right.
x <- 10 # Assign the value 10 to a variable named x
x + 1 # Add 1 to x
y <- x + 1 # Assign y the value x + 1
y
## Saved variables can be listed, overwritten and deleted
ls() # List variables in workspace
x # Print the value of x
x <- 100 # Overwrite x. Note that no warning is given!
x
rm(x) # Delete x
ls()
## Functions
## ─────────
## Using R is mostly about applying *functions* to *variables*. Functions
## • take *variable(s)* as input *argument(s)*
## • perform operations
## • *return* values which can be *assigned*
## • optionally perform side-effects such as writing a file to disk or
## opening a graphics window
## The general form for calling R functions is `FunctionName(arg.1,
## arg.2, ... arg.n)'
## Arguments can be matched by position or name
## Examples:
#?sqrt
a <- sqrt(y) # Call the sqrt function with argument x=y
round(a, digits = 2) # Call round() with arguments x=x and digits=2
# Functions can be nested so an alternative is
round(sqrt(y), digits = 5) # Take sqrt of a and round
## R packages
## ══════════
## There are thousands of R packages that extend R's capabilities.
## • To see what packages are loaded:
## `search()'
## • To view available packages:
## `library()'
## • To load a package:
## `library("car")'
## • Install new package:
## `install.packages("stringdist")'
## Getting data into R
## ═══════════════════
## The gss dataset
## ───────────────
## The next few examples use a subset of the General Social Survey data
## set. The variables in this subset include
head(read.csv("dataSets/gssInfo.csv"))
#see gssInfo.csv for rest of the variable descriptions
## The "working directory" and listing files
## ─────────────────────────────────────────
## R knows the directory it was started in, and refers to this as the
## "working directory". Since our workshop examples are in the Rintro
## folder on the desktop, we should all take a moment to set that as our
## working directory:
setwd("~/Desktop/Rintro")
## We can also set the working directory using paths relative to the
## current working directory:
getwd() # get the current working directory
setwd("dataSets") # set wd to the dataSets folder
getwd()
setwd("..") # set wd to enclosing folder ("up")
## It can be convenient to list files in a directory without leaving R
list.files("dataSets") # list files in the dataSets folder
# list.files("dataSets", pattern = ".csv") # restrict to .csv files
## Importing data from files the easy way
## ──────────────────────────────────────
## In order to read data from a file, you usually have to know what kind
## of file it is. The table below lists some common data types.
## ━━━━━━━━━━━━━━━━━━━━━━━━
## Common data types
## ────────────────────────
## comma separated (.csv)
## Stata (.dta)
## SPSS (.sav)
## SAS (.sas7bdat)
## Excel (.xls, .xlsx)
## ━━━━━━━━━━━━━━━━━━━━━━━━
## R is smart enough to recognize most common file formats for us using
## the `import()' function. To use this functionality we first need to
## install and attache the `rio' package.
## install and load the rio package
# install.packages("rio")
library(rio)
## import data from a variety of formats
# read gss data from the gss.rds R file
datGSS <- import("dataSets/gss.rds")
# read gss data from the gss.csv comma separated file
gss.data <- import("dataSets/gss.csv")
# read a Stata dataset from gss.dta
datGSS <- import("dataSets/gss.dta")
## Importing data from files the hard way
## ──────────────────────────────────────
## In order to read data from a file, you have to know what kind of file
## it is. The table below lists the functions needed to import data from
## common file formats.
## ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## data type function package
## ──────────────────────────────────────────────────────────────────────
## comma separated (.csv) read.csv() utils (default)
## other delimited formats read.table() utils (default)
## Stata version 7-12 (.dta) read.dta() foreign
## Stata version 13-14 (.dta) read_dta() haven
## SPSS (.sav) read.spss() foreign
## SAS (.sas7bdat) read.sas7bdat() sas7bdat
## Excel (.xls, .xlsx) readWorksheetFromFile() XLConnect
## ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## Examples:
# read gss data from the gss.rds R file
datGSS <- readRDS("dataSets/gss.rds")
# read gss data from the gss.csv comma separated file
gss.data <- read.csv("dataSets/gss.csv") # read gss data
# read a Stata dataset from gss.dta
library(foreign) # load foreign data functions
datGSS <- read.dta(file="dataSets/gss.dta")
## Checking imported data
## ──────────────────────
## Always a good idea to examine the imported data set–usually we want
## the results to be a `data.frame'
class(datGSS) # check to see that test is what we expect it to be
dim(datGSS) # how many rows and columns?
names(datGSS) # or colnames(datGSS)
str(datGSS[1:10]) # more details about the first 10 columns
## Saving and loading R workspaces
## ────────────────────────────────
## In addition to importing individual datasets, R can save and load
## entire workspaces
## • Save our entire workspace
ls() # list objects in our workspace
save.image(file="myWorkspace.RData") # save workspace
rm(list=ls()) # remove all objects from our workspace
ls() # list stored objects to make sure they are deleted
## • Load the "myWorkspace.RData" file and check that it is restored
load("myWorkspace.RData") # load myWorkspace.RData
ls() # list objects
## When you close R you will be asked if you want to save your workspace
## – if you choose yes then your workspace will be restored next time you
## start R
## Exercise 1
## ──────────
## 1. Install and attach the `rio' package if you haven't already done so
## 2. Read the SPSS data set in dataSets/gss.sav and assign the result to
## an R data object named GSS.sav
## 3. Make sure that the data loaded in step 2 is a data.frame
## 4. Display the dimensions of the GSS.sav
## 5. BONUS: figure out how to read the Excel file "gss.xlsx" into R
## Asking R for help
## ═════════════════
## R has extensive built-in documentation that can be accessed through R
## commands or through the GUI.
## • Start html help, search/browse using web browser
## • at the R console:
## `help.start()'
## • or use the help menu from you GUI
## • Look up the documentation for a function
## `help(plot)'
## `?kmeans'
## • Look up documentation for a package
## `help(package="stats")'
## • Search documentation from R (not always the best way… google often
## works better)
## `help.search("classification")'
## Data Manipulation
## ═════════════════
## data.frame objects
## ──────────────────
## • Usually data read into R will be stored as a *data.frame*
## • A data.frame is a list of vectors of equal length
## • Each vector in the list forms a column
## • Each column can be a differnt type of vector
## • Often the columns are variables and the rows are observations
## • A data.frame has two dimensions corresponding the number of rows and
## the number of columns (in that order)
## Extracting subsets of data.frames
## ─────────────────────────────────
## You can extract subsets of data.frames using the `subset()'
## function[1]. Use the `select' argument to select columns:
# selecting specifig columns
head(# first n rows
subset(datGSS,
select = 1:4 # column 1 to 5
),
n = 10# show first 10 rows
)
## and the `subset' argument to select rows:
subset(datGSS,
# rows where age > 90
subset = age > 90,
## sex and age columns
select = c("sex", "age"))
## the $ operator can be used to extract a single column
str(datGSS$age)
## In the previous example we used `>' to select rows where age was
## greater than 90. Other relational and logical operators are listed
## below.
## ==: equal to
## !=: not equal to
## >: greater than
## <: less than
## >=: greater than or equal to
## <=: less than or equal to
## &: and
## |: or
## Transforming data.frames
## ────────────────────────
## You can modify data.frames using the `transform()' function.
# creating new variable mean centered age
datGSS <- transform(datGSS,
ageC = age - mean(age))
#education difference between wifes and husbands
datGSS <- transform(datGSS,
educ.diff = wifeduc - husbeduc)
datGSS$wifeduc_comp <- ifelse(
is.na(datGSS$wifeduc), #condition
mean(datGSS$wifeduc, na.rm=TRUE), # value if condition met
datGSS$wifeduc) # value otherwise
## examine our newly created variables
head(subset(datGSS,
select = c("age", "ageC", "wifeduc", "wifeduc_comp",
"husbeduc", "educ.diff")), n = 8)
## Note that `transform' is a convenience function; see `?Extract' for a
## more powerful way to modify data.frames.
## Exporting Data
## ──────────────
## Now that we have made some changes to our GSS data set, we might want
## to save those changes to a file. Everything we have done so far has
## only modified the data in R; the files have remained unchanged.
# write data to a .csv file
write.csv(datGSS, file = "gss.csv")
# write data to a Stata file
write.dta(datGSS, file = "gss.dta")
# write data to an R file
saveRDS(datGSS, file = "gss.rds")
## Exercise 2: Data manipulation
## ─────────────────────────────
## Use the gss.rds data set
## 1. Generate the following variables:
## • "rich" equal to 0 if rincdol is less than 100000, and 1 otherwise
## • "sinc" equal to incomdol - rincdol
## 2. Create a subset of the data containing only rows where "usecomp" =
## "Yes"
## 3. Examine the data.frame created in step 2, and answer the following
## questions:
## • How many rows does it have?
## • How many columns does it have?
## • What is the class of the "satjob" variable?
## 4. BONUS (hard): Generate a variable named "dual.earn" equal to 1 if
## both wkftwife = 1 and wkfthusb = 1, and zero otherwise
## Basic Statistics and Graphs
## ═══════════════════════════
## Basic statistics
## ────────────────
## Descriptive statistics of single variables are straightforward:
mean(datGSS$educ) # calculate mean value of education
sd(datGSS$educ) # calculate standard deviation of x
# calculate min, max, quantiles, mean of educ, age, and ageC
summary(subset(datGSS, select = c("educ", "age", "ageC")))
## Some of these functions (e.g., summary) will also work with
## data.frames and other types of objects, others (such as `sd') will
## not.
## Counts and proportions
## ──────────────────────
## Start by using the `table()' function to tabulate counts, then perform
## additional computations if needed
sex.counts <- table(datGSS$sex) # tabulate sex categories
sex.counts
prop.table(sex.counts) # convert to proportions
## Add variables for crosstabs
table(subset(datGSS, select = c("sex", "happy"))) # crosstab marital X happy
## Statistics by classification factors
## ────────────────────────────────────
## The `by()' function can be used to perform a calculation separately
## for each level of a classifying variable
by(subset(datGSS, select = c("income", "educ")),
INDICES=datGSS["sex"],
FUN=summary)
## Correlations
## ────────────
## Let's look at correlations among between age, income, and education
cor(subset(datGSS, select = c("age", "incomdol", "educ")))
## For significance tests, use cor.test()
with(datGSS,
cor.test(age, educ))
## Multiple regression
## ───────────────────
## Modeling functions generally use the /formula/ interface whith DV on
## left followed by "~" followed by predictors–for details see
## `help("formula")'
## • Predict the number of hours individuals spend on email (emailhrs)
m1 <- lm(educ ~ sex + age, data = datGSS)
summary(m1)
## Save R output to a file
## ───────────────────────
## Earlier we learned how to write a data set to a file. But what if we
## want to write something that isn't in a nice rectangular format, like
## the results of our regression model? For that we can use the `sink()'
## function:
sink(file="output.txt", split=TRUE) # start logging
print("This is the result from model 1\n")
print(summary(m1))
sink() ## sink with no arguments turns logging off
## Basic graphics: Frequency bars
## ──────────────────────────────
## Thanks to classes and methods, you can `plot()' many kinds of objects:
plot(datGSS$marital) # Plot a factor
## Basic graphics: Boxplots by group
## ─────────────────────────────────
## Thanks to classes and methods, you can `plot()' many kinds of objects:
with(datGSS,
plot(marital, educ)) # Plot ordinal by numeric
## Basic graphics: Mosaic chart
## ────────────────────────────
## Thanks to classes and methods, you can `plot()' many kinds of objects:
with(datGSS, # Plot factor X factor
plot(marital, happy))
## Exercise 3
## ──────────
## Using the datGSS data.frame
## 1. Cross-tabulate sex and emailhrs
## 2. Calculate the mean and standard deviation of incomdol by sex
## 3. Save the results of the previous two calculations to a file
## 4. Create a scatter plot with educ on the x-axis and incomdol on the
## y-axis
## Wrap-up
## ═══════
## Help us make this workshop better!
## ──────────────────────────────────
## • Please take a moment to fill out a very short feedback form
## • These workshops exist for you – tell us what you need!
## • [http://tinyurl.com/R-intro-feedback]
## Additional resources
## ────────────────────
## • IQSS workshops:
## [http://projects.iq.harvard.edu/rtc/filter_by/workshops]
## • IQSS statistical consulting: [http://rtc.iq.harvard.edu]
## • Software (all free!):
## • R and R package download: [http://cran.r-project.org]
## • Rstudio download: [http://rstudio.org]
## • ESS (emacs R package): [http://ess.r-project.org/]
## • Online tutorials
## • [http://www.codeschool.com/courses/try-r]
## • [http://www.datamind.org]
## • Getting help:
## • Documentation and tutorials:
## [http://cran.r-project.org/other-docs.html]
## • Recommended R packages by topic:
## [http://cran.r-project.org/web/views/]
## • Mailing list: [https://stat.ethz.ch/mailman/listinfo/r-help]
## • StackOverflow: [http://stackoverflow.com/questions/tagged/r]
## Exercise solutions
## ══════════════════
## Exercise 0 solution :prototype:
## ───────────────────
## 1) ] Try to get R to add 2 plus 2.
2 + 2
## 2) Try to figure out how evaluate lines directly from your R script.
## `In Rstudo this is 'Control-Enter'; may be different in another GUI'
## 3) R includes extensive documentation, including a file named "An
## introduction to R". Try to find this help file.
## `Go to the main help page by running 'help.start() or using the GUI
## menu, find and click on the link to "An Introduction to R".'
## 4) Go to [http://cran.r-project.org/web/views/] and skim the topic
## closest to your field/interests.
## `I like the machine learning topic!'
## Exercise 1 solution :prototype:
## ───────────────────
## 1) Attach the `rio' package if you haven't already done so
## install.packages("rio")
library(rio)
## 2) Read the SPSS data set in dataSets/gss.sav and assign the result to
## an R data object named GSS.sav
gss.data <- import("dataSets/gss.sav")
## 3) Make sure that the data loaded in step 2 is a data.frame (hint:
## check the arguments documented in the help page)
class(gss.data)
## 4) Display the dimensions of the GSS.sav.
dim(gss.data)
nrow(gss.data)
ncol(gss.data)
## 6) BONUS: figure out how to read the Excel file "gss.xlsx" into R
dat <- import("dataSets/gss.xlsx")
class(dat); dim(dat)
## Exercise 2 solution :prototype:
## ───────────────────
## Use the gss.rds data set
gss <- import("dataSets/gss.rds")
## 1) Create a subset of the data containing only rows where "usecomp" =
## "Yes". How many computer users are there?
gss.usecomp <- subset(gss, usecomp == "Yes")
nrow(gss.usecomp)
## 2) Generate the following variables:
## • "rich" equal to 0 if rincdol is less than 100000, and 1 otherwise
## • "sinc" equal to incomdol - rincdol
gss <- transform(gss,
rich = ifelse(rincdol < 100000, 0, 1),
sinc = incomdol - rincdol)
head(subset(gss, select = c("rincdol", "incomdol", "rich", "sinc")))
## 3) Generate a variable named "dual.earn" equal to 1 if both wkftwife =
## 1 and wkfthusb = 1, and zero otherwise. How many dual earners are
## there?
gss$dual.earn <- ifelse(gss$wkftwife == 1 & gss$wkfthusb == 1, 1, 0)
nrow(subset(gss, dual.earn == 1))
## Exercise 3 solution :prototype:
## ───────────────────
## Using the datGSS data.frame
## 1) Cross-tabulate sex and emailhrs
with(datGSS, table(emailhrs, sex))
## 2) Calculate the mean and standard deviation of incomdol by sex
by(datGSS$incomdol, datGSS$sex, mean)
by(datGSS$incomdol, datGSS$sex, sd)
## 3) Create a scatter plot with educ on the x-axis and incomdol on the
## y-axis
plot(subset(datGSS, select = c("educ", "incomdol")))
## `That first attempt is pretty ugly, let's clean it up.'
plot(log(incomdol) ~ jitter(educ, 8), # formula interface with log an jitter
data = subset(datGSS, # subsetting data as part of the plot call
subset = educ < 97),
cex = .6 # make points smaller
)
## Footnotes
## ─────────
## [1] Note that `subset()' is a convenience function; see `?Extract' for a
## more powerful (and complicated) way to subset data.
|
#' Plot predictions produced with predict_DO
#'
#' Plots modeled values as lines, observed values as points
#'
#' @param DO_preds a data.frame of predictions such as that returned by
#' predict_DO()
#' @param y_var character. Should the plot display predicted & observed values
#' in concentration (conc) or as percent of saturation (pctsat)? The default
#' is to plot both.
#' @param style character indicating which graphics package to use
#' @param y_lim list of named vectors, each of which has length 2 and is numeric
#' and has a name in the possible values of y_var. NA within a vector
#' indicates that the data range should be used. for ggplot2, y_lim is only
#' used to exclude values outside that range and is ignored if the data span a
#' narrower range
#' @examples
#' \dontrun{
#' mm <- metab_night(specs(mm_name('night')), data=data_metab('3', day_start=12, day_end=36))
#' plot_DO_preds(predict_DO(mm))
#' plot_DO_preds(predict_DO(mm), style='dygraphs', y_var='pctsat')
#' }
#' @import dplyr
#' @importFrom unitted v
#' @export
plot_DO_preds <- function(DO_preds, y_var=c('conc','pctsat','ddodt'),
style=c('ggplot2','dygraphs'),
y_lim=list(conc=c(NA,NA), pctsat=c(NA,NA), ddodt=c(NA,NA))) {
style <- match.arg(style)
y_var <- match.arg(y_var, several.ok=TRUE)
params <- list(
xlab='Local time',
ylab='Predictions (lines) and observations (points)',
colors=list(conc=c('#A64B00','#FF7400'), pctsat=c('#007929','#23BC47'), ddodt=c('#05326D','#4282D3'))
)
DO.obs <- DO.mod <- DO.sat <- '.dplyr.var'
DO_preds_conc <- mutate(
DO_preds, as='conc', var='DO (mg/L)', col1=params$colors$conc[1], col2=params$colors$conc[2], lab='DO (mg/L)',
mod=DO.mod,
obs=DO.obs)
DO_preds_pctsat <- mutate(
DO_preds, as='pctsat', var='DO (% sat)', col1=params$colors$pctsat[1], col2=params$colors$pctsat[2], lab='DO (% sat)',
mod=100*DO.mod/DO.sat,
obs=100*DO.obs/DO.sat)
DO_preds_ddodt <-
mutate(
DO_preds[-1,], as='ddodt', var='dDO/dt (mg/L/d)', col1=params$colors$ddodt[1], col2=params$colors$ddodt[2], lab='dDO/dt~(mg~L^-1~d^-1)',
mod = diff(DO_preds$DO.mod)/as.numeric(diff(DO_preds$solar.time), units="days"),
obs = diff(DO_preds$DO.obs)/as.numeric(diff(DO_preds$solar.time), units="days")) %>%
mutate(
mod = ifelse(diff(DO_preds$date)==0, mod, NA),
obs = ifelse(diff(DO_preds$date)==0, obs, NA))
DO_preds_all <- bind_rows(DO_preds_conc, DO_preds_pctsat, DO_preds_ddodt) %>%
mutate(var=ordered(var, c(conc='DO (mg/L)', pctsat='DO (% sat)', ddodt='dDO/dt (mg/L/d)')[y_var]))
plot_out <- switch(
style,
'ggplot2' = {
if(!requireNamespace("ggplot2", quietly=TRUE))
stop("call install.packages('ggplot2') before plotting with style='ggplot2'")
. <- solar.time <- mod <- date <- col1 <- col2 <- obs <- '.ggplot.var'
preds_ggplot <- v(DO_preds_all) %>%
filter(as %in% y_var)
if('conc' %in% names(y_lim)) {
lim <- y_lim[['conc']][1]; if(!is.na(lim)) preds_ggplot <- filter(preds_ggplot, as != 'conc' | (mod >= lim & obs >= lim))
lim <- y_lim[['conc']][2]; if(!is.na(lim)) preds_ggplot <- filter(preds_ggplot, as != 'conc' | (mod <= lim & obs <= lim))
}
if('pctsat' %in% names(y_lim)) {
lim <- y_lim[['pctsat']][1]; if(!is.na(lim)) preds_ggplot <- filter(preds_ggplot, as != 'pctsat' | (mod >= lim & obs >= lim))
lim <- y_lim[['pctsat']][2]; if(!is.na(lim)) preds_ggplot <- filter(preds_ggplot, as != 'pctsat' | (mod <= lim & obs <= lim))
}
if('ddodt' %in% names(y_lim)) {
lim <- y_lim[['ddodt']][1]; if(!is.na(lim)) preds_ggplot <- filter(preds_ggplot, as != 'ddodt' | (mod >= lim & obs >= lim))
lim <- y_lim[['ddodt']][2]; if(!is.na(lim)) preds_ggplot <- filter(preds_ggplot, as != 'ddodt' | (mod <= lim & obs <= lim))
}
g <- ggplot2::ggplot(preds_ggplot, ggplot2::aes(x=solar.time, group=date)) +
ggplot2::geom_point(ggplot2::aes(y=obs, color=col2), alpha=0.6) +
ggplot2::geom_line(ggplot2::aes(y=mod, color=col1), size=0.8) +
ggplot2::scale_color_identity(guide='none') +
ggplot2::theme_bw() +
ggplot2::facet_grid(var ~ ., scales="free_y") +
ggplot2::xlab(params$xlab) + ggplot2::ylab(params$ylab)
suppressWarnings(print(g))
},
'dygraphs' = {
if(!requireNamespace("dygraphs", quietly=TRUE))
stop("call install.packages(dygraphs') before plotting with style='dygraphs'")
if(!requireNamespace("xts", quietly=TRUE))
stop("call install.packages(dygraphs') before plotting with style='dygraphs'")
. <- '.dplyr.var'
preds_xts <- v(DO_preds_all) %>%
filter(as %in% y_var) %>%
arrange(solar.time) %>%
group_by(date) %>%
do(., {
out <- .[c(seq_len(nrow(.)),nrow(.)),]
out[nrow(.)+1,c('mod','obs')] <- NA
out
}) %>%
ungroup()
prep_dygraph <- function(y_var) {
preds_xts %>%
filter(as==y_var) %>%
select(mod,obs,solar.time) %>%
mutate(solar.time=lubridate::force_tz(solar.time, Sys.getenv("TZ"))) %>% # dygraphs makes some funky tz assumptions. this seems to help.
xts::xts(x=select(., -solar.time), order.by=.$solar.time, unique=FALSE, tzone=Sys.getenv("TZ"))
}
if(length(y_var) > 1) warning("can only plot one dygraph y_var at a time for now; plotting y_vars in succession")
sapply(y_var, function(yvar) {
y_var_long <- preds_xts %>% filter(as==yvar) %>% slice(1) %>% .[['var']] %>% as.character()
y_var_col <- params$colors[[yvar]]
dat <- prep_dygraph(yvar)
ymin <- max(c(min(c(dat[,1], dat[,2]), na.rm=TRUE), y_lim[[yvar]][1]), na.rm=TRUE)
ymax <- min(c(max(c(dat[,1], dat[,2]), na.rm=TRUE), y_lim[[yvar]][2]), na.rm=TRUE)
dygraphs::dygraph(dat, xlab=params$xlab, ylab=y_var_long, group='plot_DO_preds') %>%
dygraphs::dySeries('mod', drawPoints = FALSE, label=paste0("Modeled ", y_var_long), color=y_var_col[1]) %>%
dygraphs::dySeries('obs', drawPoints = TRUE, strokeWidth=0, label=paste0("Observed ", y_var_long), color=y_var_col[2]) %>%
dygraphs::dyAxis('y', valueRange=(c(ymin,ymax)+(ymax-ymin)*c(-0.05,0.15))) %>%
dygraphs::dyOptions(colorSaturation=1) %>%
dygraphs::dyLegend(labelsSeparateLines = TRUE, width=300) %>%
dygraphs::dyRangeSelector(height = 20) %>%
print()
})
}
)
invisible(plot_out)
}
|
/R/plot_DO_preds.R
|
permissive
|
Arroita/streamMetabolizer
|
R
| false | false | 6,630 |
r
|
#' Plot predictions produced with predict_DO
#'
#' Plots modeled values as lines, observed values as points
#'
#' @param DO_preds a data.frame of predictions such as that returned by
#' predict_DO()
#' @param y_var character. Should the plot display predicted & observed values
#' in concentration (conc) or as percent of saturation (pctsat)? The default
#' is to plot both.
#' @param style character indicating which graphics package to use
#' @param y_lim list of named vectors, each of which has length 2 and is numeric
#' and has a name in the possible values of y_var. NA within a vector
#' indicates that the data range should be used. for ggplot2, y_lim is only
#' used to exclude values outside that range and is ignored if the data span a
#' narrower range
#' @examples
#' \dontrun{
#' mm <- metab_night(specs(mm_name('night')), data=data_metab('3', day_start=12, day_end=36))
#' plot_DO_preds(predict_DO(mm))
#' plot_DO_preds(predict_DO(mm), style='dygraphs', y_var='pctsat')
#' }
#' @import dplyr
#' @importFrom unitted v
#' @export
plot_DO_preds <- function(DO_preds, y_var=c('conc','pctsat','ddodt'),
style=c('ggplot2','dygraphs'),
y_lim=list(conc=c(NA,NA), pctsat=c(NA,NA), ddodt=c(NA,NA))) {
style <- match.arg(style)
y_var <- match.arg(y_var, several.ok=TRUE)
params <- list(
xlab='Local time',
ylab='Predictions (lines) and observations (points)',
colors=list(conc=c('#A64B00','#FF7400'), pctsat=c('#007929','#23BC47'), ddodt=c('#05326D','#4282D3'))
)
DO.obs <- DO.mod <- DO.sat <- '.dplyr.var'
DO_preds_conc <- mutate(
DO_preds, as='conc', var='DO (mg/L)', col1=params$colors$conc[1], col2=params$colors$conc[2], lab='DO (mg/L)',
mod=DO.mod,
obs=DO.obs)
DO_preds_pctsat <- mutate(
DO_preds, as='pctsat', var='DO (% sat)', col1=params$colors$pctsat[1], col2=params$colors$pctsat[2], lab='DO (% sat)',
mod=100*DO.mod/DO.sat,
obs=100*DO.obs/DO.sat)
DO_preds_ddodt <-
mutate(
DO_preds[-1,], as='ddodt', var='dDO/dt (mg/L/d)', col1=params$colors$ddodt[1], col2=params$colors$ddodt[2], lab='dDO/dt~(mg~L^-1~d^-1)',
mod = diff(DO_preds$DO.mod)/as.numeric(diff(DO_preds$solar.time), units="days"),
obs = diff(DO_preds$DO.obs)/as.numeric(diff(DO_preds$solar.time), units="days")) %>%
mutate(
mod = ifelse(diff(DO_preds$date)==0, mod, NA),
obs = ifelse(diff(DO_preds$date)==0, obs, NA))
DO_preds_all <- bind_rows(DO_preds_conc, DO_preds_pctsat, DO_preds_ddodt) %>%
mutate(var=ordered(var, c(conc='DO (mg/L)', pctsat='DO (% sat)', ddodt='dDO/dt (mg/L/d)')[y_var]))
plot_out <- switch(
style,
'ggplot2' = {
if(!requireNamespace("ggplot2", quietly=TRUE))
stop("call install.packages('ggplot2') before plotting with style='ggplot2'")
. <- solar.time <- mod <- date <- col1 <- col2 <- obs <- '.ggplot.var'
preds_ggplot <- v(DO_preds_all) %>%
filter(as %in% y_var)
if('conc' %in% names(y_lim)) {
lim <- y_lim[['conc']][1]; if(!is.na(lim)) preds_ggplot <- filter(preds_ggplot, as != 'conc' | (mod >= lim & obs >= lim))
lim <- y_lim[['conc']][2]; if(!is.na(lim)) preds_ggplot <- filter(preds_ggplot, as != 'conc' | (mod <= lim & obs <= lim))
}
if('pctsat' %in% names(y_lim)) {
lim <- y_lim[['pctsat']][1]; if(!is.na(lim)) preds_ggplot <- filter(preds_ggplot, as != 'pctsat' | (mod >= lim & obs >= lim))
lim <- y_lim[['pctsat']][2]; if(!is.na(lim)) preds_ggplot <- filter(preds_ggplot, as != 'pctsat' | (mod <= lim & obs <= lim))
}
if('ddodt' %in% names(y_lim)) {
lim <- y_lim[['ddodt']][1]; if(!is.na(lim)) preds_ggplot <- filter(preds_ggplot, as != 'ddodt' | (mod >= lim & obs >= lim))
lim <- y_lim[['ddodt']][2]; if(!is.na(lim)) preds_ggplot <- filter(preds_ggplot, as != 'ddodt' | (mod <= lim & obs <= lim))
}
g <- ggplot2::ggplot(preds_ggplot, ggplot2::aes(x=solar.time, group=date)) +
ggplot2::geom_point(ggplot2::aes(y=obs, color=col2), alpha=0.6) +
ggplot2::geom_line(ggplot2::aes(y=mod, color=col1), size=0.8) +
ggplot2::scale_color_identity(guide='none') +
ggplot2::theme_bw() +
ggplot2::facet_grid(var ~ ., scales="free_y") +
ggplot2::xlab(params$xlab) + ggplot2::ylab(params$ylab)
suppressWarnings(print(g))
},
'dygraphs' = {
if(!requireNamespace("dygraphs", quietly=TRUE))
stop("call install.packages(dygraphs') before plotting with style='dygraphs'")
if(!requireNamespace("xts", quietly=TRUE))
stop("call install.packages(dygraphs') before plotting with style='dygraphs'")
. <- '.dplyr.var'
preds_xts <- v(DO_preds_all) %>%
filter(as %in% y_var) %>%
arrange(solar.time) %>%
group_by(date) %>%
do(., {
out <- .[c(seq_len(nrow(.)),nrow(.)),]
out[nrow(.)+1,c('mod','obs')] <- NA
out
}) %>%
ungroup()
prep_dygraph <- function(y_var) {
preds_xts %>%
filter(as==y_var) %>%
select(mod,obs,solar.time) %>%
mutate(solar.time=lubridate::force_tz(solar.time, Sys.getenv("TZ"))) %>% # dygraphs makes some funky tz assumptions. this seems to help.
xts::xts(x=select(., -solar.time), order.by=.$solar.time, unique=FALSE, tzone=Sys.getenv("TZ"))
}
if(length(y_var) > 1) warning("can only plot one dygraph y_var at a time for now; plotting y_vars in succession")
sapply(y_var, function(yvar) {
y_var_long <- preds_xts %>% filter(as==yvar) %>% slice(1) %>% .[['var']] %>% as.character()
y_var_col <- params$colors[[yvar]]
dat <- prep_dygraph(yvar)
ymin <- max(c(min(c(dat[,1], dat[,2]), na.rm=TRUE), y_lim[[yvar]][1]), na.rm=TRUE)
ymax <- min(c(max(c(dat[,1], dat[,2]), na.rm=TRUE), y_lim[[yvar]][2]), na.rm=TRUE)
dygraphs::dygraph(dat, xlab=params$xlab, ylab=y_var_long, group='plot_DO_preds') %>%
dygraphs::dySeries('mod', drawPoints = FALSE, label=paste0("Modeled ", y_var_long), color=y_var_col[1]) %>%
dygraphs::dySeries('obs', drawPoints = TRUE, strokeWidth=0, label=paste0("Observed ", y_var_long), color=y_var_col[2]) %>%
dygraphs::dyAxis('y', valueRange=(c(ymin,ymax)+(ymax-ymin)*c(-0.05,0.15))) %>%
dygraphs::dyOptions(colorSaturation=1) %>%
dygraphs::dyLegend(labelsSeparateLines = TRUE, width=300) %>%
dygraphs::dyRangeSelector(height = 20) %>%
print()
})
}
)
invisible(plot_out)
}
|
testlist <- list(A = structure(c(2.32784507357645e-308, 9.53818252170362e+295, 4.0343042887248e-305, 4.12396251261199e-221, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(5L, 7L)), B = structure(0, .Dim = c(1L, 1L)))
result <- do.call(multivariance:::match_rows,testlist)
str(result)
|
/multivariance/inst/testfiles/match_rows/AFL_match_rows/match_rows_valgrind_files/1613107581-test.R
|
no_license
|
akhikolla/updatedatatype-list3
|
R
| false | false | 343 |
r
|
testlist <- list(A = structure(c(2.32784507357645e-308, 9.53818252170362e+295, 4.0343042887248e-305, 4.12396251261199e-221, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(5L, 7L)), B = structure(0, .Dim = c(1L, 1L)))
result <- do.call(multivariance:::match_rows,testlist)
str(result)
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/Policy.R
\name{Policy.rate.limit}
\alias{Policy.rate.limit}
\title{Create a Policy.rate.limit object enforcing the namesake access policy}
\usage{
Policy.rate.limit(rate.limit, last.access = NULL)
}
\arguments{
\item{rate.limit}{The rate limit, in accesses per second.}
\item{last.access}{The date and time of the last access. This should always be left at default}
}
\value{
The newly minted Policy.rate.limit object
}
\description{
A rate limit policy prescribes a minimum interval between accesses to a resource
}
\details{
This is used to provide the \code{.policy} argument to \code{\link{make.web.call}}.
}
|
/man/Policy.rate.limit.Rd
|
no_license
|
wulixin/webapi
|
R
| false | false | 701 |
rd
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/Policy.R
\name{Policy.rate.limit}
\alias{Policy.rate.limit}
\title{Create a Policy.rate.limit object enforcing the namesake access policy}
\usage{
Policy.rate.limit(rate.limit, last.access = NULL)
}
\arguments{
\item{rate.limit}{The rate limit, in accesses per second.}
\item{last.access}{The date and time of the last access. This should always be left at default}
}
\value{
The newly minted Policy.rate.limit object
}
\description{
A rate limit policy prescribes a minimum interval between accesses to a resource
}
\details{
This is used to provide the \code{.policy} argument to \code{\link{make.web.call}}.
}
|
drives <- read.csv("~/2005-2013 Drives.csv")
drives$End.Reason <- as.factor(drives$End.Reason)
# end.reason <- drives$End.Reason
# dummies <- model.matrix(~drives$End.Reason)
# drives <- cbind(drives, dummies)
# drives$End.Reason <- end.reason
drives$Points <- ifelse(drives$End.Reason == "TOUCHDOWN", 7,
ifelse(drives$End.Reason == "FIELD GOAL", 3,
ifelse(drives$End.Reason == "SAFETY", -2, 0)))
drives$Start.Spot <- as.factor(drives$Start.Spot)
expected <- aggregate(drives$Points, by = list(drives$Start.Spot), mean)
expected$Group.1 <- (100 - as.numeric(expected$Group.1))
drives$Starting.Pos.To.EZ <- (100 - as.numeric(drives$Start.Spot))
colnames(expected) <- c("Starting.Pos.To.EZ", "Expected.Points")
drives <- merge(drives, expected, by="Starting.Pos.To.EZ")
l <- lm(Points ~ as.numeric(Starting.Pos.To.EZ), drives)
summary(l)
plot(expected, type = "c", xlab = "Yards to End Zone", ylab = "Expected Points")
lines(expected)
abline(l)
write.csv(drives, file = "~/Drives with Expected Points.csv", row.names = FALSE)
|
/Expected Points.R
|
no_license
|
jmar257/cfb-analytics
|
R
| false | false | 1,092 |
r
|
drives <- read.csv("~/2005-2013 Drives.csv")
drives$End.Reason <- as.factor(drives$End.Reason)
# end.reason <- drives$End.Reason
# dummies <- model.matrix(~drives$End.Reason)
# drives <- cbind(drives, dummies)
# drives$End.Reason <- end.reason
drives$Points <- ifelse(drives$End.Reason == "TOUCHDOWN", 7,
ifelse(drives$End.Reason == "FIELD GOAL", 3,
ifelse(drives$End.Reason == "SAFETY", -2, 0)))
drives$Start.Spot <- as.factor(drives$Start.Spot)
expected <- aggregate(drives$Points, by = list(drives$Start.Spot), mean)
expected$Group.1 <- (100 - as.numeric(expected$Group.1))
drives$Starting.Pos.To.EZ <- (100 - as.numeric(drives$Start.Spot))
colnames(expected) <- c("Starting.Pos.To.EZ", "Expected.Points")
drives <- merge(drives, expected, by="Starting.Pos.To.EZ")
l <- lm(Points ~ as.numeric(Starting.Pos.To.EZ), drives)
summary(l)
plot(expected, type = "c", xlab = "Yards to End Zone", ylab = "Expected Points")
lines(expected)
abline(l)
write.csv(drives, file = "~/Drives with Expected Points.csv", row.names = FALSE)
|
####################################################################################################
# Load libs and data
####################################################################################################
load.libraries(c('stringr','truncnorm','reshape2','gdata','mvtnorm','gtools','yaml','GGally','stringr'))
installXLSXsupport()
#scenario.dir <- pp(afrp.shared,'Task-2.1-AFI-Deployment-Assessment/AFCI_Porfolio_Model/base/')
scenario.dir <- pp(afrp.shared,'Task-2.1-AFI-Deployment-Assessment/AFCI_Porfolio_Model/gas-experiment/')
base.scenario.dir <- pp(afrp.shared,'Task-2.1-AFI-Deployment-Assessment/AFCI_Porfolio_Model/base/')
plot.dir.base <- pp(afrp.shared,'Task-2.1-AFI-Deployment-Assessment/plots/')
#scens <- c('fuel-gasoline-price')
#scens <- c('fuel-canola-price','fuel-elec-price','fuel-h2-price','fuel-sorghum-price','fuel-sugarcane-price')
#scens <- c('discount-rate')
#scens <- pp('distrib-',c('h2','bev','phev','flex','ethanol'))
#scens <- pp('vehicle-',c('h2','bev','phev','flex'))
#scens <- c('bev-penetration')
scens <- c('blend-wall')
#scens <- c('base','fuel-gasoline-price','fuel-canola-price','fuel-elec-price','fuel-h2-price','fuel-sorghum-price','fuel-sugarcane-price','discount-rate',pp('distrib-',c('h2','bev','phev','flex','ethanol')),pp('vehicle-',c('h2','bev','phev','flex')),'bev-penetration','blend-wall')
#scens <- c('base')
####################################################################################################
# Configurations
####################################################################################################
make.plots <- F
# tool for pulling economic params
get.param <- function(param.name){
inputs[['EconomicAssumptions']][Name==param.name,list(Value)]$Value
}
load.inputs <- function(sheet){
inputs[[sheet]] <<- data.table(read.xls(input.file,sheet,stringsAsFactors=F,skip=1))
}
sheets <- c("MarketStructure","AltFuelPenetrationLimits","FuelTimeSeries","FuelRegionalMarkup","DistributionCosts","VehicleCosts","CarbonIntensity","EconomicAssumptions","CPI","VehicleLifetimeAndVMT")
# my fav color scheme
my.colors <- c(blue='#377eb8',green='#227222',orange='#C66200',purple='#470467',red='#B30C0C',yellow='#C6A600',light.green='#C0E0C0',magenta='#D0339D',dark.blue='#23128F',brown='#542D06',grey='#8A8A8A',light.yellow='#FFE664',light.purple='#9C50C0',light.orange='#FFB164')
alt.colors <- c(BEV='blue',PHEV='dark.blue',H2='red',
CanolaBiodiesel='light.green',SoyBiodiesel='green',SoyRenewableDiesel='grey',TallowRenewableDiesel='brown',UCOBiodiesel='light.purple',
SugarcaneEthanol='yellow',CornEthanolDryMill='grey',FlexCornEthanolDryMill='magenta',FlexSorghumEthanol='light.orange',FlexSugarcaneEthanol='light.yellow',SorghumEthanol='orange')
alt.colors <- data.frame(AltKey=names(alt.colors),color=alt.colors,color.hex=my.colors[alt.colors])
alt.type.colors <- c(PEV='blue',H2='red',Biodiesel='green',Ethanol='yellow',Flex='light.yellow')
alt.type.colors <- data.frame(AltKey=names(alt.type.colors),color=alt.type.colors,color.hex=my.colors[alt.type.colors])
amort.veh.cost <- function(veh.cost,tot.vmt,seg.vmt,mj.per.mile){
lifetime.in.segment <- tot.vmt / seg.vmt
overnight.cap <- (1-get.param('FractionFinancedVeh')) * veh.cost
loan.principal <- get.param('FractionFinancedVeh') * veh.cost
amort.npv <- apply(cbind(loan.principal,overnight.cap,lifetime.in.segment),1,amort.the.loan)
amort.npv.per.MJ <- amort.npv / seg.vmt / mj.per.mile
amort.npv.per.MJ
}
amort.the.loan <- function(x){
loan.principal <- x[1]
overnight.cap <- x[2]
lifetime.in.segment <- x[3]
loan.payments <- rep(loan.principal * get.param('FinanceRateVeh') / (1 - (1+get.param('FinanceRateVeh'))^-get.param('FinanceTermVeh')),get.param('FinanceTermVeh'))
loan.payments.disc <- loan.payments / (1 + get.param('DiscountRate'))^(1:(get.param('FinanceTermVeh')))
amort.npv <- (overnight.cap + sum(loan.payments.disc))*(get.param('DiscountRate')*(1+get.param('DiscountRate'))^lifetime.in.segment)/((1+get.param('DiscountRate'))^lifetime.in.segment - 1)
amort.npv
}
setup.pricing <- function(){
station.cost <<- 1
overnight.cap <<- (1-get.param('FractionFinancedDist')) * station.cost
loan.principal <<- get.param('FractionFinancedDist') * station.cost
loan.payments <<- rep(loan.principal * get.param('FinanceRateDist') / (1 - (1+get.param('FinanceRateDist'))^-get.param('FinanceTermDist')),get.param('FinanceTermDist'))
loan.payments.disc <<- loan.payments / (1 + get.param('DiscountRate'))^(0:(get.param('FinanceTermDist')-1))
amort.dist.ratio <<- (overnight.cap + sum(loan.payments.disc))*(get.param('DiscountRate')*(1+get.param('DiscountRate'))^get.param('DistributionLifetime'))/((1+get.param('DiscountRate'))^get.param('DistributionLifetime') - 1) / 1e3 # 1e3 converts from GJ to MJ
pens <<- inputs[['AltFuelPenetrationLimits']]
fuel.markup <<- inputs[['FuelRegionalMarkup']]
setkey(fuel.markup,Replaces)
carb <<- copy(inputs[['CarbonIntensity']])
gas.carb <<- carb[AltFuel=='Gas']$Intensity
gas.mj.per.gal <<- carb[AltFuel=='Gas']$ConversionFactor
diesel.carb <<- carb[AltFuel=='Diesel']$Intensity
flex.carb <<- carb[AltFuelType=='Ethanol']
flex.blend.by.energy <<- cbind(flex.carb$ConversionFactor * get.param('FlexBlend'),(1-get.param('FlexBlend'))* gas.mj.per.gal)
flex.blend.by.energy <<- as.data.frame(t(apply(flex.blend.by.energy,1,function(x){ x/sum(x) })))
names(flex.blend.by.energy) <<- c('flex','gas')
flex.carb[,':='(AltFuelType='Flex',Intensity=Intensity*flex.blend.by.energy$flex + gas.carb*flex.blend.by.energy$gas)]
eth.blend.by.energy <<- cbind(carb[AltFuelType=='Ethanol']$ConversionFactor * get.param('EthanolBlend'),(1-get.param('EthanolBlend'))* gas.mj.per.gal)
eth.blend.by.energy <<- as.data.frame(t(apply(eth.blend.by.energy,1,function(x){ x/sum(x) })))
names(eth.blend.by.energy) <<- c('eth','gas')
carb[AltFuelType=='Ethanol',':='(Intensity=Intensity*eth.blend.by.energy$eth + gas.carb*eth.blend.by.energy$gas)]
phev.carb<<- carb[AltFuelType=='Electricity']
phev.carb[,':='(AltFuelType='PHEV',AltFuel='PHEV',Intensity=Intensity*get.param('PHEVBlend')+gas.carb*(1-get.param('PHEVBlend')),EER=1/((1/EER)*get.param('PHEVBlend')+(1-get.param('PHEVBlend'))))]
carb[AltFuelType=='Biodiesel',':='(Intensity=Intensity*get.param('BiodieselBlend')+diesel.carb*(1-get.param('BiodieselBlend')))]
carb <<- rbindlist(list(carb,flex.carb,phev.carb),use.names=T,fill=T)
carb[AltFuelType=='Electricity',':='(AltFuelType='BEV',AltFuel='BEV')]
mark <<- inputs[['MarketStructure']]
ghg.goal <<- sum(mark$GHG) - (sum(mark[FuelType=='Gas']$EnergyDemand*1e3)*get.param('TargetGasolineIntensity') + sum(mark[FuelType=='Diesel']$EnergyDemand*1e3)*get.param('TargetDieselIntensity'))/1e6
fuel.price.m <<- melt(inputs[['FuelTimeSeries']],id.vars=c('Year','Quarter'))
setkey(fuel.price.m,variable)
fuel.price <<- fuel.price.m[Year>=2010,list(PriceAvg=mean(value,na.rm=T)),by='variable']
fuel.price[,FuelType:=carb$AltFuelType[match(variable,carb$AltFuel)]]
fuel.price[,':='(AltFuel=variable,variable=NULL)]
fuel.price[,Replaces:='None']
fuel.price[FuelType=='Gas' | FuelType=='Ethanol',Replaces:='Gas']
fuel.price[FuelType=='Diesel' | FuelType=='Biodiesel',Replaces:='Diesel']
fuel.price[FuelType=='H2',Replaces:='H2']
setkey(fuel.price,Replaces)
fuel.price[,PriceRV:=PriceAvg]
elec.price <<- fuel.price[AltFuel=='Electricity']$PriceRV
gas.price <<- fuel.price[AltFuel=='Gas']$PriceRV
diesel.price <<- fuel.price[AltFuel=='Diesel']$PriceRV
# Blend Flex and Ethanol
setkey(fuel.price,FuelType,AltFuel)
flex.prices <<- copy(fuel.price[FuelType=='Ethanol'])
flex.prices[,':='(FuelType='Flex',PriceRV=PriceRV*flex.blend.by.energy$flex+gas.price*flex.blend.by.energy$gas)]
fuel.price[FuelType=='Ethanol',':='(PriceRV=PriceRV*eth.blend.by.energy$eth+gas.price*eth.blend.by.energy$gas)]
# Blend PHEV
phev.price <<- data.table(AltFuel='PHEV',FuelType='PHEV',Replaces='None',PriceRV=elec.price * get.param('PHEVBlend') + gas.price * (1 - get.param('PHEVBlend')))
# Blend Biodiesel
fuel.price[FuelType=='Biodiesel',':='(PriceRV=PriceRV*get.param('BiodieselBlend')+diesel.price*(1-get.param('BiodieselBlend')))]
# Rename Elec to BEV
fuel.price[AltFuel=='Electricity',':='(FuelType='BEV',AltFuel='BEV')]
fuel.price <<- rbindlist(list(fuel.price,flex.prices,phev.price),use.names=T,fill=T)
setkey(fuel.price,Replaces)
fuel.price.by.county <<- fuel.markup[fuel.price,allow.cart=T]
fuel.price.by.county[is.na(Markup),Markup:=0]
fuel.price.by.county[,FuelCost:=PriceRV+Markup]
# Now EER weight the cost using the LDV value for H2
setkey(carb,AltFuel,IntensityEER) # need this to get H2 in correct order
setkey(carb,AltFuel)
setkey(fuel.price.by.county,AltFuel)
fuel.price.by.county <<- unique(carb)[,list(AltFuel,EER)][fuel.price.by.county]
fuel.price.by.county[,FuelCost:=FuelCost/EER]
# Join the conventional prices to market segments for later use in making the fuel cost incremental
mark <<- copy(mark)
setkey(mark,County,FuelType)
setkey(fuel.price.by.county,County,FuelType)
mark <<- fuel.price.by.county[mark,list(County,FuelType,VehicleType,VehicleStatus,NumVehicles,SegmentVMT,EnergyDemand,GHG,travel.demand,FuelCost)]
mark[,':='(ConventionalFuelCost=FuelCost,FuelCost=NULL)]
# Now drop the conventionals
fuel.price.by.county <<- fuel.price.by.county[FuelType!="Gas" & FuelType!="Diesel"]
fuel.price.by.county[,':='(AltFuelType=FuelType,FuelType=NULL)]
###
# Sample and amortized the station cost into 2014$ / MJ
###
station.costs <<- inputs[['DistributionCosts']]
station.costs[is.na(DistCostSD),DistCostSD:=0.25*DistCostAvg]
station.costs[,DistCostRV:=DistCostAvg]
station.costs[,StationCost:=DistCostRV * amort.dist.ratio]
setkey(carb,AltFuelType,IntensityEER) # need this to get H2 in correct order
setkey(carb,AltFuelType)
setkey(station.costs,AltFuelType)
station.costs <<- unique(carb)[,list(AltFuelType,EER)][station.costs]
station.costs[,StationCost:=StationCost/EER]
###
# Sample and amortize the vehicle costs into 2014$ / MJ
###
veh.costs <<- inputs[['VehicleCosts']]
setkey(veh.costs,VehicleType)
setkey(mark,VehicleType)
veh.costs <<- mark[VehicleStatus=='New'][veh.costs, allow.cartesian=T]
veh.costs[,SegmentVMT.per.veh.per.yr:=SegmentVMT/NumVehicles]
tot.vmt <<- inputs[['VehicleLifetimeAndVMT']]
# we need to base lifetime vmt off of the appropriate fuel type
veh.costs[,FuelTypeForLifetime:=FuelType]
veh.costs[AltFuelType%in%c('BEV','PHEV','Flex'),FuelTypeForLifetime:='Gas']
tot.vmt[,FuelTypeForLifetime:=FuelType]
setkey(tot.vmt,FuelTypeForLifetime,VehicleType)
setkey(veh.costs,FuelTypeForLifetime,VehicleType)
veh.costs <<- tot.vmt[veh.costs]
veh.costs[,':='(FuelType=i.FuelType,i.FuelType=NULL)]
# we need to get the segment vmt aggregated across vehicle status
setkey(mark,County,FuelType,VehicleType)
agg.seg.vmt <<- mark[,list(AggSegmentVMTPerVeh=sum(SegmentVMT)/sum(NumVehicles)),by=c('County','FuelType','VehicleType')]
setkey(veh.costs,County,FuelType,VehicleType)
veh.costs <<- agg.seg.vmt[veh.costs,allow.cartesian=T]
setkey(veh.costs,AltFuelType,VehicleType)
setkey(carb,AltFuelType,VehicleType)
veh.costs <<- unique(carb)[,list(AltFuelType,VehicleType,EER)][veh.costs]
veh.costs[is.na(EER),EER:=1]
veh.costs[,MJ.EER.per.mile:=EnergyDemand / SegmentVMT * 1000]
veh.costs[,cost.per.MJ.EER := amort.veh.cost(VehicleCostAvg,VMTPerLifetime,AggSegmentVMTPerVeh,MJ.EER.per.mile)]
veh.costs[,cost.per.MJ.EER.ratio := cost.per.MJ.EER / VehicleCostAvg]
veh.costs[,':='(VehicleCost=VehicleCostAvg * cost.per.MJ.EER.ratio)]
return(NULL)
}
##########################
# Plot Functions
##########################
plot.mac <- function(the.macc,col.to.plot='MAC',errorbars=F,the.title='',y.lim=500){
to.use <- streval(pp('the.macc[!is.nan(',col.to.plot,') & !',col.to.plot,'==Inf]'))
to.use[,AbatementPotential:=AbatementPotential/1e3]
streval(pp('setkey(to.use,',col.to.plot,')'))
to.use[,key:=factor(pp(ifelse(AltFuelType=='Flex','Flex',''),AltFuel,' - ',County,' - ',VehicleStatus,FuelType,VehicleType))]
to.use[,key:=factor(to.use$key, to.use$key)] # reorders the levels to match their rank
to.use[,x.pos:=0.5*(cumsum(AbatementPotential)+cumsum(c(0, head(AbatementPotential,-1))))]
to.use <- streval(pp('to.use[',col.to.plot,'<',y.lim,']'))
p <- streval(pp('ggplot(to.use,aes(x=x.pos,label=key,width=AbatementPotential,y=',col.to.plot,',fill=AltFuel))'))
p <- p + geom_bar(stat='identity',position='identity',colour='black',linetype='blank')+
theme_bw()+
geom_vline(xintercept=ghg.goal/1e3,color=my.colors['red'],linetype='longdash')+
scale_fill_manual(values=as.character(alt.colors$color.hex[match(sort(u(to.use$AltFuel)),alt.colors$AltKey)]))
if(errorbars & pp(col.to.plot,'.sd') %in% names(to.use)){
max.y <- streval(pp('max(to.use$',col.to.plot,' + to.use$',col.to.plot,'.sd)'))
p <- p + geom_point()+
geom_errorbar(aes(ymin=MAC-MAC.sd,ymax=MAC+MAC.sd,width=0))+
scale_y_continuous(limit=c(-750,max.y))
}
if(col.to.plot == 'MAC'){
p <- p + labs(x='GHG Abatement (1000 tons CO2eq)',y='Marginal Abatement Cost ($/ton CO2eq)',fill='County',title=the.title)
}else{
p <- p + labs(x='GHG Abatement (1000 tons CO2eq)',y='Marginal Intensity Abatement Cost ($1000/(g/MJ))',fill='County',title=the.title)
}
print(p)
p
}
all.stats <- data.table()
scen <- scens[1]
for(scen in scens){
scenario.dir <- pp(afrp.shared,'Task-2.1-AFI-Deployment-Assessment/AFCI_Porfolio_Model/',scen,'/')
exper <- list(Factors=list(list(Name="Base",Levels=c('Base'))))
if(file.exists(pp(scenario.dir,'exper.yaml'))){
exper <- yaml.load(readChar(pp(scenario.dir,'exper.yaml'),file.info(pp(scenario.dir,'exper.yaml'))$size))
}
if("InputFile" %in% names(exper)){
input.file <- pp(scenario.dir,exper$InputFile)
}else{
input.file <- pp(scenario.dir,'AFCI_Portfolio_Inputs.xlsx')
}
plot.dir <- pp(plot.dir.base,scen,'/')
make.dir(plot.dir)
load(file=pp(scenario.dir,'results.Rdata'))
level.combs <- results$level.combs
names(level.combs) <- str_replace_all(names(level.combs)," ","_")
num.factors <- ncol(level.combs)
all.avg.maccs <- data.table()
level.i <- 1
####################################################################################################
# Factorial loop
####################################################################################################
for(level.i in 1:nrow(level.combs)){
my.cat(pp(pp(names(level.combs),":",level.combs[level.i,]),collapse=' X '))
inputs<-list()
for(sheet in sheets){
load.inputs(sheet)
}
for(fac.i in 1:num.factors){
if('Scale' %in% names(exper$Factors[[fac.i]])){
sheet <- names(exper$Factors[[fac.i]]$Scale)
lev.1d.i <- which(exper$Factors[[fac.i]]$Levels == level.combs[level.i,fac.i])
if('Row' %in% names(exper$Factors[[fac.i]]$Scale[[sheet]])){
column <- names(exper$Factors[[fac.i]]$Scale[[sheet]])[!names(exper$Factors[[fac.i]]$Scale[[sheet]])=='Row']
col.names <- names(exper$Factors[[fac.i]]$Scale[[sheet]]$Row)
row.keys <- sapply(exper$Factors[[fac.i]]$Scale[[sheet]]$Row,function(x){ x })
streval(pp('inputs[["',sheet,'"]][',pp(col.names,'=="',row.keys,'"',collapse="&"),',',column,':=',column,'*',exper$Factors[[fac.i]]$Scale[[sheet]][[column]][[lev.1d.i]],']'))
}else{
column <- names(exper$Factors[[fac.i]]$Scale[[sheet]])
streval(pp('inputs[["',sheet,'"]][,',column,':=',column,'*',exper$Factors[[fac.i]]$Scale[[sheet]][[column]][[lev.1d.i]],']'))
}
}
}
setup.pricing()
the.macc <- results$the.macc[[level.i]]
mac.results <- results$mac.results[[level.i]]
####################################################
# Summarize and store the avg MACC and key stats
####################################################
# for incomplete results, constrain the inds
inds <- which(!is.na(mac.results[1,,2]))
# Find average cost/rank
setkey(the.macc,VehicleStatus,VehicleType,FuelType,County,AltFuelType,AltFuel)
avg.results <- the.macc[,list(VehicleStatus,VehicleType,FuelType,County,AltFuelType,AltFuel,EnergyDemand,GHG)]
avg.results[,':='(MAC=apply(mac.results[,inds,2],1,mean,na.rm=T),MAC.sd=apply(mac.results[,inds,2],1,sd),MIAC=apply(mac.results[,inds,4],1,mean,na.rm=T),MIAC.sd=apply(mac.results[,inds,4],1,sd),AbatementPotential=apply(mac.results[,inds,3],1,mean))]
avg.results <- avg.results[!is.nan(MAC) & !MAC==Inf]
avg.results[,AbatementPotential:=AbatementPotential/1e3]
avg.results[,rank:=rank(MAC,ties.method='random')]
n.trials <- dim(mac.results)[2]
# Report the total cost of the solution and the conventional cost by comparison
setkey(avg.results,MAC)
avg.results[,CumulativeAbatement:=cumsum(AbatementPotential)]
avg.results[,TotalCost:=MAC * AbatementPotential / 1e3] # AbatementPotential is in K-tons, so this puts cost into $M
mark[,TotalCost:=EnergyDemand*ConventionalFuelCost * 1e3 / 1e6] # puts cost into $M
avg.results[,Penetration:=AbatementPotential*1e3/GHG]
avg.results[,EnergyPenetrated:=EnergyDemand*Penetration]
avg.results[,MJToGalFactor:=c('Gas'=115.63,'Diesel'=134)[FuelType]]
avg.results[,GalPenetrated:=EnergyPenetrated*1e3/MJToGalFactor]
stats <- data.table(WtAvgMAC=avg.results[CumulativeAbatement<ghg.goal/1e3,list(WtAvg=weighted.mean(MAC,AbatementPotential))]$WtAvg,NetCost=sum(avg.results[CumulativeAbatement <= ghg.goal/1e3]$TotalCost),BAUCost=sum(mark$TotalCost))
setkey(avg.results,MAC)
avg.results[,x.pos:=0.5*(cumsum(AbatementPotential)+cumsum(c(0, head(AbatementPotential,-1))))]
avg.results[,WtAvgMAC:=stats$WtAvgMAC]
for(fac.i in 1:num.factors){
fac.name <- names(level.combs)[fac.i]
streval(pp('avg.results[,',fac.name,':="',level.combs[level.i,fac.i],'"]'))
streval(pp('stats[,":="(Factor="',fac.name,'",Level="',level.combs[level.i,fac.i],'")]'))
}
all.avg.maccs <- rbindlist(list(all.avg.maccs,avg.results),use.names=T,fill=T)
all.stats <- rbindlist(list(all.stats,stats),use.names=T,fill=T)
}
all.avg.maccs[,key:=factor(pp(ifelse(AltFuelType=='Flex','Flex',''),AltFuel))]
for(fac.i in 1:num.factors){
fac.name <- names(level.combs)[fac.i]
streval(pp('all.avg.maccs[,',fac.name,':=factor(',fac.name,',level.combs[,',fac.i,'])]'))
}
if(make.plots){
to.use <- all.avg.maccs[AbatementPotential>0]
factor.name <- names(level.combs)[1]
streval(pp('setkey(to.use,',factor.name,',MAC)'))
to.use[,first.over.target:=CumulativeAbatement >= ghg.goal/1e3]
streval(pp('to.use[,first.over.target:=(first.over.target==T & c(F,head(first.over.target,-1))==F),by="',factor.name,'"]'))
to.use <- to.use[CumulativeAbatement <= ghg.goal/1e3 | first.over.target==T]
setkey(to.use,MAC)
alt.order <- to.use[,list(key=key[1]),by='key']
to.use[,key:=factor(key,alt.order$key)]
#to.use[abs(MAC)>1000,MAC:=1000*sign(MAC)]
p <- ggplot(to.use,aes(x=x.pos,label=key,width=AbatementPotential,y=MAC,fill=key))+
geom_bar(stat='identity',position='identity',colour='black',linetype='blank')+
theme_bw()+
geom_hline(aes(yintercept=WtAvgMAC),colour=my.colors['grey'],linetype='longdash')+
geom_vline(xintercept=ghg.goal/1e3,color=my.colors['red'],linetype='longdash')+
coord_cartesian(xlim = c(-10,ghg.goal/1e3+10),ylim=c(max(-1000,min(to.use$MAC))-20,min(1000,max(to.use$MAC))+20))+
scale_fill_manual(values=as.character(alt.colors$color.hex[match(sort(u(to.use$key)),alt.colors$AltKey)]))+
labs(x='GHG Abatement (kt CO2eq)',y='Marginal Abatement Cost ($/t CO2eq)',fill='Alternative',title=pp(exper$Name,': Average MACCs (',n.trials,' Trials)'))
p <- p + streval(pp('facet_wrap(~',factor.name,')'))
if(nrow(level.combs)==3){
ggsave(file=pp(plot.dir,'macc-avg.pdf'),plot=p,width=12,height=4)
}else if(nrow(level.combs)==4 | nrow(level.combs)==1){
ggsave(file=pp(plot.dir,'macc-avg.pdf'),plot=p,width=12,height=8)
}
}
}
# stats in slightly more useable form for comparing
#cast(melt(all.stats,measure.vars=c('WtAvgMAC','NetCost')),Factor + variable ~ Level)
#cast(melt(all.stats,measure.vars=c('WtAvgMAC','NetCost')),Factor ~ variable + Level)
|
/sensitivity.R
|
no_license
|
colinsheppard/smacc
|
R
| false | false | 20,520 |
r
|
####################################################################################################
# Load libs and data
####################################################################################################
load.libraries(c('stringr','truncnorm','reshape2','gdata','mvtnorm','gtools','yaml','GGally','stringr'))
installXLSXsupport()
#scenario.dir <- pp(afrp.shared,'Task-2.1-AFI-Deployment-Assessment/AFCI_Porfolio_Model/base/')
scenario.dir <- pp(afrp.shared,'Task-2.1-AFI-Deployment-Assessment/AFCI_Porfolio_Model/gas-experiment/')
base.scenario.dir <- pp(afrp.shared,'Task-2.1-AFI-Deployment-Assessment/AFCI_Porfolio_Model/base/')
plot.dir.base <- pp(afrp.shared,'Task-2.1-AFI-Deployment-Assessment/plots/')
#scens <- c('fuel-gasoline-price')
#scens <- c('fuel-canola-price','fuel-elec-price','fuel-h2-price','fuel-sorghum-price','fuel-sugarcane-price')
#scens <- c('discount-rate')
#scens <- pp('distrib-',c('h2','bev','phev','flex','ethanol'))
#scens <- pp('vehicle-',c('h2','bev','phev','flex'))
#scens <- c('bev-penetration')
scens <- c('blend-wall')
#scens <- c('base','fuel-gasoline-price','fuel-canola-price','fuel-elec-price','fuel-h2-price','fuel-sorghum-price','fuel-sugarcane-price','discount-rate',pp('distrib-',c('h2','bev','phev','flex','ethanol')),pp('vehicle-',c('h2','bev','phev','flex')),'bev-penetration','blend-wall')
#scens <- c('base')
####################################################################################################
# Configurations
####################################################################################################
make.plots <- F
# tool for pulling economic params
get.param <- function(param.name){
inputs[['EconomicAssumptions']][Name==param.name,list(Value)]$Value
}
load.inputs <- function(sheet){
inputs[[sheet]] <<- data.table(read.xls(input.file,sheet,stringsAsFactors=F,skip=1))
}
sheets <- c("MarketStructure","AltFuelPenetrationLimits","FuelTimeSeries","FuelRegionalMarkup","DistributionCosts","VehicleCosts","CarbonIntensity","EconomicAssumptions","CPI","VehicleLifetimeAndVMT")
# my fav color scheme
my.colors <- c(blue='#377eb8',green='#227222',orange='#C66200',purple='#470467',red='#B30C0C',yellow='#C6A600',light.green='#C0E0C0',magenta='#D0339D',dark.blue='#23128F',brown='#542D06',grey='#8A8A8A',light.yellow='#FFE664',light.purple='#9C50C0',light.orange='#FFB164')
alt.colors <- c(BEV='blue',PHEV='dark.blue',H2='red',
CanolaBiodiesel='light.green',SoyBiodiesel='green',SoyRenewableDiesel='grey',TallowRenewableDiesel='brown',UCOBiodiesel='light.purple',
SugarcaneEthanol='yellow',CornEthanolDryMill='grey',FlexCornEthanolDryMill='magenta',FlexSorghumEthanol='light.orange',FlexSugarcaneEthanol='light.yellow',SorghumEthanol='orange')
alt.colors <- data.frame(AltKey=names(alt.colors),color=alt.colors,color.hex=my.colors[alt.colors])
alt.type.colors <- c(PEV='blue',H2='red',Biodiesel='green',Ethanol='yellow',Flex='light.yellow')
alt.type.colors <- data.frame(AltKey=names(alt.type.colors),color=alt.type.colors,color.hex=my.colors[alt.type.colors])
amort.veh.cost <- function(veh.cost,tot.vmt,seg.vmt,mj.per.mile){
lifetime.in.segment <- tot.vmt / seg.vmt
overnight.cap <- (1-get.param('FractionFinancedVeh')) * veh.cost
loan.principal <- get.param('FractionFinancedVeh') * veh.cost
amort.npv <- apply(cbind(loan.principal,overnight.cap,lifetime.in.segment),1,amort.the.loan)
amort.npv.per.MJ <- amort.npv / seg.vmt / mj.per.mile
amort.npv.per.MJ
}
amort.the.loan <- function(x){
loan.principal <- x[1]
overnight.cap <- x[2]
lifetime.in.segment <- x[3]
loan.payments <- rep(loan.principal * get.param('FinanceRateVeh') / (1 - (1+get.param('FinanceRateVeh'))^-get.param('FinanceTermVeh')),get.param('FinanceTermVeh'))
loan.payments.disc <- loan.payments / (1 + get.param('DiscountRate'))^(1:(get.param('FinanceTermVeh')))
amort.npv <- (overnight.cap + sum(loan.payments.disc))*(get.param('DiscountRate')*(1+get.param('DiscountRate'))^lifetime.in.segment)/((1+get.param('DiscountRate'))^lifetime.in.segment - 1)
amort.npv
}
setup.pricing <- function(){
station.cost <<- 1
overnight.cap <<- (1-get.param('FractionFinancedDist')) * station.cost
loan.principal <<- get.param('FractionFinancedDist') * station.cost
loan.payments <<- rep(loan.principal * get.param('FinanceRateDist') / (1 - (1+get.param('FinanceRateDist'))^-get.param('FinanceTermDist')),get.param('FinanceTermDist'))
loan.payments.disc <<- loan.payments / (1 + get.param('DiscountRate'))^(0:(get.param('FinanceTermDist')-1))
amort.dist.ratio <<- (overnight.cap + sum(loan.payments.disc))*(get.param('DiscountRate')*(1+get.param('DiscountRate'))^get.param('DistributionLifetime'))/((1+get.param('DiscountRate'))^get.param('DistributionLifetime') - 1) / 1e3 # 1e3 converts from GJ to MJ
pens <<- inputs[['AltFuelPenetrationLimits']]
fuel.markup <<- inputs[['FuelRegionalMarkup']]
setkey(fuel.markup,Replaces)
carb <<- copy(inputs[['CarbonIntensity']])
gas.carb <<- carb[AltFuel=='Gas']$Intensity
gas.mj.per.gal <<- carb[AltFuel=='Gas']$ConversionFactor
diesel.carb <<- carb[AltFuel=='Diesel']$Intensity
flex.carb <<- carb[AltFuelType=='Ethanol']
flex.blend.by.energy <<- cbind(flex.carb$ConversionFactor * get.param('FlexBlend'),(1-get.param('FlexBlend'))* gas.mj.per.gal)
flex.blend.by.energy <<- as.data.frame(t(apply(flex.blend.by.energy,1,function(x){ x/sum(x) })))
names(flex.blend.by.energy) <<- c('flex','gas')
flex.carb[,':='(AltFuelType='Flex',Intensity=Intensity*flex.blend.by.energy$flex + gas.carb*flex.blend.by.energy$gas)]
eth.blend.by.energy <<- cbind(carb[AltFuelType=='Ethanol']$ConversionFactor * get.param('EthanolBlend'),(1-get.param('EthanolBlend'))* gas.mj.per.gal)
eth.blend.by.energy <<- as.data.frame(t(apply(eth.blend.by.energy,1,function(x){ x/sum(x) })))
names(eth.blend.by.energy) <<- c('eth','gas')
carb[AltFuelType=='Ethanol',':='(Intensity=Intensity*eth.blend.by.energy$eth + gas.carb*eth.blend.by.energy$gas)]
phev.carb<<- carb[AltFuelType=='Electricity']
phev.carb[,':='(AltFuelType='PHEV',AltFuel='PHEV',Intensity=Intensity*get.param('PHEVBlend')+gas.carb*(1-get.param('PHEVBlend')),EER=1/((1/EER)*get.param('PHEVBlend')+(1-get.param('PHEVBlend'))))]
carb[AltFuelType=='Biodiesel',':='(Intensity=Intensity*get.param('BiodieselBlend')+diesel.carb*(1-get.param('BiodieselBlend')))]
carb <<- rbindlist(list(carb,flex.carb,phev.carb),use.names=T,fill=T)
carb[AltFuelType=='Electricity',':='(AltFuelType='BEV',AltFuel='BEV')]
mark <<- inputs[['MarketStructure']]
ghg.goal <<- sum(mark$GHG) - (sum(mark[FuelType=='Gas']$EnergyDemand*1e3)*get.param('TargetGasolineIntensity') + sum(mark[FuelType=='Diesel']$EnergyDemand*1e3)*get.param('TargetDieselIntensity'))/1e6
fuel.price.m <<- melt(inputs[['FuelTimeSeries']],id.vars=c('Year','Quarter'))
setkey(fuel.price.m,variable)
fuel.price <<- fuel.price.m[Year>=2010,list(PriceAvg=mean(value,na.rm=T)),by='variable']
fuel.price[,FuelType:=carb$AltFuelType[match(variable,carb$AltFuel)]]
fuel.price[,':='(AltFuel=variable,variable=NULL)]
fuel.price[,Replaces:='None']
fuel.price[FuelType=='Gas' | FuelType=='Ethanol',Replaces:='Gas']
fuel.price[FuelType=='Diesel' | FuelType=='Biodiesel',Replaces:='Diesel']
fuel.price[FuelType=='H2',Replaces:='H2']
setkey(fuel.price,Replaces)
fuel.price[,PriceRV:=PriceAvg]
elec.price <<- fuel.price[AltFuel=='Electricity']$PriceRV
gas.price <<- fuel.price[AltFuel=='Gas']$PriceRV
diesel.price <<- fuel.price[AltFuel=='Diesel']$PriceRV
# Blend Flex and Ethanol
setkey(fuel.price,FuelType,AltFuel)
flex.prices <<- copy(fuel.price[FuelType=='Ethanol'])
flex.prices[,':='(FuelType='Flex',PriceRV=PriceRV*flex.blend.by.energy$flex+gas.price*flex.blend.by.energy$gas)]
fuel.price[FuelType=='Ethanol',':='(PriceRV=PriceRV*eth.blend.by.energy$eth+gas.price*eth.blend.by.energy$gas)]
# Blend PHEV
phev.price <<- data.table(AltFuel='PHEV',FuelType='PHEV',Replaces='None',PriceRV=elec.price * get.param('PHEVBlend') + gas.price * (1 - get.param('PHEVBlend')))
# Blend Biodiesel
fuel.price[FuelType=='Biodiesel',':='(PriceRV=PriceRV*get.param('BiodieselBlend')+diesel.price*(1-get.param('BiodieselBlend')))]
# Rename Elec to BEV
fuel.price[AltFuel=='Electricity',':='(FuelType='BEV',AltFuel='BEV')]
fuel.price <<- rbindlist(list(fuel.price,flex.prices,phev.price),use.names=T,fill=T)
setkey(fuel.price,Replaces)
fuel.price.by.county <<- fuel.markup[fuel.price,allow.cart=T]
fuel.price.by.county[is.na(Markup),Markup:=0]
fuel.price.by.county[,FuelCost:=PriceRV+Markup]
# Now EER weight the cost using the LDV value for H2
setkey(carb,AltFuel,IntensityEER) # need this to get H2 in correct order
setkey(carb,AltFuel)
setkey(fuel.price.by.county,AltFuel)
fuel.price.by.county <<- unique(carb)[,list(AltFuel,EER)][fuel.price.by.county]
fuel.price.by.county[,FuelCost:=FuelCost/EER]
# Join the conventional prices to market segments for later use in making the fuel cost incremental
mark <<- copy(mark)
setkey(mark,County,FuelType)
setkey(fuel.price.by.county,County,FuelType)
mark <<- fuel.price.by.county[mark,list(County,FuelType,VehicleType,VehicleStatus,NumVehicles,SegmentVMT,EnergyDemand,GHG,travel.demand,FuelCost)]
mark[,':='(ConventionalFuelCost=FuelCost,FuelCost=NULL)]
# Now drop the conventionals
fuel.price.by.county <<- fuel.price.by.county[FuelType!="Gas" & FuelType!="Diesel"]
fuel.price.by.county[,':='(AltFuelType=FuelType,FuelType=NULL)]
###
# Sample and amortized the station cost into 2014$ / MJ
###
station.costs <<- inputs[['DistributionCosts']]
station.costs[is.na(DistCostSD),DistCostSD:=0.25*DistCostAvg]
station.costs[,DistCostRV:=DistCostAvg]
station.costs[,StationCost:=DistCostRV * amort.dist.ratio]
setkey(carb,AltFuelType,IntensityEER) # need this to get H2 in correct order
setkey(carb,AltFuelType)
setkey(station.costs,AltFuelType)
station.costs <<- unique(carb)[,list(AltFuelType,EER)][station.costs]
station.costs[,StationCost:=StationCost/EER]
###
# Sample and amortize the vehicle costs into 2014$ / MJ
###
veh.costs <<- inputs[['VehicleCosts']]
setkey(veh.costs,VehicleType)
setkey(mark,VehicleType)
veh.costs <<- mark[VehicleStatus=='New'][veh.costs, allow.cartesian=T]
veh.costs[,SegmentVMT.per.veh.per.yr:=SegmentVMT/NumVehicles]
tot.vmt <<- inputs[['VehicleLifetimeAndVMT']]
# we need to base lifetime vmt off of the appropriate fuel type
veh.costs[,FuelTypeForLifetime:=FuelType]
veh.costs[AltFuelType%in%c('BEV','PHEV','Flex'),FuelTypeForLifetime:='Gas']
tot.vmt[,FuelTypeForLifetime:=FuelType]
setkey(tot.vmt,FuelTypeForLifetime,VehicleType)
setkey(veh.costs,FuelTypeForLifetime,VehicleType)
veh.costs <<- tot.vmt[veh.costs]
veh.costs[,':='(FuelType=i.FuelType,i.FuelType=NULL)]
# we need to get the segment vmt aggregated across vehicle status
setkey(mark,County,FuelType,VehicleType)
agg.seg.vmt <<- mark[,list(AggSegmentVMTPerVeh=sum(SegmentVMT)/sum(NumVehicles)),by=c('County','FuelType','VehicleType')]
setkey(veh.costs,County,FuelType,VehicleType)
veh.costs <<- agg.seg.vmt[veh.costs,allow.cartesian=T]
setkey(veh.costs,AltFuelType,VehicleType)
setkey(carb,AltFuelType,VehicleType)
veh.costs <<- unique(carb)[,list(AltFuelType,VehicleType,EER)][veh.costs]
veh.costs[is.na(EER),EER:=1]
veh.costs[,MJ.EER.per.mile:=EnergyDemand / SegmentVMT * 1000]
veh.costs[,cost.per.MJ.EER := amort.veh.cost(VehicleCostAvg,VMTPerLifetime,AggSegmentVMTPerVeh,MJ.EER.per.mile)]
veh.costs[,cost.per.MJ.EER.ratio := cost.per.MJ.EER / VehicleCostAvg]
veh.costs[,':='(VehicleCost=VehicleCostAvg * cost.per.MJ.EER.ratio)]
return(NULL)
}
##########################
# Plot Functions
##########################
plot.mac <- function(the.macc,col.to.plot='MAC',errorbars=F,the.title='',y.lim=500){
to.use <- streval(pp('the.macc[!is.nan(',col.to.plot,') & !',col.to.plot,'==Inf]'))
to.use[,AbatementPotential:=AbatementPotential/1e3]
streval(pp('setkey(to.use,',col.to.plot,')'))
to.use[,key:=factor(pp(ifelse(AltFuelType=='Flex','Flex',''),AltFuel,' - ',County,' - ',VehicleStatus,FuelType,VehicleType))]
to.use[,key:=factor(to.use$key, to.use$key)] # reorders the levels to match their rank
to.use[,x.pos:=0.5*(cumsum(AbatementPotential)+cumsum(c(0, head(AbatementPotential,-1))))]
to.use <- streval(pp('to.use[',col.to.plot,'<',y.lim,']'))
p <- streval(pp('ggplot(to.use,aes(x=x.pos,label=key,width=AbatementPotential,y=',col.to.plot,',fill=AltFuel))'))
p <- p + geom_bar(stat='identity',position='identity',colour='black',linetype='blank')+
theme_bw()+
geom_vline(xintercept=ghg.goal/1e3,color=my.colors['red'],linetype='longdash')+
scale_fill_manual(values=as.character(alt.colors$color.hex[match(sort(u(to.use$AltFuel)),alt.colors$AltKey)]))
if(errorbars & pp(col.to.plot,'.sd') %in% names(to.use)){
max.y <- streval(pp('max(to.use$',col.to.plot,' + to.use$',col.to.plot,'.sd)'))
p <- p + geom_point()+
geom_errorbar(aes(ymin=MAC-MAC.sd,ymax=MAC+MAC.sd,width=0))+
scale_y_continuous(limit=c(-750,max.y))
}
if(col.to.plot == 'MAC'){
p <- p + labs(x='GHG Abatement (1000 tons CO2eq)',y='Marginal Abatement Cost ($/ton CO2eq)',fill='County',title=the.title)
}else{
p <- p + labs(x='GHG Abatement (1000 tons CO2eq)',y='Marginal Intensity Abatement Cost ($1000/(g/MJ))',fill='County',title=the.title)
}
print(p)
p
}
all.stats <- data.table()
scen <- scens[1]
for(scen in scens){
scenario.dir <- pp(afrp.shared,'Task-2.1-AFI-Deployment-Assessment/AFCI_Porfolio_Model/',scen,'/')
exper <- list(Factors=list(list(Name="Base",Levels=c('Base'))))
if(file.exists(pp(scenario.dir,'exper.yaml'))){
exper <- yaml.load(readChar(pp(scenario.dir,'exper.yaml'),file.info(pp(scenario.dir,'exper.yaml'))$size))
}
if("InputFile" %in% names(exper)){
input.file <- pp(scenario.dir,exper$InputFile)
}else{
input.file <- pp(scenario.dir,'AFCI_Portfolio_Inputs.xlsx')
}
plot.dir <- pp(plot.dir.base,scen,'/')
make.dir(plot.dir)
load(file=pp(scenario.dir,'results.Rdata'))
level.combs <- results$level.combs
names(level.combs) <- str_replace_all(names(level.combs)," ","_")
num.factors <- ncol(level.combs)
all.avg.maccs <- data.table()
level.i <- 1
####################################################################################################
# Factorial loop
####################################################################################################
for(level.i in 1:nrow(level.combs)){
my.cat(pp(pp(names(level.combs),":",level.combs[level.i,]),collapse=' X '))
inputs<-list()
for(sheet in sheets){
load.inputs(sheet)
}
for(fac.i in 1:num.factors){
if('Scale' %in% names(exper$Factors[[fac.i]])){
sheet <- names(exper$Factors[[fac.i]]$Scale)
lev.1d.i <- which(exper$Factors[[fac.i]]$Levels == level.combs[level.i,fac.i])
if('Row' %in% names(exper$Factors[[fac.i]]$Scale[[sheet]])){
column <- names(exper$Factors[[fac.i]]$Scale[[sheet]])[!names(exper$Factors[[fac.i]]$Scale[[sheet]])=='Row']
col.names <- names(exper$Factors[[fac.i]]$Scale[[sheet]]$Row)
row.keys <- sapply(exper$Factors[[fac.i]]$Scale[[sheet]]$Row,function(x){ x })
streval(pp('inputs[["',sheet,'"]][',pp(col.names,'=="',row.keys,'"',collapse="&"),',',column,':=',column,'*',exper$Factors[[fac.i]]$Scale[[sheet]][[column]][[lev.1d.i]],']'))
}else{
column <- names(exper$Factors[[fac.i]]$Scale[[sheet]])
streval(pp('inputs[["',sheet,'"]][,',column,':=',column,'*',exper$Factors[[fac.i]]$Scale[[sheet]][[column]][[lev.1d.i]],']'))
}
}
}
setup.pricing()
the.macc <- results$the.macc[[level.i]]
mac.results <- results$mac.results[[level.i]]
####################################################
# Summarize and store the avg MACC and key stats
####################################################
# for incomplete results, constrain the inds
inds <- which(!is.na(mac.results[1,,2]))
# Find average cost/rank
setkey(the.macc,VehicleStatus,VehicleType,FuelType,County,AltFuelType,AltFuel)
avg.results <- the.macc[,list(VehicleStatus,VehicleType,FuelType,County,AltFuelType,AltFuel,EnergyDemand,GHG)]
avg.results[,':='(MAC=apply(mac.results[,inds,2],1,mean,na.rm=T),MAC.sd=apply(mac.results[,inds,2],1,sd),MIAC=apply(mac.results[,inds,4],1,mean,na.rm=T),MIAC.sd=apply(mac.results[,inds,4],1,sd),AbatementPotential=apply(mac.results[,inds,3],1,mean))]
avg.results <- avg.results[!is.nan(MAC) & !MAC==Inf]
avg.results[,AbatementPotential:=AbatementPotential/1e3]
avg.results[,rank:=rank(MAC,ties.method='random')]
n.trials <- dim(mac.results)[2]
# Report the total cost of the solution and the conventional cost by comparison
setkey(avg.results,MAC)
avg.results[,CumulativeAbatement:=cumsum(AbatementPotential)]
avg.results[,TotalCost:=MAC * AbatementPotential / 1e3] # AbatementPotential is in K-tons, so this puts cost into $M
mark[,TotalCost:=EnergyDemand*ConventionalFuelCost * 1e3 / 1e6] # puts cost into $M
avg.results[,Penetration:=AbatementPotential*1e3/GHG]
avg.results[,EnergyPenetrated:=EnergyDemand*Penetration]
avg.results[,MJToGalFactor:=c('Gas'=115.63,'Diesel'=134)[FuelType]]
avg.results[,GalPenetrated:=EnergyPenetrated*1e3/MJToGalFactor]
stats <- data.table(WtAvgMAC=avg.results[CumulativeAbatement<ghg.goal/1e3,list(WtAvg=weighted.mean(MAC,AbatementPotential))]$WtAvg,NetCost=sum(avg.results[CumulativeAbatement <= ghg.goal/1e3]$TotalCost),BAUCost=sum(mark$TotalCost))
setkey(avg.results,MAC)
avg.results[,x.pos:=0.5*(cumsum(AbatementPotential)+cumsum(c(0, head(AbatementPotential,-1))))]
avg.results[,WtAvgMAC:=stats$WtAvgMAC]
for(fac.i in 1:num.factors){
fac.name <- names(level.combs)[fac.i]
streval(pp('avg.results[,',fac.name,':="',level.combs[level.i,fac.i],'"]'))
streval(pp('stats[,":="(Factor="',fac.name,'",Level="',level.combs[level.i,fac.i],'")]'))
}
all.avg.maccs <- rbindlist(list(all.avg.maccs,avg.results),use.names=T,fill=T)
all.stats <- rbindlist(list(all.stats,stats),use.names=T,fill=T)
}
all.avg.maccs[,key:=factor(pp(ifelse(AltFuelType=='Flex','Flex',''),AltFuel))]
for(fac.i in 1:num.factors){
fac.name <- names(level.combs)[fac.i]
streval(pp('all.avg.maccs[,',fac.name,':=factor(',fac.name,',level.combs[,',fac.i,'])]'))
}
if(make.plots){
to.use <- all.avg.maccs[AbatementPotential>0]
factor.name <- names(level.combs)[1]
streval(pp('setkey(to.use,',factor.name,',MAC)'))
to.use[,first.over.target:=CumulativeAbatement >= ghg.goal/1e3]
streval(pp('to.use[,first.over.target:=(first.over.target==T & c(F,head(first.over.target,-1))==F),by="',factor.name,'"]'))
to.use <- to.use[CumulativeAbatement <= ghg.goal/1e3 | first.over.target==T]
setkey(to.use,MAC)
alt.order <- to.use[,list(key=key[1]),by='key']
to.use[,key:=factor(key,alt.order$key)]
#to.use[abs(MAC)>1000,MAC:=1000*sign(MAC)]
p <- ggplot(to.use,aes(x=x.pos,label=key,width=AbatementPotential,y=MAC,fill=key))+
geom_bar(stat='identity',position='identity',colour='black',linetype='blank')+
theme_bw()+
geom_hline(aes(yintercept=WtAvgMAC),colour=my.colors['grey'],linetype='longdash')+
geom_vline(xintercept=ghg.goal/1e3,color=my.colors['red'],linetype='longdash')+
coord_cartesian(xlim = c(-10,ghg.goal/1e3+10),ylim=c(max(-1000,min(to.use$MAC))-20,min(1000,max(to.use$MAC))+20))+
scale_fill_manual(values=as.character(alt.colors$color.hex[match(sort(u(to.use$key)),alt.colors$AltKey)]))+
labs(x='GHG Abatement (kt CO2eq)',y='Marginal Abatement Cost ($/t CO2eq)',fill='Alternative',title=pp(exper$Name,': Average MACCs (',n.trials,' Trials)'))
p <- p + streval(pp('facet_wrap(~',factor.name,')'))
if(nrow(level.combs)==3){
ggsave(file=pp(plot.dir,'macc-avg.pdf'),plot=p,width=12,height=4)
}else if(nrow(level.combs)==4 | nrow(level.combs)==1){
ggsave(file=pp(plot.dir,'macc-avg.pdf'),plot=p,width=12,height=8)
}
}
}
# stats in slightly more useable form for comparing
#cast(melt(all.stats,measure.vars=c('WtAvgMAC','NetCost')),Factor + variable ~ Level)
#cast(melt(all.stats,measure.vars=c('WtAvgMAC','NetCost')),Factor ~ variable + Level)
|
library(party)
library(C50)
df = read.csv("Company_Data.csv")
str(df)
head(df)
pairs(df)
range(df$Sales)
median(df$Sales)
sort(df$Sales)
length(df$Sales)
# Less Assume 3 sales segments I may consider low, Medium and High.
# Lets see the cutoff for High sales
sort(df$Sales)[(400/3 * 2)]
# As here cutoff we got is 8.67
# so we can condider that those sales which values are gratter than 8.5 will be considered as High sales and rest will be low sales
SaleC <- ifelse(df$Sales > 8.5,"High","Low")
prop.table(table(SaleC)) # Desired result achieved
comp <- data.frame(SaleC,df[,-1])
set.seed(101);split <- sample(nrow(comp),nrow(comp)*.7,F)
Train_comp <- comp[split,]
Test_comp <- comp[-split,]
#Model Building
model<-C5.0(SaleC~.,data = Train_comp)
#Generate the model summary
summary(model)
#Predict for test data set
prob<-predict.C5.0(model,Test_comp[,-5]) #type ="prob"
#Accuracy of the algorithm
a<-table(Test_comp$Sales,pred)
sum(diag(a))/sum(a)
#Visualize the decision tree
plot(model)
|
/Decision Forest.R
|
no_license
|
AkshayChopade07/R-Codes
|
R
| false | false | 1,034 |
r
|
library(party)
library(C50)
df = read.csv("Company_Data.csv")
str(df)
head(df)
pairs(df)
range(df$Sales)
median(df$Sales)
sort(df$Sales)
length(df$Sales)
# Less Assume 3 sales segments I may consider low, Medium and High.
# Lets see the cutoff for High sales
sort(df$Sales)[(400/3 * 2)]
# As here cutoff we got is 8.67
# so we can condider that those sales which values are gratter than 8.5 will be considered as High sales and rest will be low sales
SaleC <- ifelse(df$Sales > 8.5,"High","Low")
prop.table(table(SaleC)) # Desired result achieved
comp <- data.frame(SaleC,df[,-1])
set.seed(101);split <- sample(nrow(comp),nrow(comp)*.7,F)
Train_comp <- comp[split,]
Test_comp <- comp[-split,]
#Model Building
model<-C5.0(SaleC~.,data = Train_comp)
#Generate the model summary
summary(model)
#Predict for test data set
prob<-predict.C5.0(model,Test_comp[,-5]) #type ="prob"
#Accuracy of the algorithm
a<-table(Test_comp$Sales,pred)
sum(diag(a))/sum(a)
#Visualize the decision tree
plot(model)
|
#### This provides the complete code to read in the household power consumption data
##### from a file saved in the same directory, get the data for the first two days in February 2007
##### then produce the fourth chart (a panel plot of four other graphs) and save it as a .png file.
pwrdata <-read.table("household_power_consumption.txt", sep = ";", na.strings = "?", header = TRUE)
#Convert the date column from a factor to a date
pwrdata$Date = as.Date(pwrdata$Date, format = "%d/%m/%Y")
#Get the data for the first two days in February 2007
thedata<-pwrdata[pwrdata$Date==as.Date("1/2/2007",format = "%d/%m/%Y") | pwrdata$Date==as.Date("2/2/2007",format = "%d/%m/%Y"),]
#Delete the full data set from memory - it is no longer needed
> rm(pwrdata)
#Convert the time column from a factor to a time
tempdate <-as.character(thedata$Date) #string holding the date
temptime <-as.character(thedata$Time) #string holding the time
tempdatetime = paste(tempdate, temptime) #string holding both date and time
thedata$Time <-strptime(tempdatetime, "%Y-%m-%d %H:%M:%S") #Convert the time column
#set up grid for four graphs
par(mfrow = c(2, 2))
#top left panel
plot(thedata$Time,thedata$Global_active_power, xlab = "", ylab = "Global Active Power (kilowatts)",type = "n")
lines(thedata$Time,thedata$Global_active_power)
#top right panel
plot(thedata$Time,thedata$Voltage, ylab = "Voltage", xlab = "datetime",type = "n")
lines(thedata$Time,thedata$Voltage)
#bottom left panel
plot(thedata$Time,thedata$Sub_metering_1, xlab = "", ylab = "Energy sub metering",type = "n")
lines(thedata$Time,thedata$Sub_metering_1)
lines(thedata$Time, thedata$Sub_metering_2, col = "red")
lines(thedata$Time,thedata$Sub_metering_3, col = "blue")
legend("topright", lty = c(1,1,1), col = c("black", "red","blue"), legend = c("Sub_metering_1","Sub_metering_2","Sub_metering_3"), bty = "n", cex = 0.7)
#bottom right panel
plot(thedata$Time,thedata$Global_reactive_power, ylab = "Global_reactive_power", xlab = "datetime",type = "n")
lines(thedata$Time,thedata$Global_reactive_power)
#copy the file to a png file
dev.copy(png, height = 480, width = 480, file = "plot4.png")
dev.off() #close the png device
|
/plot4.R
|
no_license
|
james882/ExData_Plotting1
|
R
| false | false | 2,206 |
r
|
#### This provides the complete code to read in the household power consumption data
##### from a file saved in the same directory, get the data for the first two days in February 2007
##### then produce the fourth chart (a panel plot of four other graphs) and save it as a .png file.
pwrdata <-read.table("household_power_consumption.txt", sep = ";", na.strings = "?", header = TRUE)
#Convert the date column from a factor to a date
pwrdata$Date = as.Date(pwrdata$Date, format = "%d/%m/%Y")
#Get the data for the first two days in February 2007
thedata<-pwrdata[pwrdata$Date==as.Date("1/2/2007",format = "%d/%m/%Y") | pwrdata$Date==as.Date("2/2/2007",format = "%d/%m/%Y"),]
#Delete the full data set from memory - it is no longer needed
> rm(pwrdata)
#Convert the time column from a factor to a time
tempdate <-as.character(thedata$Date) #string holding the date
temptime <-as.character(thedata$Time) #string holding the time
tempdatetime = paste(tempdate, temptime) #string holding both date and time
thedata$Time <-strptime(tempdatetime, "%Y-%m-%d %H:%M:%S") #Convert the time column
#set up grid for four graphs
par(mfrow = c(2, 2))
#top left panel
plot(thedata$Time,thedata$Global_active_power, xlab = "", ylab = "Global Active Power (kilowatts)",type = "n")
lines(thedata$Time,thedata$Global_active_power)
#top right panel
plot(thedata$Time,thedata$Voltage, ylab = "Voltage", xlab = "datetime",type = "n")
lines(thedata$Time,thedata$Voltage)
#bottom left panel
plot(thedata$Time,thedata$Sub_metering_1, xlab = "", ylab = "Energy sub metering",type = "n")
lines(thedata$Time,thedata$Sub_metering_1)
lines(thedata$Time, thedata$Sub_metering_2, col = "red")
lines(thedata$Time,thedata$Sub_metering_3, col = "blue")
legend("topright", lty = c(1,1,1), col = c("black", "red","blue"), legend = c("Sub_metering_1","Sub_metering_2","Sub_metering_3"), bty = "n", cex = 0.7)
#bottom right panel
plot(thedata$Time,thedata$Global_reactive_power, ylab = "Global_reactive_power", xlab = "datetime",type = "n")
lines(thedata$Time,thedata$Global_reactive_power)
#copy the file to a png file
dev.copy(png, height = 480, width = 480, file = "plot4.png")
dev.off() #close the png device
|
test_script <- function() {
library(data.table)
t <- as.data.table(list(
col1 = rep(1,100),
col2 = rep(2,100),
col3 = c(rep("a",50),rep("b",50)),
col4 = c(rep("d",25),rep("e",25),rep("f",25),rep("g",25))
))
pivot_table_gadget(t)
}
|
/R/test_script.R
|
no_license
|
mrhopko/pivotTableFlavoR
|
R
| false | false | 265 |
r
|
test_script <- function() {
library(data.table)
t <- as.data.table(list(
col1 = rep(1,100),
col2 = rep(2,100),
col3 = c(rep("a",50),rep("b",50)),
col4 = c(rep("d",25),rep("e",25),rep("f",25),rep("g",25))
))
pivot_table_gadget(t)
}
|
library("clickstream")
library("arulesSequences")
## Load Data
cls <- readClickstreams(file = "path_to_file", sep = ",", header = F)
cls
summary(cls)
## Fitting Markov Chain
mc <- fitMarkovChain(clickstreamList = cls, order = 3)
options(digits = 2)
##compute transition probabilities
mc
options(digits = 7)
summary(mc)
##plot transition diagram and heatmap
plot(mc)
hmPlot(mc)
##clustering
set.seed(42)
clusters <- clusterClickstreams(clickstreamList = cls, order = 1, centers = 2)
clusters
#To produce the clustering plot of the post in the code of function clusterClickstreams
#add tthe following in line 17:
# ggplot(transitionData, aes(StatesChanged,id, colour=fit$cluster))+geom_point()
summary(clusters)
#Predict clicks
pattern <- new("Pattern", sequence = c("Action14", "Action4"))
resultPattern <- predict(mc, startPattern = pattern, dist = 1) # set dist = n to predict n steps ahead
resultPattern
##cSPACE data mining
frequencyDF <- frequencies(cls)
frequencyDF
trans <- as.transactions(cls)
sequences <- as(cspade(trans, parameter = list(support = 0)), "data.frame")
sequences
|
/clickstream_analysis.R
|
permissive
|
jaradc/ClickstreamAnalysis
|
R
| false | false | 1,096 |
r
|
library("clickstream")
library("arulesSequences")
## Load Data
cls <- readClickstreams(file = "path_to_file", sep = ",", header = F)
cls
summary(cls)
## Fitting Markov Chain
mc <- fitMarkovChain(clickstreamList = cls, order = 3)
options(digits = 2)
##compute transition probabilities
mc
options(digits = 7)
summary(mc)
##plot transition diagram and heatmap
plot(mc)
hmPlot(mc)
##clustering
set.seed(42)
clusters <- clusterClickstreams(clickstreamList = cls, order = 1, centers = 2)
clusters
#To produce the clustering plot of the post in the code of function clusterClickstreams
#add tthe following in line 17:
# ggplot(transitionData, aes(StatesChanged,id, colour=fit$cluster))+geom_point()
summary(clusters)
#Predict clicks
pattern <- new("Pattern", sequence = c("Action14", "Action4"))
resultPattern <- predict(mc, startPattern = pattern, dist = 1) # set dist = n to predict n steps ahead
resultPattern
##cSPACE data mining
frequencyDF <- frequencies(cls)
frequencyDF
trans <- as.transactions(cls)
sequences <- as(cspade(trans, parameter = list(support = 0)), "data.frame")
sequences
|
testlist <- list(m = NULL, repetitions = 0L, in_m = structure(c(2.31584307392677e+77, 9.85751697122895e+295, 1.22810536108214e+146, 4.12396251261199e-221, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(10L, 3L)))
result <- do.call(CNull:::communities_individual_based_sampling_alpha,testlist)
str(result)
|
/CNull/inst/testfiles/communities_individual_based_sampling_alpha/AFL_communities_individual_based_sampling_alpha/communities_individual_based_sampling_alpha_valgrind_files/1615777940-test.R
|
no_license
|
akhikolla/updatedatatype-list2
|
R
| false | false | 348 |
r
|
testlist <- list(m = NULL, repetitions = 0L, in_m = structure(c(2.31584307392677e+77, 9.85751697122895e+295, 1.22810536108214e+146, 4.12396251261199e-221, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(10L, 3L)))
result <- do.call(CNull:::communities_individual_based_sampling_alpha,testlist)
str(result)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/removeSeq.R
\name{removeSeq}
\alias{removeSeq}
\title{Remove sequence}
\usage{
removeSeq(file.list, sequence)
}
\arguments{
\item{file.list}{A list of data frames imported using the LymphoSeq function
readImmunoSeq. "aminoAcid", "count", and "frequencyCount" are required columns.}
\item{sequence}{A character vector of one or more amino acid sequences to
remove from the list of data frames.}
}
\value{
Returns a list of data frames like the one imported except all rows
with the specified amino acid sequence are removed. The frequencyCount is
recalculated.
}
\description{
Removes an amino acid sequence and associated data from all instances within
a list of data frames and then recomputes the frequencyCount.
}
\examples{
file.path <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq")
file.list <- readImmunoSeq(path = file.path)
searchSeq(list = file.list, sequence = "CASSDLIGNGKLFF")
cleansed <- removeSeq(file.list = file.list, sequence = "CASSDLIGNGKLFF")
searchSeq(list = cleansed, sequence = "CASSDLIGNGKLFF")
}
|
/man/removeSeq.Rd
|
no_license
|
davidcoffey/LymphoSeq
|
R
| false | true | 1,130 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/removeSeq.R
\name{removeSeq}
\alias{removeSeq}
\title{Remove sequence}
\usage{
removeSeq(file.list, sequence)
}
\arguments{
\item{file.list}{A list of data frames imported using the LymphoSeq function
readImmunoSeq. "aminoAcid", "count", and "frequencyCount" are required columns.}
\item{sequence}{A character vector of one or more amino acid sequences to
remove from the list of data frames.}
}
\value{
Returns a list of data frames like the one imported except all rows
with the specified amino acid sequence are removed. The frequencyCount is
recalculated.
}
\description{
Removes an amino acid sequence and associated data from all instances within
a list of data frames and then recomputes the frequencyCount.
}
\examples{
file.path <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq")
file.list <- readImmunoSeq(path = file.path)
searchSeq(list = file.list, sequence = "CASSDLIGNGKLFF")
cleansed <- removeSeq(file.list = file.list, sequence = "CASSDLIGNGKLFF")
searchSeq(list = cleansed, sequence = "CASSDLIGNGKLFF")
}
|
# read the dataset from the source text file which was unzip in a local folder
dataset <- read.table(file = "D:/Disco D/data/EDA/Week1/household_power_consumption.txt",
sep=";", stringsAsFactors = FALSE, na.strings = '?', skipNul = TRUE, header = TRUE)
## We will only be using data from the dates 2007-02-01 and 2007-02-02.
subdata <- dataset[dataset$Date %in% c("1/2/2007", "2/2/2007"),]
subdata$Date <- as.Date.character(subdata$Date, format = "%d/%m/%Y")
Sys.setlocale("LC_TIME", "English")
datetime <- strptime(paste(as.character(subdata$Date),subdata$Time), format = "%Y-%m-%d %H:%M:%S")
## Launch the png graphic device
png(filename = "Plot4.png")
## define 2 rows and 2 columns
par(mfrow = c(2,2))
## make the plots
plot(datetime, subdata$Global_active_power, type = "l", xlab = "", ylab = "Global Active Power")
plot(datetime, subdata$Voltage , type = "l", xlab = "datetime", ylab = "Voltage")
with(subdata, plot(datetime, Sub_metering_1, type = "n", xlab="", ylab="Energy sub metering"))
with(subdata, points(datetime, Sub_metering_1, type = "l", main = "Sub_metering_1"))
with(subdata, points(datetime, Sub_metering_2, type = "l", col="red", main = "Sub_metering_2"))
with(subdata, points(datetime, Sub_metering_3, type = "l", col="blue", main = "Sub_metering_3"))
legend("topright", lwd = 2, lty=1, bty="n", col = c("black","red", "blue"),
legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
plot(datetime, subdata$Global_reactive_power , type = "l", xlab = "datetime",
ylab = "Global_reactive_power")
## close graphic device
dev.off()
|
/Plot4.R
|
no_license
|
PattyCh/ExData_Plotting1
|
R
| false | false | 1,599 |
r
|
# read the dataset from the source text file which was unzip in a local folder
dataset <- read.table(file = "D:/Disco D/data/EDA/Week1/household_power_consumption.txt",
sep=";", stringsAsFactors = FALSE, na.strings = '?', skipNul = TRUE, header = TRUE)
## We will only be using data from the dates 2007-02-01 and 2007-02-02.
subdata <- dataset[dataset$Date %in% c("1/2/2007", "2/2/2007"),]
subdata$Date <- as.Date.character(subdata$Date, format = "%d/%m/%Y")
Sys.setlocale("LC_TIME", "English")
datetime <- strptime(paste(as.character(subdata$Date),subdata$Time), format = "%Y-%m-%d %H:%M:%S")
## Launch the png graphic device
png(filename = "Plot4.png")
## define 2 rows and 2 columns
par(mfrow = c(2,2))
## make the plots
plot(datetime, subdata$Global_active_power, type = "l", xlab = "", ylab = "Global Active Power")
plot(datetime, subdata$Voltage , type = "l", xlab = "datetime", ylab = "Voltage")
with(subdata, plot(datetime, Sub_metering_1, type = "n", xlab="", ylab="Energy sub metering"))
with(subdata, points(datetime, Sub_metering_1, type = "l", main = "Sub_metering_1"))
with(subdata, points(datetime, Sub_metering_2, type = "l", col="red", main = "Sub_metering_2"))
with(subdata, points(datetime, Sub_metering_3, type = "l", col="blue", main = "Sub_metering_3"))
legend("topright", lwd = 2, lty=1, bty="n", col = c("black","red", "blue"),
legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
plot(datetime, subdata$Global_reactive_power , type = "l", xlab = "datetime",
ylab = "Global_reactive_power")
## close graphic device
dev.off()
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/cluster_mutual_information.R
\name{cluster_mutual_information}
\alias{cluster_mutual_information}
\title{A MVDA Function}
\usage{
cluster_mutual_information(vc1, vc2, elem)
}
\arguments{
\item{vc1}{a numeric vector of clustering results}
\item{vc2}{a numeric vector of clustering results}
\item{elem}{the matrix representing your dataset}
}
\value{
the mutual information between clustering
}
\description{
This function evaluate the mutual information between two clustering results
}
\keyword{clustering;}
\keyword{entropy}
\keyword{multi-view}
|
/MVDA_package/man/cluster_mutual_information.Rd
|
no_license
|
angy89/MVDA
|
R
| false | false | 636 |
rd
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/cluster_mutual_information.R
\name{cluster_mutual_information}
\alias{cluster_mutual_information}
\title{A MVDA Function}
\usage{
cluster_mutual_information(vc1, vc2, elem)
}
\arguments{
\item{vc1}{a numeric vector of clustering results}
\item{vc2}{a numeric vector of clustering results}
\item{elem}{the matrix representing your dataset}
}
\value{
the mutual information between clustering
}
\description{
This function evaluate the mutual information between two clustering results
}
\keyword{clustering;}
\keyword{entropy}
\keyword{multi-view}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/NegLogInt_Fn.R
\name{NegLogInt_Fn}
\alias{NegLogInt_Fn}
\title{Perform SS implementation of Laplace Approximation}
\usage{
NegLogInt_Fn(File = NA, Input_SD_Group_Vec, CTL_linenum_List, ESTPAR_num_List,
PAR_num_Vec, Int_Group_List = list(1), StartFromPar = TRUE,
Intern = TRUE, ReDoBiasRamp = FALSE, BiasRamp_linenum_Vec = NULL,
CTL_linenum_Type = NULL, systemcmd = FALSE, exe = "ss")
}
\arguments{
\item{File}{Directory containing Stock Synthesis files
(e.g., "C:/Users/James Thorson/Desktop/")}
\item{Input_SD_Group_Vec}{Vector where each element is the standard deviation
for a group of random effects (e.g., a model with a single group of random
effects will have Input_SD_Group_Vec be a vector of length one)}
\item{CTL_linenum_List}{List (same length as \code{Input_SD_Group_Vec}),
where each
element is a vector giving the line number(s) for the random effect standard
deviation parameter or penalty in the CTL file (and where each line will
correspond to a 7-parameter or 14-parameter line).}
\item{ESTPAR_num_List}{List (same length as \code{Input_SD_Group_Vec}),
where each
element is a vector giving the parameter number for the random effect
coefficients in that group of random effects. These "parameter numbers"
correspond to the number of these parameters in the list of parameters in the
".cor" output file.}
\item{PAR_num_Vec}{Vector giving the number in the ".par" vector for each
random effect coefficient.}
\item{Int_Group_List}{List where each element is a vector, providing a way of
grouping different random effect groups into a single category. Although
this input is still required, it is no has the former input Version has been
hardwired to Version = 1.}
\item{StartFromPar}{Logical flag (TRUE or FALSE) saying whether to start each
round of optimization from a ".par" file (I recommend TRUE)}
\item{Intern}{Logical flag saying whether to display all ss3 runtime output
in the R terminal}
\item{ReDoBiasRamp}{Logical flag saying whether to re-do the bias ramp
(using \code{\link{SS_fitbiasramp}}) each time Stock Synthesis is run.}
\item{BiasRamp_linenum_Vec}{Vector giving the line numbers from the CTL file
that contain the information about the bias ramp.}
\item{CTL_linenum_Type}{Character vector (same length as
\code{Input_SD_Group_Vec}),
where each element is either "Short_Param", "Long_Penalty", "Long_Penalty".
Default is NULL, and if not explicitly specified the program will attempt to
detect these automatically based on the length of relevant lines from the CTL
file.}
\item{systemcmd}{Should R call SS using "system" function instead of "shell".
This may be required when running R in Emacs on Windows. Default = FALSE.}
\item{exe}{SS executable name (excluding extension), either "ss" or "ss3".
This string is used for both calling the executable and also finding the
output files like ss.par. For 3.30, it should always be "ss" since the
output file names are hardwired in the TPL code.}
}
\description{
(Attempt to) perform the SS implementation of the Laplace Approximation
from Thorson, Hicks and Methot (2014) ICES J. Mar. Sci.
}
\examples{
\dontrun{
direc <- "C:/Models/LaplaceApprox/base" #need the full path because wd is changed in function
if("Optimization_record.txt" \%in\% list.files(direc)) {
file.remove(file.path(direc,"Optimization_record.txt"))
}
Opt = optimize(f=NegLogInt_Fn,
interval=c(0.001, 0.12),
maximum=FALSE,
File=direc,
Input_SD_Group_Vec=1,
CTL_linenum_List=list(127:131),
ESTPAR_num_List=list(86:205),
Int_Group_List=1,
PAR_num_Vec=NA,
Intern=TRUE)
}
}
\references{
Thorson, J.T., Hicks, A.C., and Methot, R.D. 2014. Random
effect estimation of time-varying factors in Stock Synthesis. ICES J. Mar.
Sci.
}
\seealso{
\code{\link{read.admbFit}}, \code{\link{getADMBHessian}}
}
\author{
James Thorson
}
|
/man/NegLogInt_Fn.Rd
|
no_license
|
paulburch/r4ss
|
R
| false | true | 4,070 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/NegLogInt_Fn.R
\name{NegLogInt_Fn}
\alias{NegLogInt_Fn}
\title{Perform SS implementation of Laplace Approximation}
\usage{
NegLogInt_Fn(File = NA, Input_SD_Group_Vec, CTL_linenum_List, ESTPAR_num_List,
PAR_num_Vec, Int_Group_List = list(1), StartFromPar = TRUE,
Intern = TRUE, ReDoBiasRamp = FALSE, BiasRamp_linenum_Vec = NULL,
CTL_linenum_Type = NULL, systemcmd = FALSE, exe = "ss")
}
\arguments{
\item{File}{Directory containing Stock Synthesis files
(e.g., "C:/Users/James Thorson/Desktop/")}
\item{Input_SD_Group_Vec}{Vector where each element is the standard deviation
for a group of random effects (e.g., a model with a single group of random
effects will have Input_SD_Group_Vec be a vector of length one)}
\item{CTL_linenum_List}{List (same length as \code{Input_SD_Group_Vec}),
where each
element is a vector giving the line number(s) for the random effect standard
deviation parameter or penalty in the CTL file (and where each line will
correspond to a 7-parameter or 14-parameter line).}
\item{ESTPAR_num_List}{List (same length as \code{Input_SD_Group_Vec}),
where each
element is a vector giving the parameter number for the random effect
coefficients in that group of random effects. These "parameter numbers"
correspond to the number of these parameters in the list of parameters in the
".cor" output file.}
\item{PAR_num_Vec}{Vector giving the number in the ".par" vector for each
random effect coefficient.}
\item{Int_Group_List}{List where each element is a vector, providing a way of
grouping different random effect groups into a single category. Although
this input is still required, it is no has the former input Version has been
hardwired to Version = 1.}
\item{StartFromPar}{Logical flag (TRUE or FALSE) saying whether to start each
round of optimization from a ".par" file (I recommend TRUE)}
\item{Intern}{Logical flag saying whether to display all ss3 runtime output
in the R terminal}
\item{ReDoBiasRamp}{Logical flag saying whether to re-do the bias ramp
(using \code{\link{SS_fitbiasramp}}) each time Stock Synthesis is run.}
\item{BiasRamp_linenum_Vec}{Vector giving the line numbers from the CTL file
that contain the information about the bias ramp.}
\item{CTL_linenum_Type}{Character vector (same length as
\code{Input_SD_Group_Vec}),
where each element is either "Short_Param", "Long_Penalty", "Long_Penalty".
Default is NULL, and if not explicitly specified the program will attempt to
detect these automatically based on the length of relevant lines from the CTL
file.}
\item{systemcmd}{Should R call SS using "system" function instead of "shell".
This may be required when running R in Emacs on Windows. Default = FALSE.}
\item{exe}{SS executable name (excluding extension), either "ss" or "ss3".
This string is used for both calling the executable and also finding the
output files like ss.par. For 3.30, it should always be "ss" since the
output file names are hardwired in the TPL code.}
}
\description{
(Attempt to) perform the SS implementation of the Laplace Approximation
from Thorson, Hicks and Methot (2014) ICES J. Mar. Sci.
}
\examples{
\dontrun{
direc <- "C:/Models/LaplaceApprox/base" #need the full path because wd is changed in function
if("Optimization_record.txt" \%in\% list.files(direc)) {
file.remove(file.path(direc,"Optimization_record.txt"))
}
Opt = optimize(f=NegLogInt_Fn,
interval=c(0.001, 0.12),
maximum=FALSE,
File=direc,
Input_SD_Group_Vec=1,
CTL_linenum_List=list(127:131),
ESTPAR_num_List=list(86:205),
Int_Group_List=1,
PAR_num_Vec=NA,
Intern=TRUE)
}
}
\references{
Thorson, J.T., Hicks, A.C., and Methot, R.D. 2014. Random
effect estimation of time-varying factors in Stock Synthesis. ICES J. Mar.
Sci.
}
\seealso{
\code{\link{read.admbFit}}, \code{\link{getADMBHessian}}
}
\author{
James Thorson
}
|
rm(list = ls())
library(tidyverse)
'%nin%' <- function(x,y)!('%in%'(x,y))
printf <- function(...) cat(sprintf(...))
rxn_md = read_csv("Recon2_export/rxn_md.csv")
central_carbon = rxn_md %>%
# turns out that some of the interesting glycolysis reactions (e.g., HEX1 = hexokinase) have confidence = 2
# filter(rxn_confidence %in% c("0", "4")) %>%
filter(str_detect(subsystem, "(Glycolysis|Citric acid cycle)")) %>%
arrange(rxn_code_nodirection) %>%
#manually exclude some uninteresting reactions
filter(!str_detect(rxn_code_nodirection, "^AL(C|D)D2")) %>%
filter(rxn_code_nodirection %nin% c("ETOHMO")) %>%
filter(!grepl("\\[(l|r|x|n|g|a)\\]", rxn_formula))
#unique just in case
writeLines(central_carbon$rxn_code_nodirection %>% unique(),
con = "Recon2_export/central_carbon_rxns.txt")
exchange_rxns = rxn_md %>%
#no need to include the DM_ reactions that have Exchange/demand as their subsystem,
#David didn't define them as exchange, it seems
filter(grepl("^EX_", rxn_code_nodirection)) #| (subsystem == "Exchange/demand reaction"))
writeLines(exchange_rxns$rxn_code_nodirection %>% unique(),
con = "Recon2_export/exchange_rxns.txt")
|
/compass/Resources/Recon2_export/define_reaction_subsets.R
|
permissive
|
merlinTFG/Compass
|
R
| false | false | 1,183 |
r
|
rm(list = ls())
library(tidyverse)
'%nin%' <- function(x,y)!('%in%'(x,y))
printf <- function(...) cat(sprintf(...))
rxn_md = read_csv("Recon2_export/rxn_md.csv")
central_carbon = rxn_md %>%
# turns out that some of the interesting glycolysis reactions (e.g., HEX1 = hexokinase) have confidence = 2
# filter(rxn_confidence %in% c("0", "4")) %>%
filter(str_detect(subsystem, "(Glycolysis|Citric acid cycle)")) %>%
arrange(rxn_code_nodirection) %>%
#manually exclude some uninteresting reactions
filter(!str_detect(rxn_code_nodirection, "^AL(C|D)D2")) %>%
filter(rxn_code_nodirection %nin% c("ETOHMO")) %>%
filter(!grepl("\\[(l|r|x|n|g|a)\\]", rxn_formula))
#unique just in case
writeLines(central_carbon$rxn_code_nodirection %>% unique(),
con = "Recon2_export/central_carbon_rxns.txt")
exchange_rxns = rxn_md %>%
#no need to include the DM_ reactions that have Exchange/demand as their subsystem,
#David didn't define them as exchange, it seems
filter(grepl("^EX_", rxn_code_nodirection)) #| (subsystem == "Exchange/demand reaction"))
writeLines(exchange_rxns$rxn_code_nodirection %>% unique(),
con = "Recon2_export/exchange_rxns.txt")
|
ui <- navbarPage(title = "test",
tabPanel("Scot Demo"))
server <- function(input, output, session) {
}
shinyApp(ui = ui, server = server)
|
/sg-training-r-project/app.R
|
no_license
|
Swirrl/sg-training-material
|
R
| false | false | 156 |
r
|
ui <- navbarPage(title = "test",
tabPanel("Scot Demo"))
server <- function(input, output, session) {
}
shinyApp(ui = ui, server = server)
|
##### Reason: combine the results for the variance explained by different factors for each gene
##### Author: Drew Neavin
##### Date: 14 March, 2022
##### Load in libraries #####
library(data.table)
library(tidyverse)
library(ggridges)
library(raincloudplots)
library(ggdist)
library(clusterProfiler)
library(org.Hs.eg.db)
library(GOSemSim)
library(RColorBrewer)
##### Set up directories #####
dir <- "/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/"
icc_dir <- "/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/multi-passage/Variance/variance_integratedSCT/gene_separated/icc/"
icc_dir2 <- "/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/multi-passage/Variance/variance/gene_separated/icc/"
icc_interaction_dir <- "/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/multi-passage/Variance/variance_integratedSCT/gene_separated/icc_interaction/"
outdir <- "/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/multi-passage/Variance/variance_integratedSCT/combined/"
outdir_comparison <- "/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/multi-passage/Variance/comparison/"
dir.create(outdir, recursive = TRUE)
dir.create(outdir_comparison, recursive = TRUE)
vars <- c("Line", "Passage", "Line:Passage", "Residual")
selected_vars <- c("Line", "Passage", "Line:Passage", "Residual")
var_colors <- c("#4734a9", "#78c0fe", "#97cf8a", "gray80")
names(var_colors) <- vars
var_colors <- var_colors[selected_vars]
##### Get list of icc files #####
icc_files <- list.files(icc_dir, pattern= ".rds")
# icc_files2 <- list.files(icc_dir2)
##### Read in icc results #####
icc_results_list <- lapply(icc_files, function(x){
readRDS(paste0(icc_dir,x))
})
names(icc_results_list) <- icc_files
# icc_results_list2 <- lapply(icc_files2, function(x){
# readRDS(paste0(icc_dir2,x))
# })
# names(icc_results_list2) <- icc_files2
##### Get list of icc interaction files #####
icc_interaction_files <- list.files(icc_interaction_dir, pattern = "_icc.rds")
##### Read in icc results #####
icc_interaction_results_list <- lapply(icc_interaction_files, function(x){
readRDS(paste0(icc_interaction_dir,x))
})
names(icc_interaction_results_list) <- icc_interaction_files
##### Merge icc results into a single data.table #####
icc_dt <- do.call(rbind, icc_results_list)
# icc_dt2 <- do.call(rbind, icc_results_list2)
# colnames(icc_dt2) <- paste0("integrate_sct_", colnames(icc_dt2))
# icc_dt_joined <- icc_dt[icc_dt2, on = c("grp" = "integrate_sct_grp", "gene" = "integrate_sct_gene")]
# icc_dt_joined$diff <- icc_dt_joined$percent - icc_dt_joined$integrate_sct_percent
# max(na.omit(icc_dt_joined$diff))
# icc_dt_joined[diff == max(na.omit(icc_dt_joined$diff))]
# icc_dt_joined[gene == "ENSG00000106153"]
# diff_dist <- ggplot(icc_dt_joined, aes(diff)) +
# geom_histogram() +
# facet_wrap(vars(grp))
# # theme_classic()
# ggsave(diff_dist, filename = paste0(outdir_comparison, "diff_histogram.png"))
# scatter <- ggplot(icc_dt_joined, aes(percent,integrate_sct_percent)) +
# geom_point() +
# facet_wrap(vars(grp), scales = "free") +
# theme_classic()
# ggsave(scatter, filename = paste0(outdir_comparison, "scatter_correlation.png"), height = 4.5)
# icc_dt_joined_long <- melt(icc_dt_joined, id.vars = c("grp", "gene"),measure.vars = c("percent", "integrate_sct_percent"))
# dist <- ggplot(icc_dt_joined_long, aes(value)) +
# geom_histogram() +
# facet_grid(grp ~ variable) +
# theme_classic()
# ggsave(dist, filename = paste0(outdir_comparison, "histogram.png"))
# ### want to use the integrate_sct_percent
# ### compared the two methods and appears that fitting when have been separately sct assessed => Line effect well correlated but time is much less (and not well correlated between the two methods) so likely inducing Time effects from normalizing all together
icc_dt$percent_round <- round(icc_dt$percent)
icc_dt$grp <- gsub("Time", "Passage", icc_dt$grp)
icc_dt$grp <- factor(icc_dt$grp, levels= rev(c("Line", "Passage", "Line:Passage", "Residual")))
group_size <- data.table(table(icc_dt$grp))
colnames(group_size) <- c("grp", "size")
group_size$grp_size <- paste0(group_size$grp, "\nN = ", group_size$size)
icc_dt <- group_size[icc_dt, on = "grp"]
icc_dt$grp_size <- factor(icc_dt$grp_size, levels = unique(group_size$grp_size))
##### Merge icc_interaction results into a single data.table #####
icc_interaction_dt <- do.call(rbind, icc_interaction_results_list)
icc_interaction_dt$percent_round <- round(icc_interaction_dt$percent)
icc_interaction_dt$grp <- gsub("Time", "Passage", icc_interaction_dt$grp)
icc_interaction_dt$grp <- factor(icc_interaction_dt$grp, levels= rev(c("Line", "Passage", "Line:Passage", "Residual")))
group_size <- data.table(table(icc_interaction_dt$grp))
colnames(group_size) <- c("grp", "size")
group_size$grp_size <- paste0(group_size$grp, "\nN = ", group_size$size)
icc_interaction_dt <- group_size[icc_interaction_dt, on = "grp"]
icc_interaction_dt$grp_size <- factor(icc_interaction_dt$grp_size, levels = unique(group_size$grp_size))
### *** Need to add individual effects without interaction in to interaction dt *** ###
icc_interaction_plus_dt <- rbind(icc_interaction_dt, icc_dt[!(gene %in% icc_interaction_dt$gene)])
group_size <- data.table(table(icc_interaction_plus_dt$grp))
colnames(group_size) <- c("grp", "size")
group_size$grp_size <- paste0(group_size$grp, "\nN = ", group_size$size)
icc_interaction_plus_dt <- group_size[icc_interaction_plus_dt, on = "grp"]
icc_interaction_plus_dt$grp_size <- factor(icc_interaction_plus_dt$grp_size, levels = unique(group_size$grp_size))
var_explained_manuscript <- icc_interaction_plus_dt[,c("grp", "percent", "P", "gene")]
colnames(var_explained_manuscript) <- c("Covariate", "Percent_Variance_Explained", "P", "ENSG")
##### Add gene IDs for easy identification downstream #####
GeneConversion1 <- read_delim("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/data/Expression_200128_A00152_0196_BH3HNFDSXY/GE/DRENEA_1/outs/filtered_feature_bc_matrix/features.tsv.gz", col_names = F, delim = "\t")
GeneConversion2 <- read_delim("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/data/Expression_200128_A00152_0196_BH3HNFDSXY/GE/Village_B_1_week/outs/filtered_feature_bc_matrix/features.tsv.gz", col_names = F, delim = "\t")
GeneConversion <- unique(rbind(GeneConversion1, GeneConversion2))
GeneConversion <- GeneConversion[!duplicated(GeneConversion$X1),]
GeneConversion$X3 <- NULL
colnames(GeneConversion) <- c("ENSG", "Gene_ID")
GeneConversion <- data.table(GeneConversion)
### Add the gene IDs to the icc_dt ###
var_explained_manuscript <- GeneConversion[var_explained_manuscript, on =c("ENSG")]
fwrite(var_explained_manuscript, paste0(outdir, "multi-passage_variance_explained_manuscript.tsv"), sep = "\t")
##### Check difference in percent explained with and without interactions #####
icc_interaction_dt_joined <- icc_dt[icc_interaction_dt, on = c("grp", "gene")]
icc_interaction_dt_joined$difference <- icc_interaction_dt_joined$percent - icc_interaction_dt_joined$i.percent
pRaincloud_dif <- ggplot(icc_interaction_dt_joined, aes(x = difference, y = factor(grp_size, levels = rev(levels(grp_size))), fill = factor(grp, levels = rev(selected_vars)))) +
geom_density_ridges(stat = "binline", bins = 90, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..), alpha = 0.75) +
geom_boxplot(size = 0.5,width = .15, outlier.size = 0.25, position = position_nudge(y=-0.12), alpha = 0.75) +
coord_cartesian(xlim = c(1.2, NA), clip = "off") +
theme_classic() +
theme(axis.title.y=element_blank()) +
xlab("Percent Variance Explained") +
scale_y_discrete(expand = c(0.03, 0)) +
scale_fill_manual(values = var_colors)
ggsave(pRaincloud_dif, filename = paste0(outdir, "variance_explained_interaction_difference_raincloud.png"), height = 8, width = 7)
ggsave(pRaincloud_dif, filename = paste0(outdir, "variance_explained_interaction_difference_raincloud.pdf"), height = 8, width = 7)
##### Make a figure of stacked variance explained #####
### Order based on line variance explained ###
genes_list <- list()
for (group in c("Line", "Passage", "Line:Passage", "Residual")){
genes_list[[group]] <- icc_dt[grp == group][rev(order(percent_round))]$gene
}
genes <- unique(unlist(genes_list))
icc_dt$gene <- factor(icc_dt$gene, levels = genes)
icc_dt$grp <- factor(icc_dt$grp, levels= rev(c("Line", "Passage", "Line:Passage", "Residual")))
## First on line percent, then village percent ##
bar_proportions <- ggplot(icc_dt, aes(x = gene, y = percent, fill = grp)) +
geom_bar(position="stack", stat="identity") +
theme_classic() +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()) +
scale_fill_manual(values = var_colors)
ggsave(bar_proportions, filename = paste0(outdir, "variance_explained_bar.png"), width = 20)
### Try boxplot ###
boxplot <- ggplot(icc_dt, aes(x = factor(grp, levels = rev(levels(grp))), y = percent, fill = factor(grp, levels = rev(levels(grp))), color = factor(grp, levels = rev(levels(grp))))) +
geom_boxplot(alpha = 0.5, size = 0.5) +
theme_classic() +
xlab("Covariate") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
legend.position="none") +
scale_fill_manual(values = var_colors) +
scale_color_manual(values = var_colors)
ggsave(boxplot, filename = paste0(outdir, "variance_explained_box.png"), height = 3, width = 2)
### Try ridgplots ###
pRidges <- ggplot(icc_dt[grp != "Residual"], aes(x = percent, y = factor(grp, levels = rev(levels(grp))), fill = factor(grp, levels = rev(levels(grp))))) +
geom_density_ridges(stat = "binline", bins = 200, scale = 0.95, draw_baseline = FALSE) +
# geom_density_ridges() +
theme_classic()
ggsave(pRidges, filename = paste0(outdir, "variance_explained_ridge.png"), height = 8, width = 10)
### Try ridgplots ###
pRidges_pop <- ggplot(icc_dt[grp != "Residual"][percent > 10], aes(x = percent, y = factor(grp, levels = rev(levels(grp))), fill = factor(grp, levels = rev(levels(grp))))) +
geom_density_ridges(stat = "binline", bins = 180, scale = 0.95, draw_baseline = FALSE) +
# geom_density_ridges() +
theme_classic()
ggsave(pRidges_pop, filename = paste0(outdir, "variance_explained_ridge_pop.png"), height = 8, width = 10)
# pRaincloud <- ggplot(icc_dt, aes(x = percent, y = factor(grp_size, levels = rev(levels(grp_size))), fill = factor(grp, levels = rev(vars)))) +
# geom_density_ridges(stat = "binline", bins = 90, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..), alpha = 0.75) +
# geom_boxplot(size = 0.5,width = .15, outlier.size = 0.25, position = position_nudge(y=-0.12), alpha = 0.75) +
# coord_cartesian(xlim = c(1.2, NA), clip = "off") +
# theme_classic() +
# theme(axis.title.y=element_blank()) +
# xlab("Percent Variance Explained") +
# scale_y_discrete(expand = c(0.03, 0)) +
# scale_fill_manual(values = var_colors)
pRaincloud <- ggplot(icc_dt, aes(x = percent, y = factor(grp_size, levels = rev(levels(grp_size))), fill = factor(grp, levels = rev(vars)))) +
geom_density_ridges(size = 0.1,stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..)) +
geom_point(size =1, position = position_nudge(y=-0.09), shape = "|", aes(color = factor(grp, levels = rev(vars)))) +
coord_cartesian(xlim = c(1.2, NA), clip = "off") +
theme_classic() +
theme(axis.title.y=element_blank()) +
xlab("Percent Variance Explained") +
scale_y_discrete(expand = c(0.1, 0)) +
scale_fill_manual(values = var_colors, name = "Variable") +
scale_color_manual(values = var_colors, name = "Variable") +
geom_vline(xintercept = 1, lty="11", color = "grey50", size = 0.5)
ggsave(pRaincloud, filename = paste0(outdir, "variance_explained_raincloud.png"), height = 2, width = 5)
ggsave(pRaincloud, filename = paste0(outdir, "variance_explained_raincloud.pdf"), height = 2, width = 5)
icc_interaction_plus_dt$grp_size <- factor(icc_interaction_plus_dt$grp_size, levels = c("Line\nN = 12076", "Passage\nN = 3620", "Line:Passage\nN = 2033", "Residual\nN = 12076"))
# pRaincloud_interaction <- ggplot(icc_interaction_plus_dt, aes(x = percent, y = grp_size, fill = factor(grp, levels = rev(vars)))) +
# geom_density_ridges(size = 0.1, stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity.., fill = factor(grp, levels = rev(vars))), alpha = 0.75) +
# geom_boxplot(outlier.shape=20, size = 0.1,width = .15, outlier.size = 0.01, position = position_nudge(y=-0.12), alpha = 0.75) +
# coord_cartesian(xlim = c(1.2, NA), clip = "off") +
# theme_classic() +
# theme(axis.title.y=element_blank()) +
# xlab("Percent Variance Explained") +
# scale_y_discrete(expand = c(0.03, 0)) +
# scale_fill_manual(values = var_colors) +
# scale_color_manual(values = var_colors) +
# geom_vline(xintercept = 1,color = "grey70", size = 0.4, lty="11")
pRaincloud_interaction <- ggplot(icc_interaction_plus_dt, aes(x = percent, y = factor(grp_size, levels = levels(grp_size)), fill = factor(grp, levels = rev(vars)))) +
geom_density_ridges(size = 0.1,stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..)) +
geom_point(size =1, position = position_nudge(y=-0.09), shape = "|", aes(color = factor(grp, levels = rev(vars)))) +
coord_cartesian(xlim = c(1.2, NA), clip = "off") +
theme_classic() +
theme(axis.title.y=element_blank()) +
xlab("Percent Variance Explained") +
scale_y_discrete(expand = c(0.1, 0)) +
scale_fill_manual(values = var_colors, name = "Variable") +
scale_color_manual(values = var_colors, name = "Variable") +
geom_vline(xintercept = 1, lty="11", color = "grey50", size = 0.5)
ggsave(pRaincloud_interaction, filename = paste0(outdir, "variance_explained_raincloud_interaction.png"), height = 2, width = 4)
ggsave(pRaincloud_interaction, filename = paste0(outdir, "variance_explained_raincloud_interaction.pdf"), height = 2, width = 4)
icc_interaction_sig_list <- list()
for (ensg in unique(icc_interaction_plus_dt$gene)){
icc_interaction_sig_list[[ensg]] <- icc_interaction_plus_dt[gene == ensg][P < 0.05/(nrow(icc_interaction_plus_dt[gene == ensg])-1)]
icc_interaction_sig_list[[ensg]] <- rbind(icc_interaction_sig_list[[ensg]], icc_interaction_plus_dt[gene == ensg & grp == "Residual"])
}
icc_interaction_sig_dt <- do.call(rbind, icc_interaction_sig_list)
group_size <- data.table(table(icc_interaction_sig_dt$grp))
colnames(group_size) <- c("grp", "size")
group_size$grp_size <- paste0(group_size$grp, "\nN = ", group_size$size)
icc_interaction_sig_dt <- group_size[icc_interaction_sig_dt, on = "grp"]
icc_interaction_sig_dt$grp_size <- factor(icc_interaction_sig_dt$grp_size, levels = unique(group_size$grp_size))
grp_size_order <- c("Line\nN = 12076", "Passage\nN = 3620", "Line:Passage\nN = 2033", "Residual\nN = 12076")
mean(icc_interaction_sig_dt[grp == "Line"]$percent)
mean(icc_interaction_sig_dt[grp == "Passage"]$percent)
mean(icc_interaction_sig_dt[grp == "Line:Passage"]$percent)
mean(icc_interaction_sig_dt[grp == "Line" & percent > 1]$percent)
mean(icc_interaction_sig_dt[grp == "Passage" & percent > 1]$percent)
mean(icc_interaction_sig_dt[grp == "Line:Passage" & percent > 1]$percent)
# pRaincloud_interaction_sig <- ggplot(icc_interaction_sig_dt, aes(x = percent, y = factor(grp_size, levels = grp_size_order), fill = factor(grp, levels = rev(vars)))) +
# geom_density_ridges(stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..), alpha = 0.75) +
# geom_boxplot(size = 0.5,width = .15, outlier.size = 0.15, position = position_nudge(y=-0.12), alpha = 0.75) +
# coord_cartesian(xlim = c(1.2, NA), clip = "off") +
# theme_classic() +
# theme(axis.title.y=element_blank()) +
# xlab("Percent Variance Explained") +
# scale_y_discrete(expand = c(0.03, 0)) +
# scale_fill_manual(values = var_colors) +
# geom_vline(xintercept = 1, lty="11", color = "grey50", size = 0.5)
pRaincloud_interaction_sig <- ggplot(icc_interaction_sig_dt, aes(x = percent, y = factor(grp_size, levels = grp_size_order), fill = factor(grp, levels = rev(vars)))) +
geom_density_ridges(size = 0.1,stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..)) +
geom_point(size =1, position = position_nudge(y=-0.09), shape = "|", aes(color = factor(grp, levels = rev(vars)))) +
coord_cartesian(xlim = c(1.2, NA), clip = "off") +
theme_classic() +
theme(axis.title.y=element_blank()) +
xlab("Percent Variance Explained") +
scale_y_discrete(expand = c(0.07, 0)) +
scale_fill_manual(values = var_colors, name = "Variable") +
scale_color_manual(values = var_colors, name = "Variable") +
geom_vline(xintercept = 1, lty="11", color = "grey50", size = 0.5)
ggsave(pRaincloud_interaction_sig, filename = paste0(outdir, "variance_explained_raincloud_interaction_significant.png"), height = 2, width = 4)
ggsave(pRaincloud_interaction_sig, filename = paste0(outdir, "variance_explained_raincloud_interaction_significant.pdf"), height = 2, width = 4)
total <- icc_interaction_sig_dt[,.(count = .N), by = .(grp)]
total_less1pct <- icc_interaction_sig_dt[percent <= 1][,.(count_less_1pct = .N), by = .(grp)]
total_less5pct <- icc_interaction_sig_dt[percent <= 5][,.(count_less_5pct = .N), by = .(grp)]
total_less10pct <- icc_interaction_sig_dt[percent <= 10][,.(count_less_10pct = .N), by = .(grp)]
summary <- total[total_less1pct, on = "grp"]
summary <- summary[total_less5pct, on = "grp"]
summary <- summary[total_less10pct, on = "grp"]
summary$count_greater_1pct <- summary$count - summary$count_less_1pct
summary$count_greater_5pct <- summary$count - summary$count_less_5pct
summary$count_greater_10pct <- summary$count - summary$count_less_10pct
summary$percent_1pct <- (summary$count_less_1pct/summary$count)*100
summary$percent_5pct <- (summary$count_less_5pct/summary$count)*100
summary$percent_10pct <- (summary$count_less_10pct/summary$count)*100
# pRaincloud_interaction_sig_1pct <- ggplot(icc_interaction_sig_dt[percent >= 1], aes(x = percent, y = factor(grp_size, levels = grp_size_order), fill = factor(grp, levels = rev(vars)))) +
# geom_density_ridges(stat = "binline", bins = 90, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..), alpha = 0.75) +
# geom_boxplot(size = 0.5,width = .15, outlier.size = 0.25, position = position_nudge(y=-0.12), alpha = 0.75) +
# coord_cartesian(xlim = c(1.2, NA), clip = "off") +
# theme_classic() +
# theme(axis.title.y=element_blank()) +
# xlab("Percent Variance Explained") +
# scale_y_discrete(expand = c(0.03, 0)) +
# scale_fill_manual(values = var_colors) +
# geom_vline(xintercept = 1, linetype = "dashed", color = "firebrick3")
pRaincloud_interaction_sig_1pct <- ggplot(icc_interaction_sig_dt[percent >= 1], aes(x = percent, y = factor(grp_size, levels = grp_size_order), fill = factor(grp, levels = rev(vars)))) +
geom_density_ridges(size = 0.1,stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..)) +
geom_point(size =1, position = position_nudge(y=-0.09), shape = "|", aes(color = factor(grp, levels = rev(vars)))) +
coord_cartesian(xlim = c(1.2, NA), clip = "off") +
theme_classic() +
theme(axis.title.y=element_blank()) +
xlab("Percent Variance Explained") +
scale_y_discrete(expand = c(0.07, 0)) +
scale_fill_manual(values = var_colors, name = "Variable") +
scale_color_manual(values = var_colors, name = "Variable") +
geom_vline(xintercept = 1, lty="11", color = "grey50", size = 0.5)
ggsave(pRaincloud_interaction_sig_1pct, filename = paste0(outdir, "variance_explained_raincloud_interaction_significant_1pct.png"), height = 2, width = 4)
ggsave(pRaincloud_interaction_sig_1pct, filename = paste0(outdir, "variance_explained_raincloud_interaction_significant_1pct.pdf"), height = 2, width = 4)
##### Pull just the significant variances genome-wide #####
icc_interaction_plus_dt$fdr <- p.adjust(icc_interaction_plus_dt$P, method="fdr")
icc_interaction_sig_gw_dt <- icc_interaction_plus_dt[fdr < 0.05 | is.na(fdr)]
group_size_sig_gw <- data.table(table(icc_interaction_sig_gw_dt$grp))
colnames(group_size_sig_gw) <- c("grp", "size")
group_size_sig_gw$grp_size <- paste0(group_size_sig_gw$grp, "\nN = ", formatC(group_size_sig_gw$size, format="d", big.mark=","))
icc_interaction_sig_gw_dt <- group_size_sig_gw[icc_interaction_sig_gw_dt, on = "grp"]
icc_interaction_sig_gw_dt$grp_size <- factor(icc_interaction_sig_gw_dt$grp_size, levels = unique(group_size_sig_gw$grp_size))
group_size_sig_gw_order <- c("Line\nN = 750", "Passage\nN = 750", "Line:Passage\nN = 619", "Residual\nN = 750")
pRaincloud_interaction_sig_gw <- ggplot(icc_interaction_sig_gw_dt, aes(x = percent, y = factor(grp_size, levels = group_size_sig_gw_order), fill = factor(grp, levels = rev(vars)))) +
geom_density_ridges(stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..), alpha = 0.75) +
geom_boxplot(size = 0.5,width = .15, outlier.size = 0.25, position = position_nudge(y=-0.12), alpha = 0.75) +
coord_cartesian(xlim = c(1.2, NA), clip = "off") +
theme_classic() +
theme(axis.title.y=element_blank()) +
xlab("Percent Variance Explained") +
scale_y_discrete(expand = c(0.03, 0)) +
scale_fill_manual(values = var_colors) +
geom_vline(xintercept = 1, linetype = "dashed", color = "firebrick3") +
labs(fill="Covariate")
ggsave(pRaincloud_interaction_sig_gw, filename = paste0(outdir, "variance_explained_raincloud_interaction_significant_genome_wide.png"), height = 8, width = 7)
ggsave(pRaincloud_interaction_sig_gw, filename = paste0(outdir, "variance_explained_raincloud_interaction_significant_genome_wide.pdf"), height = 8, width = 7)
icc_interaction_sig_dt[gene == "ENSG00000106153"]
icc_dt[gene == "ENSG00000106153"]
icc_interaction_plus_dt[gene == "ENSG00000106153"]
icc_interaction_sig_gw_dt[gene == "ENSG00000106153"]
icc_interaction_sig_gw_dt2[gene == "ENSG00000106153"]
##### Add gene IDs for easy identification downstream #####
GeneConversion1 <- read_delim("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/data/Expression_200128_A00152_0196_BH3HNFDSXY/GE/DRENEA_1/outs/filtered_feature_bc_matrix/features.tsv.gz", col_names = F, delim = "\t")
GeneConversion2 <- read_delim("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/data/Expression_200128_A00152_0196_BH3HNFDSXY/GE/Village_B_1_week/outs/filtered_feature_bc_matrix/features.tsv.gz", col_names = F, delim = "\t")
GeneConversion <- unique(rbind(GeneConversion1, GeneConversion2))
GeneConversion <- GeneConversion[!duplicated(GeneConversion$X1),]
GeneConversion$X3 <- NULL
colnames(GeneConversion) <- c("gene", "Gene_ID")
GeneConversion <- data.table(GeneConversion)
### Add the gene IDs to the icc_dt ###
icc_interaction_sig_gw_dt <- GeneConversion[icc_interaction_sig_gw_dt, on = "gene"]
icc_interaction_sig_gw_dt[grp == "Cryopreserved"& percent_round > 1][rev(order(percent))]$Gene_ID
head(icc_interaction_sig_gw_dt[grp == "Cryopreserved" & percent_round > 1][rev(order(percent))][,c("Gene_ID", "percent_round")], n = 50)
icc_interaction_sig_gw_dt[grp == "Line"][rev(order(percent))]$Gene_ID
head(icc_interaction_sig_gw_dt[grp == "Line" & percent_round > 1][rev(order(percent))][,c("Gene_ID", "percent_round")], n = 50)
icc_interaction_sig_gw_dt[grp == "Village"][rev(order(percent))]$Gene_ID
head(icc_interaction_sig_gw_dt[grp == "Village" & percent_round > 1][rev(order(percent))][,c("Gene_ID", "percent_round")], n = 50)
fwrite(icc_interaction_sig_gw_dt, paste0(outdir, "sig_results.tsv.gz"), sep = "\t", compress = "gzip")
## Highlight
## X chromosome genes - wouldn't expect these to be Line-biased because expressed by both males and females
## Y chromosome genes - should be line-biased because expressed by only males and have some male(s) and some female(s)
## mt genes
## ribosomal genes
## look at gsea and kegg pathways for each
## Read in gtf used as reference and pull just X, Y or MT chromosome genes from it, use ribosomal file for rb genes
gtf <- fread("/directflow/GWCCGPipeline/projects/reference/refdata-cellranger-GRCh38-3.0.0/genes/genes.gtf", sep = "\t", autostart = 6, header = FALSE)
gtf_genes <- gtf[!(grep("transcript_id", V9))]
gtf_genes$V9 <- gsub("gene_id \"", "",gtf_genes$V9 ) %>%
gsub("\"; gene_version \"", ";", .) %>%
gsub("\"; gene_name \"", ";", .) %>%
gsub("\"; gene_source \"", ";", .) %>%
gsub("\"; gene_biotype \"", ";", .) %>%
gsub("\"", "", .)
gtf_genes[, c("gene_id", "gene_version", "gene_name", "gene_source", "gene_biotype") := data.table(str_split_fixed(V9,";", 5))]
icc_interaction_plus_dt <- GeneConversion[icc_interaction_plus_dt, on = "gene"]
X_chromosome_genes <- gtf_genes[V1 == "X"]
X_genelist <- X_chromosome_genes$gene_id[X_chromosome_genes$gene_id %in% genes]
Y_chromosome_genes <- gtf_genes[V1 == "Y"]
Y_genelist <- Y_chromosome_genes$gene_id[Y_chromosome_genes$gene_id %in% genes]
MT_chromosome_genes <- gtf_genes[V1 == "MT"]
MT_genelist <- MT_chromosome_genes$gene_id[MT_chromosome_genes$gene_id %in% genes]
RbGeneList <- read.delim(file = "/directflow/SCCGGroupShare/projects/DrewNeavin/References/RibosomalGeneList_GeneID_ENSG.txt")
Rb_genelist <- RbGeneList$ENSG[RbGeneList$ENSG %in% genes]
### Make stacked bar plots of the variance explained by different factors for these gene groups
## Figure of x chromosome genes ##
icc_x <- icc_interaction_plus_dt[data.table(gene = icc_interaction_plus_dt[grp == "Residual"][gene %in% X_genelist][order(percent_round)]$gene), on = "gene"]
icc_x$grp <- factor(icc_x$grp, levels = rev(selected_vars))
icc_x$gene <- factor(icc_x$gene, levels = unique(icc_x$gene))
bar_proportions_x <- ggplot(icc_x, aes(x = gene, y = percent, fill = grp)) +
geom_bar(position="stack", stat="identity", alpha = 0.75) +
theme_classic() +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5)) +
scale_fill_manual(values = var_colors) +
ggtitle("Variance Explained of\nX Chromosome Genes")
ggsave(bar_proportions_x, filename = paste0(outdir, "variance_explained_bar_x_genes.png"), width = 20)
## Figure of y chromosome genes ##
icc_y <- icc_interaction_plus_dt[data.table(gene = icc_interaction_plus_dt[grp == "Residual"][gene %in% Y_genelist][order(percent_round)]$gene), on = "gene"]
icc_y$grp <- factor(icc_y$grp, levels = rev(selected_vars))
icc_y$gene <- factor(icc_y$gene, levels = unique(icc_y$gene))
icc_y$Gene_ID <- factor(icc_y$Gene_ID, levels = unique(icc_y$Gene_ID))
bar_proportions_y <- ggplot(icc_y, aes(x = Gene_ID, y = percent, fill = grp)) +
geom_bar(position="stack", stat="identity", alpha = 0.75) +
theme_classic() +
theme(axis.title.x=element_blank(),
# axis.text.x=element_blank(),
# axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
scale_fill_manual(values = var_colors) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Variance Explained of\nY Chromosome Genes") +
ylab("Percent")
ggsave(bar_proportions_y, filename = paste0(outdir, "variance_explained_bar_y_genes.png"), width = 4.5, height = 4.5)
ggsave(bar_proportions_y, filename = paste0(outdir, "variance_explained_bar_y_genes.pdf"), width = 4.5, height = 4.5)
## Figure of mt chromosome genes ##
icc_mt <- icc_interaction_plus_dt[data.table(gene = icc_interaction_plus_dt[grp == "Residual"][gene %in% MT_genelist][order(percent_round)]$gene), on = "gene"]
icc_mt$grp <- factor(icc_mt$grp, levels = rev(selected_vars))
icc_mt$gene <- factor(icc_mt$gene, levels = unique(icc_mt$gene))
icc_mt$Gene_ID <- factor(icc_mt$Gene_ID, levels = unique(icc_mt$Gene_ID))
bar_proportions_mt <- ggplot(icc_mt, aes(x = Gene_ID, y = percent, fill = grp)) +
geom_bar(position="stack", stat="identity", alpha = 0.75) +
theme_classic() +
theme(axis.title.x=element_blank(),
# axis.text.x=element_blank(),
# axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
scale_fill_manual(values = var_colors) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Variance Explained of\nMitochondrial Genes") +
ylab("Percent")
ggsave(bar_proportions_mt, filename = paste0(outdir, "variance_explained_bar_mt_genes.png"), width = 4.5, height = 4.5)
ggsave(bar_proportions_mt, filename = paste0(outdir, "variance_explained_bar_mt_genes.pdf"), width = 4.5, height = 4.5)
## Figure of mt chromosome genes ##
icc_rb <- icc_interaction_plus_dt[data.table(gene = icc_interaction_plus_dt[grp == "Residual"][gene %in% Rb_genelist][order(percent_round)]$gene), on = "gene"]
icc_rb$grp <- factor(icc_rb$grp, levels = rev(selected_vars))
icc_rb$gene <- factor(icc_rb$gene, levels = unique(icc_rb$gene))
bar_proportions_rb <- ggplot(icc_rb, aes(x = gene, y = percent, fill = grp)) +
geom_bar(position="stack", stat="identity", alpha = 0.75) +
theme_classic() +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5)) +
scale_fill_manual(values = var_colors) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Variance Explained\nof Ribosomal Genes")
ggsave(bar_proportions_rb, filename = paste0(outdir, "variance_explained_bar_rb_genes.png"), width = 10, height = 4)
ggsave(bar_proportions_rb, filename = paste0(outdir, "variance_explained_bar_rb_genes.pdf"), width = 10, height = 4)
### Plot Pluripotency Genes ###
pluri_genes <- fread(paste0(dir,"data/pluripotency_genes.tsv"), sep = "\t", col.names = "Gene_ID", header = FALSE)
pluri_genes <- GeneConversion[pluri_genes, on = "Gene_ID"]
icc_dt_pluri_genes <- icc_interaction_plus_dt[pluri_genes,on = c("gene")]
icc_dt_pluri_genes$grp <- factor(icc_dt_pluri_genes$grp, levels = rev(selected_vars))
icc_dt_pluri_genes <- icc_dt_pluri_genes[data.table(gene = icc_dt_pluri_genes[grp == "Residual"][order(percent)]$gene), on = "gene"]
icc_dt_pluri_genes$Gene_ID <- factor(icc_dt_pluri_genes$Gene_ID, levels = unique(icc_dt_pluri_genes$Gene_ID))
pPluri_Genes_Cont <- ggplot() +
geom_bar(data = icc_dt_pluri_genes, aes(Gene_ID, percent, fill = grp), position = "stack", stat = "identity", alpha = 0.75) +
theme_classic() +
# facet_wrap(Gene_ID ~ ., nrow = 3) +
scale_fill_manual(values = var_colors) +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
ylab("Percent Gene Expression Variance Explained") +
ggtitle("Variance Explained of\nPluripotency Genes") +
theme(axis.title.x=element_blank())
ggsave(pPluri_Genes_Cont, filename = paste0(outdir, "Pluripotent_Gene_Variable_Contributions.png"), width = 6, height = 4.5)
ggsave(pPluri_Genes_Cont, filename = paste0(outdir, "Pluripotent_Gene_Variable_Contributions.pdf"), width = 6, height = 4.5)
##### check for variance explained for eQTL genes (from Kilpinen et al) that are #####
eqtls <- fread("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/eQTL_check/KilpinenOverlap/gene_snp_list.tsv", sep = "\t")
eqtls_icc <- icc_interaction_plus_dt[unique(eqtls[,"gene"]), on = "gene"]
eqtls_icc$grp <- factor(eqtls_icc$grp, levels = rev(vars))
eqtls_icc <- eqtls_icc[data.table(gene = eqtls_icc[grp == "Residual"][order(percent)]$gene), on = "gene"]
eqtls_icc$Gene_ID <- factor(eqtls_icc$Gene_ID, levels = unique(eqtls_icc$Gene_ID))
group_size_eqtl <- data.table(table(eqtls_icc$grp))
colnames(group_size_eqtl) <- c("grp", "size")
group_size_eqtl$grp_size <- paste0(group_size_eqtl$grp, "\nN = ", group_size_eqtl$size)
eqtls_icc <- group_size_eqtl[eqtls_icc, on = "grp"]
grp_size_order_eqtl <- c("Line\nN = 2371", "Village\nN = 1836", "Site\nN = 2461", "Replicate\nN = 869", "Line:Village\nN = 367", "Line:Site\nN = 911", "Village:Site\nN = 898","Replicate:Village\nN = 305", "Replicate:Line\nN = 25", "Replicate:Site\nN = 78", "Residual\nN = 2542")
eqtls_icc$grp_size <- factor(eqtls_icc$grp_size, levels = grp_size_order_eqtl)
eqtls_icc_1pct <- eqtls_icc
for (ensg in unique(eqtls_icc$gene)){
if (!any(eqtls_icc[gene == ensg & grp != "Residual"]$percent > 1)){
eqtls_icc_1pct <- eqtls_icc_1pct[gene != ensg]
}
}
eqtls_icc_1pct_grouped_list <- list()
for (ensg in unique(eqtls_icc_1pct$gene)){
group <- eqtls_icc_1pct[gene == ensg & grp != "Residual"][which.max(percent)]$grp
eqtls_icc_1pct_grouped_list[[group]][[ensg]] <- eqtls_icc_1pct[gene == ensg]
}
eqtls_icc_1pct_grouped <- lapply(eqtls_icc_1pct_grouped_list, function(x) do.call(rbind, x))
eqtls_icc_1pct_grouped <- lapply(names(eqtls_icc_1pct_grouped), function(x){
eqtls_icc_1pct_grouped[[x]]$largest_contributor <- x
return(eqtls_icc_1pct_grouped[[x]])
})
eqtls_icc_1pct_grouped_dt <- do.call(rbind, eqtls_icc_1pct_grouped)
eqtls_icc_1pct_grouped_dt$largest_contributor <- factor(eqtls_icc_1pct_grouped_dt$largest_contributor, levels = c("Line", "Village", "Site", "Line:Village", "Line:Site", "Village:Site", "Replicate:Village"))
pPluri_Genes_largest_Cont_eqtl <- ggplot() +
geom_bar(data = eqtls_icc_1pct_grouped_dt, aes(Gene_ID, percent, fill = factor(grp, levels = rev(vars))), position = "stack", stat = "identity", alpha = 0.75) +
theme_classic() +
facet_grid(. ~ largest_contributor, scales = "free_x", space = "free_x") +
scale_fill_manual(values = var_colors) +
ylab("Percent Gene Expression Variance Explained") +
theme(axis.title.x=element_blank(),
axis.text.x = element_blank(),
panel.spacing.x=unit(0, "lines"),
axis.ticks.x = element_blank()) +
geom_hline(yintercept = 1, linetype = "dashed")
# scale_y_discrete(expand = c(0.03, 0))
# scale_x_discrete(expand = c(0.03, 0))
ggsave(pPluri_Genes_largest_Cont_eqtl, filename = paste0(outdir, "eQTL_Genes_Variance_Contributions_1pct_largest_cont.png"), width = 10, height = 4)
ggsave(pPluri_Genes_largest_Cont_eqtl, filename = paste0(outdir, "eQTL_Genes_Variance_Contributions_1pct_largest_cont.pdf"), width = 10, height = 4)
# ### Count number of each chromosome/gene category type
# ### numbers are the numbers of that category that where a significant percent of variance is explained by this variable
# ### percent of that category that where a significant percent of variance is explained by this variable
# x_number <- lapply(genes_list, function(x){
# length(which(x %in% X_chromosome_genes$gene_id))
# })
# x_percent <- lapply(genes_list, function(x){
# length(which(x %in% X_chromosome_genes$gene_id))/length(X_chromosome_genes$gene_id)
# })
# y_number <- lapply(genes_list, function(x){
# length(which(x %in% Y_chromosome_genes$gene_id))
# })
# y_percent <- lapply(genes_list, function(x){
# length(which(x %in% Y_chromosome_genes$gene_id))/length(Y_chromosome_genes$gene_id)
# })
# mt_number <- lapply(genes_list, function(x){
# length(which(x %in% MT_chromosome_genes$gene_id))
# })
# mt_percent <- lapply(genes_list, function(x){
# length(which(x %in% MT_chromosome_genes$gene_id))/length(MT_chromosome_genes$gene_id)
# })
# rb_number <- lapply(genes_list, function(x){
# length(which(x %in% RbGeneList$ENSG))
# })
# rb_percent <- lapply(genes_list, function(x){
# length(which(x %in% RbGeneList$ENSG))/length(RbGeneList$ENSG)
# })
# ### for > 1% var explained
# ### Count number of each chromosome/gene category type
# x_number_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% X_chromosome_genes$gene_id))
# })
# names(x_number_1) <- vars
# x_percent_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% X_chromosome_genes$gene_id))/length(X_chromosome_genes$gene_id)
# })
# names(x_percent_1) <- vars
# y_number_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% Y_chromosome_genes$gene_id))
# })
# names(y_number_1) <- vars
# y_percent_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% Y_chromosome_genes$gene_id))/length(Y_chromosome_genes$gene_id)
# })
# names(y_percent_1) <- vars
# mt_number_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% MT_chromosome_genes$gene_id))
# })
# names(mt_number_1) <- vars
# mt_percent_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% MT_chromosome_genes$gene_id))/length( MT_chromosome_genes$gene_id)
# })
# names(mt_percent_1) <- vars
# rb_number_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% RbGeneList$ENSG))
# })
# names(rb_number_1) <- vars
# rb_percent_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% RbGeneList$ENSG))/length(RbGeneList$ENSG)
# })
# names(rb_percent_1) <- vars
### Pathway analysis ###
geneList <- lapply(genes_list, function(x){
tmp <- bitr(x, fromType = "ENSEMBL",
toType = c("ENTREZID"),
OrgDb = org.Hs.eg.db)$ENTREZID
tmp[!is.na(tmp)]
})
gg <- list()
kk <- list()
df = as.data.frame(org.Hs.egGO)
go_gene_list = unique(sort(df$gene_id))
dfk = as.data.frame(org.Hs.egPATH)
kegg_gene_list = unique(sort(dfk$gene_id))
for (group in c("Line", "Village", "Cryopreserved", "Replicate","Line:Village", "Line:Cryopreserved", "Village:Cryopreserved", "Replicate:Village", "Replicate:Line", "Replicate:Cryopreserved", "Residual")){
kk[[group]] <- enrichKEGG(gene = geneList[[group]],
universe = geneList[[group]],
organism = 'hsa',
pvalueCutoff = 0.05,
keyType = 'ncbi-geneid')
gg[[group]] <- groupGO(gene = geneList[[group]],
OrgDb = org.Hs.eg.db,
readable = TRUE)
}
hsGO <- godata('org.Hs.eg.db', ont="MF")
sim_results <- list()
vars <- c("Line", "Village", "Cryopreserved", "Replicate","Village:Line", "Line:Cryopreserved", "Village:Cryopreserved", "Replicate:Village", "Replicate:Line", "Replicate:Cryopreserved")
for (group1 in vars){
print(group1)
for (group2 in vars[(grep(paste0("^",group1, "$"), vars) + 1): length(vars)]){
print(group2)
sim_results[[group1]][[group2]] <- clusterSim(geneList[[group1]], geneList[[group2]], semData=hsGO, measure="Wang", combine="BMA")
}
}
# genes2rerun <- c(character())
# for (g in unique(icc_dt$gene)){
# print(g)
# genes2rerun <- c(genes2rerun, as.character(unique(icc_dt[gene == g][P > 0.05/(nrow(icc_dt[gene == g])-1)]$gene)))
# }
# for (gene in genes2rerun){
# print(gene)
# # unlink(paste0(icc_dir,gene,"_icc.rds"))
# print(file.exists(paste0(icc_dir,gene,"_icc.rds")))
# unlink(paste0("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/variance_partition_post_review/gene_separated/fit_models/",gene,"_fitted_models.rds"))
# unlink(paste0("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/variance_partition_post_review/gene_separated/residuals4qtl/",gene,"_residuals4qtl.rds"))
# }
|
/multi-passage/Variance/variance_partition_multipassage_combine.R
|
no_license
|
powellgenomicslab/iPSC_Village_scripts
|
R
| false | false | 41,664 |
r
|
##### Reason: combine the results for the variance explained by different factors for each gene
##### Author: Drew Neavin
##### Date: 14 March, 2022
##### Load in libraries #####
library(data.table)
library(tidyverse)
library(ggridges)
library(raincloudplots)
library(ggdist)
library(clusterProfiler)
library(org.Hs.eg.db)
library(GOSemSim)
library(RColorBrewer)
##### Set up directories #####
dir <- "/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/"
icc_dir <- "/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/multi-passage/Variance/variance_integratedSCT/gene_separated/icc/"
icc_dir2 <- "/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/multi-passage/Variance/variance/gene_separated/icc/"
icc_interaction_dir <- "/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/multi-passage/Variance/variance_integratedSCT/gene_separated/icc_interaction/"
outdir <- "/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/multi-passage/Variance/variance_integratedSCT/combined/"
outdir_comparison <- "/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/multi-passage/Variance/comparison/"
dir.create(outdir, recursive = TRUE)
dir.create(outdir_comparison, recursive = TRUE)
vars <- c("Line", "Passage", "Line:Passage", "Residual")
selected_vars <- c("Line", "Passage", "Line:Passage", "Residual")
var_colors <- c("#4734a9", "#78c0fe", "#97cf8a", "gray80")
names(var_colors) <- vars
var_colors <- var_colors[selected_vars]
##### Get list of icc files #####
icc_files <- list.files(icc_dir, pattern= ".rds")
# icc_files2 <- list.files(icc_dir2)
##### Read in icc results #####
icc_results_list <- lapply(icc_files, function(x){
readRDS(paste0(icc_dir,x))
})
names(icc_results_list) <- icc_files
# icc_results_list2 <- lapply(icc_files2, function(x){
# readRDS(paste0(icc_dir2,x))
# })
# names(icc_results_list2) <- icc_files2
##### Get list of icc interaction files #####
icc_interaction_files <- list.files(icc_interaction_dir, pattern = "_icc.rds")
##### Read in icc results #####
icc_interaction_results_list <- lapply(icc_interaction_files, function(x){
readRDS(paste0(icc_interaction_dir,x))
})
names(icc_interaction_results_list) <- icc_interaction_files
##### Merge icc results into a single data.table #####
icc_dt <- do.call(rbind, icc_results_list)
# icc_dt2 <- do.call(rbind, icc_results_list2)
# colnames(icc_dt2) <- paste0("integrate_sct_", colnames(icc_dt2))
# icc_dt_joined <- icc_dt[icc_dt2, on = c("grp" = "integrate_sct_grp", "gene" = "integrate_sct_gene")]
# icc_dt_joined$diff <- icc_dt_joined$percent - icc_dt_joined$integrate_sct_percent
# max(na.omit(icc_dt_joined$diff))
# icc_dt_joined[diff == max(na.omit(icc_dt_joined$diff))]
# icc_dt_joined[gene == "ENSG00000106153"]
# diff_dist <- ggplot(icc_dt_joined, aes(diff)) +
# geom_histogram() +
# facet_wrap(vars(grp))
# # theme_classic()
# ggsave(diff_dist, filename = paste0(outdir_comparison, "diff_histogram.png"))
# scatter <- ggplot(icc_dt_joined, aes(percent,integrate_sct_percent)) +
# geom_point() +
# facet_wrap(vars(grp), scales = "free") +
# theme_classic()
# ggsave(scatter, filename = paste0(outdir_comparison, "scatter_correlation.png"), height = 4.5)
# icc_dt_joined_long <- melt(icc_dt_joined, id.vars = c("grp", "gene"),measure.vars = c("percent", "integrate_sct_percent"))
# dist <- ggplot(icc_dt_joined_long, aes(value)) +
# geom_histogram() +
# facet_grid(grp ~ variable) +
# theme_classic()
# ggsave(dist, filename = paste0(outdir_comparison, "histogram.png"))
# ### want to use the integrate_sct_percent
# ### compared the two methods and appears that fitting when have been separately sct assessed => Line effect well correlated but time is much less (and not well correlated between the two methods) so likely inducing Time effects from normalizing all together
icc_dt$percent_round <- round(icc_dt$percent)
icc_dt$grp <- gsub("Time", "Passage", icc_dt$grp)
icc_dt$grp <- factor(icc_dt$grp, levels= rev(c("Line", "Passage", "Line:Passage", "Residual")))
group_size <- data.table(table(icc_dt$grp))
colnames(group_size) <- c("grp", "size")
group_size$grp_size <- paste0(group_size$grp, "\nN = ", group_size$size)
icc_dt <- group_size[icc_dt, on = "grp"]
icc_dt$grp_size <- factor(icc_dt$grp_size, levels = unique(group_size$grp_size))
##### Merge icc_interaction results into a single data.table #####
icc_interaction_dt <- do.call(rbind, icc_interaction_results_list)
icc_interaction_dt$percent_round <- round(icc_interaction_dt$percent)
icc_interaction_dt$grp <- gsub("Time", "Passage", icc_interaction_dt$grp)
icc_interaction_dt$grp <- factor(icc_interaction_dt$grp, levels= rev(c("Line", "Passage", "Line:Passage", "Residual")))
group_size <- data.table(table(icc_interaction_dt$grp))
colnames(group_size) <- c("grp", "size")
group_size$grp_size <- paste0(group_size$grp, "\nN = ", group_size$size)
icc_interaction_dt <- group_size[icc_interaction_dt, on = "grp"]
icc_interaction_dt$grp_size <- factor(icc_interaction_dt$grp_size, levels = unique(group_size$grp_size))
### *** Need to add individual effects without interaction in to interaction dt *** ###
icc_interaction_plus_dt <- rbind(icc_interaction_dt, icc_dt[!(gene %in% icc_interaction_dt$gene)])
group_size <- data.table(table(icc_interaction_plus_dt$grp))
colnames(group_size) <- c("grp", "size")
group_size$grp_size <- paste0(group_size$grp, "\nN = ", group_size$size)
icc_interaction_plus_dt <- group_size[icc_interaction_plus_dt, on = "grp"]
icc_interaction_plus_dt$grp_size <- factor(icc_interaction_plus_dt$grp_size, levels = unique(group_size$grp_size))
var_explained_manuscript <- icc_interaction_plus_dt[,c("grp", "percent", "P", "gene")]
colnames(var_explained_manuscript) <- c("Covariate", "Percent_Variance_Explained", "P", "ENSG")
##### Add gene IDs for easy identification downstream #####
GeneConversion1 <- read_delim("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/data/Expression_200128_A00152_0196_BH3HNFDSXY/GE/DRENEA_1/outs/filtered_feature_bc_matrix/features.tsv.gz", col_names = F, delim = "\t")
GeneConversion2 <- read_delim("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/data/Expression_200128_A00152_0196_BH3HNFDSXY/GE/Village_B_1_week/outs/filtered_feature_bc_matrix/features.tsv.gz", col_names = F, delim = "\t")
GeneConversion <- unique(rbind(GeneConversion1, GeneConversion2))
GeneConversion <- GeneConversion[!duplicated(GeneConversion$X1),]
GeneConversion$X3 <- NULL
colnames(GeneConversion) <- c("ENSG", "Gene_ID")
GeneConversion <- data.table(GeneConversion)
### Add the gene IDs to the icc_dt ###
var_explained_manuscript <- GeneConversion[var_explained_manuscript, on =c("ENSG")]
fwrite(var_explained_manuscript, paste0(outdir, "multi-passage_variance_explained_manuscript.tsv"), sep = "\t")
##### Check difference in percent explained with and without interactions #####
icc_interaction_dt_joined <- icc_dt[icc_interaction_dt, on = c("grp", "gene")]
icc_interaction_dt_joined$difference <- icc_interaction_dt_joined$percent - icc_interaction_dt_joined$i.percent
pRaincloud_dif <- ggplot(icc_interaction_dt_joined, aes(x = difference, y = factor(grp_size, levels = rev(levels(grp_size))), fill = factor(grp, levels = rev(selected_vars)))) +
geom_density_ridges(stat = "binline", bins = 90, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..), alpha = 0.75) +
geom_boxplot(size = 0.5,width = .15, outlier.size = 0.25, position = position_nudge(y=-0.12), alpha = 0.75) +
coord_cartesian(xlim = c(1.2, NA), clip = "off") +
theme_classic() +
theme(axis.title.y=element_blank()) +
xlab("Percent Variance Explained") +
scale_y_discrete(expand = c(0.03, 0)) +
scale_fill_manual(values = var_colors)
ggsave(pRaincloud_dif, filename = paste0(outdir, "variance_explained_interaction_difference_raincloud.png"), height = 8, width = 7)
ggsave(pRaincloud_dif, filename = paste0(outdir, "variance_explained_interaction_difference_raincloud.pdf"), height = 8, width = 7)
##### Make a figure of stacked variance explained #####
### Order based on line variance explained ###
genes_list <- list()
for (group in c("Line", "Passage", "Line:Passage", "Residual")){
genes_list[[group]] <- icc_dt[grp == group][rev(order(percent_round))]$gene
}
genes <- unique(unlist(genes_list))
icc_dt$gene <- factor(icc_dt$gene, levels = genes)
icc_dt$grp <- factor(icc_dt$grp, levels= rev(c("Line", "Passage", "Line:Passage", "Residual")))
## First on line percent, then village percent ##
bar_proportions <- ggplot(icc_dt, aes(x = gene, y = percent, fill = grp)) +
geom_bar(position="stack", stat="identity") +
theme_classic() +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()) +
scale_fill_manual(values = var_colors)
ggsave(bar_proportions, filename = paste0(outdir, "variance_explained_bar.png"), width = 20)
### Try boxplot ###
boxplot <- ggplot(icc_dt, aes(x = factor(grp, levels = rev(levels(grp))), y = percent, fill = factor(grp, levels = rev(levels(grp))), color = factor(grp, levels = rev(levels(grp))))) +
geom_boxplot(alpha = 0.5, size = 0.5) +
theme_classic() +
xlab("Covariate") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
legend.position="none") +
scale_fill_manual(values = var_colors) +
scale_color_manual(values = var_colors)
ggsave(boxplot, filename = paste0(outdir, "variance_explained_box.png"), height = 3, width = 2)
### Try ridgplots ###
pRidges <- ggplot(icc_dt[grp != "Residual"], aes(x = percent, y = factor(grp, levels = rev(levels(grp))), fill = factor(grp, levels = rev(levels(grp))))) +
geom_density_ridges(stat = "binline", bins = 200, scale = 0.95, draw_baseline = FALSE) +
# geom_density_ridges() +
theme_classic()
ggsave(pRidges, filename = paste0(outdir, "variance_explained_ridge.png"), height = 8, width = 10)
### Try ridgplots ###
pRidges_pop <- ggplot(icc_dt[grp != "Residual"][percent > 10], aes(x = percent, y = factor(grp, levels = rev(levels(grp))), fill = factor(grp, levels = rev(levels(grp))))) +
geom_density_ridges(stat = "binline", bins = 180, scale = 0.95, draw_baseline = FALSE) +
# geom_density_ridges() +
theme_classic()
ggsave(pRidges_pop, filename = paste0(outdir, "variance_explained_ridge_pop.png"), height = 8, width = 10)
# pRaincloud <- ggplot(icc_dt, aes(x = percent, y = factor(grp_size, levels = rev(levels(grp_size))), fill = factor(grp, levels = rev(vars)))) +
# geom_density_ridges(stat = "binline", bins = 90, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..), alpha = 0.75) +
# geom_boxplot(size = 0.5,width = .15, outlier.size = 0.25, position = position_nudge(y=-0.12), alpha = 0.75) +
# coord_cartesian(xlim = c(1.2, NA), clip = "off") +
# theme_classic() +
# theme(axis.title.y=element_blank()) +
# xlab("Percent Variance Explained") +
# scale_y_discrete(expand = c(0.03, 0)) +
# scale_fill_manual(values = var_colors)
pRaincloud <- ggplot(icc_dt, aes(x = percent, y = factor(grp_size, levels = rev(levels(grp_size))), fill = factor(grp, levels = rev(vars)))) +
geom_density_ridges(size = 0.1,stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..)) +
geom_point(size =1, position = position_nudge(y=-0.09), shape = "|", aes(color = factor(grp, levels = rev(vars)))) +
coord_cartesian(xlim = c(1.2, NA), clip = "off") +
theme_classic() +
theme(axis.title.y=element_blank()) +
xlab("Percent Variance Explained") +
scale_y_discrete(expand = c(0.1, 0)) +
scale_fill_manual(values = var_colors, name = "Variable") +
scale_color_manual(values = var_colors, name = "Variable") +
geom_vline(xintercept = 1, lty="11", color = "grey50", size = 0.5)
ggsave(pRaincloud, filename = paste0(outdir, "variance_explained_raincloud.png"), height = 2, width = 5)
ggsave(pRaincloud, filename = paste0(outdir, "variance_explained_raincloud.pdf"), height = 2, width = 5)
icc_interaction_plus_dt$grp_size <- factor(icc_interaction_plus_dt$grp_size, levels = c("Line\nN = 12076", "Passage\nN = 3620", "Line:Passage\nN = 2033", "Residual\nN = 12076"))
# pRaincloud_interaction <- ggplot(icc_interaction_plus_dt, aes(x = percent, y = grp_size, fill = factor(grp, levels = rev(vars)))) +
# geom_density_ridges(size = 0.1, stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity.., fill = factor(grp, levels = rev(vars))), alpha = 0.75) +
# geom_boxplot(outlier.shape=20, size = 0.1,width = .15, outlier.size = 0.01, position = position_nudge(y=-0.12), alpha = 0.75) +
# coord_cartesian(xlim = c(1.2, NA), clip = "off") +
# theme_classic() +
# theme(axis.title.y=element_blank()) +
# xlab("Percent Variance Explained") +
# scale_y_discrete(expand = c(0.03, 0)) +
# scale_fill_manual(values = var_colors) +
# scale_color_manual(values = var_colors) +
# geom_vline(xintercept = 1,color = "grey70", size = 0.4, lty="11")
pRaincloud_interaction <- ggplot(icc_interaction_plus_dt, aes(x = percent, y = factor(grp_size, levels = levels(grp_size)), fill = factor(grp, levels = rev(vars)))) +
geom_density_ridges(size = 0.1,stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..)) +
geom_point(size =1, position = position_nudge(y=-0.09), shape = "|", aes(color = factor(grp, levels = rev(vars)))) +
coord_cartesian(xlim = c(1.2, NA), clip = "off") +
theme_classic() +
theme(axis.title.y=element_blank()) +
xlab("Percent Variance Explained") +
scale_y_discrete(expand = c(0.1, 0)) +
scale_fill_manual(values = var_colors, name = "Variable") +
scale_color_manual(values = var_colors, name = "Variable") +
geom_vline(xintercept = 1, lty="11", color = "grey50", size = 0.5)
ggsave(pRaincloud_interaction, filename = paste0(outdir, "variance_explained_raincloud_interaction.png"), height = 2, width = 4)
ggsave(pRaincloud_interaction, filename = paste0(outdir, "variance_explained_raincloud_interaction.pdf"), height = 2, width = 4)
icc_interaction_sig_list <- list()
for (ensg in unique(icc_interaction_plus_dt$gene)){
icc_interaction_sig_list[[ensg]] <- icc_interaction_plus_dt[gene == ensg][P < 0.05/(nrow(icc_interaction_plus_dt[gene == ensg])-1)]
icc_interaction_sig_list[[ensg]] <- rbind(icc_interaction_sig_list[[ensg]], icc_interaction_plus_dt[gene == ensg & grp == "Residual"])
}
icc_interaction_sig_dt <- do.call(rbind, icc_interaction_sig_list)
group_size <- data.table(table(icc_interaction_sig_dt$grp))
colnames(group_size) <- c("grp", "size")
group_size$grp_size <- paste0(group_size$grp, "\nN = ", group_size$size)
icc_interaction_sig_dt <- group_size[icc_interaction_sig_dt, on = "grp"]
icc_interaction_sig_dt$grp_size <- factor(icc_interaction_sig_dt$grp_size, levels = unique(group_size$grp_size))
grp_size_order <- c("Line\nN = 12076", "Passage\nN = 3620", "Line:Passage\nN = 2033", "Residual\nN = 12076")
mean(icc_interaction_sig_dt[grp == "Line"]$percent)
mean(icc_interaction_sig_dt[grp == "Passage"]$percent)
mean(icc_interaction_sig_dt[grp == "Line:Passage"]$percent)
mean(icc_interaction_sig_dt[grp == "Line" & percent > 1]$percent)
mean(icc_interaction_sig_dt[grp == "Passage" & percent > 1]$percent)
mean(icc_interaction_sig_dt[grp == "Line:Passage" & percent > 1]$percent)
# pRaincloud_interaction_sig <- ggplot(icc_interaction_sig_dt, aes(x = percent, y = factor(grp_size, levels = grp_size_order), fill = factor(grp, levels = rev(vars)))) +
# geom_density_ridges(stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..), alpha = 0.75) +
# geom_boxplot(size = 0.5,width = .15, outlier.size = 0.15, position = position_nudge(y=-0.12), alpha = 0.75) +
# coord_cartesian(xlim = c(1.2, NA), clip = "off") +
# theme_classic() +
# theme(axis.title.y=element_blank()) +
# xlab("Percent Variance Explained") +
# scale_y_discrete(expand = c(0.03, 0)) +
# scale_fill_manual(values = var_colors) +
# geom_vline(xintercept = 1, lty="11", color = "grey50", size = 0.5)
pRaincloud_interaction_sig <- ggplot(icc_interaction_sig_dt, aes(x = percent, y = factor(grp_size, levels = grp_size_order), fill = factor(grp, levels = rev(vars)))) +
geom_density_ridges(size = 0.1,stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..)) +
geom_point(size =1, position = position_nudge(y=-0.09), shape = "|", aes(color = factor(grp, levels = rev(vars)))) +
coord_cartesian(xlim = c(1.2, NA), clip = "off") +
theme_classic() +
theme(axis.title.y=element_blank()) +
xlab("Percent Variance Explained") +
scale_y_discrete(expand = c(0.07, 0)) +
scale_fill_manual(values = var_colors, name = "Variable") +
scale_color_manual(values = var_colors, name = "Variable") +
geom_vline(xintercept = 1, lty="11", color = "grey50", size = 0.5)
ggsave(pRaincloud_interaction_sig, filename = paste0(outdir, "variance_explained_raincloud_interaction_significant.png"), height = 2, width = 4)
ggsave(pRaincloud_interaction_sig, filename = paste0(outdir, "variance_explained_raincloud_interaction_significant.pdf"), height = 2, width = 4)
total <- icc_interaction_sig_dt[,.(count = .N), by = .(grp)]
total_less1pct <- icc_interaction_sig_dt[percent <= 1][,.(count_less_1pct = .N), by = .(grp)]
total_less5pct <- icc_interaction_sig_dt[percent <= 5][,.(count_less_5pct = .N), by = .(grp)]
total_less10pct <- icc_interaction_sig_dt[percent <= 10][,.(count_less_10pct = .N), by = .(grp)]
summary <- total[total_less1pct, on = "grp"]
summary <- summary[total_less5pct, on = "grp"]
summary <- summary[total_less10pct, on = "grp"]
summary$count_greater_1pct <- summary$count - summary$count_less_1pct
summary$count_greater_5pct <- summary$count - summary$count_less_5pct
summary$count_greater_10pct <- summary$count - summary$count_less_10pct
summary$percent_1pct <- (summary$count_less_1pct/summary$count)*100
summary$percent_5pct <- (summary$count_less_5pct/summary$count)*100
summary$percent_10pct <- (summary$count_less_10pct/summary$count)*100
# pRaincloud_interaction_sig_1pct <- ggplot(icc_interaction_sig_dt[percent >= 1], aes(x = percent, y = factor(grp_size, levels = grp_size_order), fill = factor(grp, levels = rev(vars)))) +
# geom_density_ridges(stat = "binline", bins = 90, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..), alpha = 0.75) +
# geom_boxplot(size = 0.5,width = .15, outlier.size = 0.25, position = position_nudge(y=-0.12), alpha = 0.75) +
# coord_cartesian(xlim = c(1.2, NA), clip = "off") +
# theme_classic() +
# theme(axis.title.y=element_blank()) +
# xlab("Percent Variance Explained") +
# scale_y_discrete(expand = c(0.03, 0)) +
# scale_fill_manual(values = var_colors) +
# geom_vline(xintercept = 1, linetype = "dashed", color = "firebrick3")
pRaincloud_interaction_sig_1pct <- ggplot(icc_interaction_sig_dt[percent >= 1], aes(x = percent, y = factor(grp_size, levels = grp_size_order), fill = factor(grp, levels = rev(vars)))) +
geom_density_ridges(size = 0.1,stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..)) +
geom_point(size =1, position = position_nudge(y=-0.09), shape = "|", aes(color = factor(grp, levels = rev(vars)))) +
coord_cartesian(xlim = c(1.2, NA), clip = "off") +
theme_classic() +
theme(axis.title.y=element_blank()) +
xlab("Percent Variance Explained") +
scale_y_discrete(expand = c(0.07, 0)) +
scale_fill_manual(values = var_colors, name = "Variable") +
scale_color_manual(values = var_colors, name = "Variable") +
geom_vline(xintercept = 1, lty="11", color = "grey50", size = 0.5)
ggsave(pRaincloud_interaction_sig_1pct, filename = paste0(outdir, "variance_explained_raincloud_interaction_significant_1pct.png"), height = 2, width = 4)
ggsave(pRaincloud_interaction_sig_1pct, filename = paste0(outdir, "variance_explained_raincloud_interaction_significant_1pct.pdf"), height = 2, width = 4)
##### Pull just the significant variances genome-wide #####
icc_interaction_plus_dt$fdr <- p.adjust(icc_interaction_plus_dt$P, method="fdr")
icc_interaction_sig_gw_dt <- icc_interaction_plus_dt[fdr < 0.05 | is.na(fdr)]
group_size_sig_gw <- data.table(table(icc_interaction_sig_gw_dt$grp))
colnames(group_size_sig_gw) <- c("grp", "size")
group_size_sig_gw$grp_size <- paste0(group_size_sig_gw$grp, "\nN = ", formatC(group_size_sig_gw$size, format="d", big.mark=","))
icc_interaction_sig_gw_dt <- group_size_sig_gw[icc_interaction_sig_gw_dt, on = "grp"]
icc_interaction_sig_gw_dt$grp_size <- factor(icc_interaction_sig_gw_dt$grp_size, levels = unique(group_size_sig_gw$grp_size))
group_size_sig_gw_order <- c("Line\nN = 750", "Passage\nN = 750", "Line:Passage\nN = 619", "Residual\nN = 750")
pRaincloud_interaction_sig_gw <- ggplot(icc_interaction_sig_gw_dt, aes(x = percent, y = factor(grp_size, levels = group_size_sig_gw_order), fill = factor(grp, levels = rev(vars)))) +
geom_density_ridges(stat = "binline", bins = 100, scale = 0.7, draw_baseline = FALSE, aes(height =..ndensity..), alpha = 0.75) +
geom_boxplot(size = 0.5,width = .15, outlier.size = 0.25, position = position_nudge(y=-0.12), alpha = 0.75) +
coord_cartesian(xlim = c(1.2, NA), clip = "off") +
theme_classic() +
theme(axis.title.y=element_blank()) +
xlab("Percent Variance Explained") +
scale_y_discrete(expand = c(0.03, 0)) +
scale_fill_manual(values = var_colors) +
geom_vline(xintercept = 1, linetype = "dashed", color = "firebrick3") +
labs(fill="Covariate")
ggsave(pRaincloud_interaction_sig_gw, filename = paste0(outdir, "variance_explained_raincloud_interaction_significant_genome_wide.png"), height = 8, width = 7)
ggsave(pRaincloud_interaction_sig_gw, filename = paste0(outdir, "variance_explained_raincloud_interaction_significant_genome_wide.pdf"), height = 8, width = 7)
icc_interaction_sig_dt[gene == "ENSG00000106153"]
icc_dt[gene == "ENSG00000106153"]
icc_interaction_plus_dt[gene == "ENSG00000106153"]
icc_interaction_sig_gw_dt[gene == "ENSG00000106153"]
icc_interaction_sig_gw_dt2[gene == "ENSG00000106153"]
##### Add gene IDs for easy identification downstream #####
GeneConversion1 <- read_delim("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/data/Expression_200128_A00152_0196_BH3HNFDSXY/GE/DRENEA_1/outs/filtered_feature_bc_matrix/features.tsv.gz", col_names = F, delim = "\t")
GeneConversion2 <- read_delim("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/data/Expression_200128_A00152_0196_BH3HNFDSXY/GE/Village_B_1_week/outs/filtered_feature_bc_matrix/features.tsv.gz", col_names = F, delim = "\t")
GeneConversion <- unique(rbind(GeneConversion1, GeneConversion2))
GeneConversion <- GeneConversion[!duplicated(GeneConversion$X1),]
GeneConversion$X3 <- NULL
colnames(GeneConversion) <- c("gene", "Gene_ID")
GeneConversion <- data.table(GeneConversion)
### Add the gene IDs to the icc_dt ###
icc_interaction_sig_gw_dt <- GeneConversion[icc_interaction_sig_gw_dt, on = "gene"]
icc_interaction_sig_gw_dt[grp == "Cryopreserved"& percent_round > 1][rev(order(percent))]$Gene_ID
head(icc_interaction_sig_gw_dt[grp == "Cryopreserved" & percent_round > 1][rev(order(percent))][,c("Gene_ID", "percent_round")], n = 50)
icc_interaction_sig_gw_dt[grp == "Line"][rev(order(percent))]$Gene_ID
head(icc_interaction_sig_gw_dt[grp == "Line" & percent_round > 1][rev(order(percent))][,c("Gene_ID", "percent_round")], n = 50)
icc_interaction_sig_gw_dt[grp == "Village"][rev(order(percent))]$Gene_ID
head(icc_interaction_sig_gw_dt[grp == "Village" & percent_round > 1][rev(order(percent))][,c("Gene_ID", "percent_round")], n = 50)
fwrite(icc_interaction_sig_gw_dt, paste0(outdir, "sig_results.tsv.gz"), sep = "\t", compress = "gzip")
## Highlight
## X chromosome genes - wouldn't expect these to be Line-biased because expressed by both males and females
## Y chromosome genes - should be line-biased because expressed by only males and have some male(s) and some female(s)
## mt genes
## ribosomal genes
## look at gsea and kegg pathways for each
## Read in gtf used as reference and pull just X, Y or MT chromosome genes from it, use ribosomal file for rb genes
gtf <- fread("/directflow/GWCCGPipeline/projects/reference/refdata-cellranger-GRCh38-3.0.0/genes/genes.gtf", sep = "\t", autostart = 6, header = FALSE)
gtf_genes <- gtf[!(grep("transcript_id", V9))]
gtf_genes$V9 <- gsub("gene_id \"", "",gtf_genes$V9 ) %>%
gsub("\"; gene_version \"", ";", .) %>%
gsub("\"; gene_name \"", ";", .) %>%
gsub("\"; gene_source \"", ";", .) %>%
gsub("\"; gene_biotype \"", ";", .) %>%
gsub("\"", "", .)
gtf_genes[, c("gene_id", "gene_version", "gene_name", "gene_source", "gene_biotype") := data.table(str_split_fixed(V9,";", 5))]
icc_interaction_plus_dt <- GeneConversion[icc_interaction_plus_dt, on = "gene"]
X_chromosome_genes <- gtf_genes[V1 == "X"]
X_genelist <- X_chromosome_genes$gene_id[X_chromosome_genes$gene_id %in% genes]
Y_chromosome_genes <- gtf_genes[V1 == "Y"]
Y_genelist <- Y_chromosome_genes$gene_id[Y_chromosome_genes$gene_id %in% genes]
MT_chromosome_genes <- gtf_genes[V1 == "MT"]
MT_genelist <- MT_chromosome_genes$gene_id[MT_chromosome_genes$gene_id %in% genes]
RbGeneList <- read.delim(file = "/directflow/SCCGGroupShare/projects/DrewNeavin/References/RibosomalGeneList_GeneID_ENSG.txt")
Rb_genelist <- RbGeneList$ENSG[RbGeneList$ENSG %in% genes]
### Make stacked bar plots of the variance explained by different factors for these gene groups
## Figure of x chromosome genes ##
icc_x <- icc_interaction_plus_dt[data.table(gene = icc_interaction_plus_dt[grp == "Residual"][gene %in% X_genelist][order(percent_round)]$gene), on = "gene"]
icc_x$grp <- factor(icc_x$grp, levels = rev(selected_vars))
icc_x$gene <- factor(icc_x$gene, levels = unique(icc_x$gene))
bar_proportions_x <- ggplot(icc_x, aes(x = gene, y = percent, fill = grp)) +
geom_bar(position="stack", stat="identity", alpha = 0.75) +
theme_classic() +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5)) +
scale_fill_manual(values = var_colors) +
ggtitle("Variance Explained of\nX Chromosome Genes")
ggsave(bar_proportions_x, filename = paste0(outdir, "variance_explained_bar_x_genes.png"), width = 20)
## Figure of y chromosome genes ##
icc_y <- icc_interaction_plus_dt[data.table(gene = icc_interaction_plus_dt[grp == "Residual"][gene %in% Y_genelist][order(percent_round)]$gene), on = "gene"]
icc_y$grp <- factor(icc_y$grp, levels = rev(selected_vars))
icc_y$gene <- factor(icc_y$gene, levels = unique(icc_y$gene))
icc_y$Gene_ID <- factor(icc_y$Gene_ID, levels = unique(icc_y$Gene_ID))
bar_proportions_y <- ggplot(icc_y, aes(x = Gene_ID, y = percent, fill = grp)) +
geom_bar(position="stack", stat="identity", alpha = 0.75) +
theme_classic() +
theme(axis.title.x=element_blank(),
# axis.text.x=element_blank(),
# axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
scale_fill_manual(values = var_colors) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Variance Explained of\nY Chromosome Genes") +
ylab("Percent")
ggsave(bar_proportions_y, filename = paste0(outdir, "variance_explained_bar_y_genes.png"), width = 4.5, height = 4.5)
ggsave(bar_proportions_y, filename = paste0(outdir, "variance_explained_bar_y_genes.pdf"), width = 4.5, height = 4.5)
## Figure of mt chromosome genes ##
icc_mt <- icc_interaction_plus_dt[data.table(gene = icc_interaction_plus_dt[grp == "Residual"][gene %in% MT_genelist][order(percent_round)]$gene), on = "gene"]
icc_mt$grp <- factor(icc_mt$grp, levels = rev(selected_vars))
icc_mt$gene <- factor(icc_mt$gene, levels = unique(icc_mt$gene))
icc_mt$Gene_ID <- factor(icc_mt$Gene_ID, levels = unique(icc_mt$Gene_ID))
bar_proportions_mt <- ggplot(icc_mt, aes(x = Gene_ID, y = percent, fill = grp)) +
geom_bar(position="stack", stat="identity", alpha = 0.75) +
theme_classic() +
theme(axis.title.x=element_blank(),
# axis.text.x=element_blank(),
# axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
scale_fill_manual(values = var_colors) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Variance Explained of\nMitochondrial Genes") +
ylab("Percent")
ggsave(bar_proportions_mt, filename = paste0(outdir, "variance_explained_bar_mt_genes.png"), width = 4.5, height = 4.5)
ggsave(bar_proportions_mt, filename = paste0(outdir, "variance_explained_bar_mt_genes.pdf"), width = 4.5, height = 4.5)
## Figure of mt chromosome genes ##
icc_rb <- icc_interaction_plus_dt[data.table(gene = icc_interaction_plus_dt[grp == "Residual"][gene %in% Rb_genelist][order(percent_round)]$gene), on = "gene"]
icc_rb$grp <- factor(icc_rb$grp, levels = rev(selected_vars))
icc_rb$gene <- factor(icc_rb$gene, levels = unique(icc_rb$gene))
bar_proportions_rb <- ggplot(icc_rb, aes(x = gene, y = percent, fill = grp)) +
geom_bar(position="stack", stat="identity", alpha = 0.75) +
theme_classic() +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5)) +
scale_fill_manual(values = var_colors) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Variance Explained\nof Ribosomal Genes")
ggsave(bar_proportions_rb, filename = paste0(outdir, "variance_explained_bar_rb_genes.png"), width = 10, height = 4)
ggsave(bar_proportions_rb, filename = paste0(outdir, "variance_explained_bar_rb_genes.pdf"), width = 10, height = 4)
### Plot Pluripotency Genes ###
pluri_genes <- fread(paste0(dir,"data/pluripotency_genes.tsv"), sep = "\t", col.names = "Gene_ID", header = FALSE)
pluri_genes <- GeneConversion[pluri_genes, on = "Gene_ID"]
icc_dt_pluri_genes <- icc_interaction_plus_dt[pluri_genes,on = c("gene")]
icc_dt_pluri_genes$grp <- factor(icc_dt_pluri_genes$grp, levels = rev(selected_vars))
icc_dt_pluri_genes <- icc_dt_pluri_genes[data.table(gene = icc_dt_pluri_genes[grp == "Residual"][order(percent)]$gene), on = "gene"]
icc_dt_pluri_genes$Gene_ID <- factor(icc_dt_pluri_genes$Gene_ID, levels = unique(icc_dt_pluri_genes$Gene_ID))
pPluri_Genes_Cont <- ggplot() +
geom_bar(data = icc_dt_pluri_genes, aes(Gene_ID, percent, fill = grp), position = "stack", stat = "identity", alpha = 0.75) +
theme_classic() +
# facet_wrap(Gene_ID ~ ., nrow = 3) +
scale_fill_manual(values = var_colors) +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
ylab("Percent Gene Expression Variance Explained") +
ggtitle("Variance Explained of\nPluripotency Genes") +
theme(axis.title.x=element_blank())
ggsave(pPluri_Genes_Cont, filename = paste0(outdir, "Pluripotent_Gene_Variable_Contributions.png"), width = 6, height = 4.5)
ggsave(pPluri_Genes_Cont, filename = paste0(outdir, "Pluripotent_Gene_Variable_Contributions.pdf"), width = 6, height = 4.5)
##### check for variance explained for eQTL genes (from Kilpinen et al) that are #####
eqtls <- fread("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/eQTL_check/KilpinenOverlap/gene_snp_list.tsv", sep = "\t")
eqtls_icc <- icc_interaction_plus_dt[unique(eqtls[,"gene"]), on = "gene"]
eqtls_icc$grp <- factor(eqtls_icc$grp, levels = rev(vars))
eqtls_icc <- eqtls_icc[data.table(gene = eqtls_icc[grp == "Residual"][order(percent)]$gene), on = "gene"]
eqtls_icc$Gene_ID <- factor(eqtls_icc$Gene_ID, levels = unique(eqtls_icc$Gene_ID))
group_size_eqtl <- data.table(table(eqtls_icc$grp))
colnames(group_size_eqtl) <- c("grp", "size")
group_size_eqtl$grp_size <- paste0(group_size_eqtl$grp, "\nN = ", group_size_eqtl$size)
eqtls_icc <- group_size_eqtl[eqtls_icc, on = "grp"]
grp_size_order_eqtl <- c("Line\nN = 2371", "Village\nN = 1836", "Site\nN = 2461", "Replicate\nN = 869", "Line:Village\nN = 367", "Line:Site\nN = 911", "Village:Site\nN = 898","Replicate:Village\nN = 305", "Replicate:Line\nN = 25", "Replicate:Site\nN = 78", "Residual\nN = 2542")
eqtls_icc$grp_size <- factor(eqtls_icc$grp_size, levels = grp_size_order_eqtl)
eqtls_icc_1pct <- eqtls_icc
for (ensg in unique(eqtls_icc$gene)){
if (!any(eqtls_icc[gene == ensg & grp != "Residual"]$percent > 1)){
eqtls_icc_1pct <- eqtls_icc_1pct[gene != ensg]
}
}
eqtls_icc_1pct_grouped_list <- list()
for (ensg in unique(eqtls_icc_1pct$gene)){
group <- eqtls_icc_1pct[gene == ensg & grp != "Residual"][which.max(percent)]$grp
eqtls_icc_1pct_grouped_list[[group]][[ensg]] <- eqtls_icc_1pct[gene == ensg]
}
eqtls_icc_1pct_grouped <- lapply(eqtls_icc_1pct_grouped_list, function(x) do.call(rbind, x))
eqtls_icc_1pct_grouped <- lapply(names(eqtls_icc_1pct_grouped), function(x){
eqtls_icc_1pct_grouped[[x]]$largest_contributor <- x
return(eqtls_icc_1pct_grouped[[x]])
})
eqtls_icc_1pct_grouped_dt <- do.call(rbind, eqtls_icc_1pct_grouped)
eqtls_icc_1pct_grouped_dt$largest_contributor <- factor(eqtls_icc_1pct_grouped_dt$largest_contributor, levels = c("Line", "Village", "Site", "Line:Village", "Line:Site", "Village:Site", "Replicate:Village"))
pPluri_Genes_largest_Cont_eqtl <- ggplot() +
geom_bar(data = eqtls_icc_1pct_grouped_dt, aes(Gene_ID, percent, fill = factor(grp, levels = rev(vars))), position = "stack", stat = "identity", alpha = 0.75) +
theme_classic() +
facet_grid(. ~ largest_contributor, scales = "free_x", space = "free_x") +
scale_fill_manual(values = var_colors) +
ylab("Percent Gene Expression Variance Explained") +
theme(axis.title.x=element_blank(),
axis.text.x = element_blank(),
panel.spacing.x=unit(0, "lines"),
axis.ticks.x = element_blank()) +
geom_hline(yintercept = 1, linetype = "dashed")
# scale_y_discrete(expand = c(0.03, 0))
# scale_x_discrete(expand = c(0.03, 0))
ggsave(pPluri_Genes_largest_Cont_eqtl, filename = paste0(outdir, "eQTL_Genes_Variance_Contributions_1pct_largest_cont.png"), width = 10, height = 4)
ggsave(pPluri_Genes_largest_Cont_eqtl, filename = paste0(outdir, "eQTL_Genes_Variance_Contributions_1pct_largest_cont.pdf"), width = 10, height = 4)
# ### Count number of each chromosome/gene category type
# ### numbers are the numbers of that category that where a significant percent of variance is explained by this variable
# ### percent of that category that where a significant percent of variance is explained by this variable
# x_number <- lapply(genes_list, function(x){
# length(which(x %in% X_chromosome_genes$gene_id))
# })
# x_percent <- lapply(genes_list, function(x){
# length(which(x %in% X_chromosome_genes$gene_id))/length(X_chromosome_genes$gene_id)
# })
# y_number <- lapply(genes_list, function(x){
# length(which(x %in% Y_chromosome_genes$gene_id))
# })
# y_percent <- lapply(genes_list, function(x){
# length(which(x %in% Y_chromosome_genes$gene_id))/length(Y_chromosome_genes$gene_id)
# })
# mt_number <- lapply(genes_list, function(x){
# length(which(x %in% MT_chromosome_genes$gene_id))
# })
# mt_percent <- lapply(genes_list, function(x){
# length(which(x %in% MT_chromosome_genes$gene_id))/length(MT_chromosome_genes$gene_id)
# })
# rb_number <- lapply(genes_list, function(x){
# length(which(x %in% RbGeneList$ENSG))
# })
# rb_percent <- lapply(genes_list, function(x){
# length(which(x %in% RbGeneList$ENSG))/length(RbGeneList$ENSG)
# })
# ### for > 1% var explained
# ### Count number of each chromosome/gene category type
# x_number_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% X_chromosome_genes$gene_id))
# })
# names(x_number_1) <- vars
# x_percent_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% X_chromosome_genes$gene_id))/length(X_chromosome_genes$gene_id)
# })
# names(x_percent_1) <- vars
# y_number_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% Y_chromosome_genes$gene_id))
# })
# names(y_number_1) <- vars
# y_percent_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% Y_chromosome_genes$gene_id))/length(Y_chromosome_genes$gene_id)
# })
# names(y_percent_1) <- vars
# mt_number_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% MT_chromosome_genes$gene_id))
# })
# names(mt_number_1) <- vars
# mt_percent_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% MT_chromosome_genes$gene_id))/length( MT_chromosome_genes$gene_id)
# })
# names(mt_percent_1) <- vars
# rb_number_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% RbGeneList$ENSG))
# })
# names(rb_number_1) <- vars
# rb_percent_1 <- lapply(vars, function(group){
# genes <- icc_dt[grp == group][rev(order(percent_round))][percent > 1]$gene
# length(which(genes %in% RbGeneList$ENSG))/length(RbGeneList$ENSG)
# })
# names(rb_percent_1) <- vars
### Pathway analysis ###
geneList <- lapply(genes_list, function(x){
tmp <- bitr(x, fromType = "ENSEMBL",
toType = c("ENTREZID"),
OrgDb = org.Hs.eg.db)$ENTREZID
tmp[!is.na(tmp)]
})
gg <- list()
kk <- list()
df = as.data.frame(org.Hs.egGO)
go_gene_list = unique(sort(df$gene_id))
dfk = as.data.frame(org.Hs.egPATH)
kegg_gene_list = unique(sort(dfk$gene_id))
for (group in c("Line", "Village", "Cryopreserved", "Replicate","Line:Village", "Line:Cryopreserved", "Village:Cryopreserved", "Replicate:Village", "Replicate:Line", "Replicate:Cryopreserved", "Residual")){
kk[[group]] <- enrichKEGG(gene = geneList[[group]],
universe = geneList[[group]],
organism = 'hsa',
pvalueCutoff = 0.05,
keyType = 'ncbi-geneid')
gg[[group]] <- groupGO(gene = geneList[[group]],
OrgDb = org.Hs.eg.db,
readable = TRUE)
}
hsGO <- godata('org.Hs.eg.db', ont="MF")
sim_results <- list()
vars <- c("Line", "Village", "Cryopreserved", "Replicate","Village:Line", "Line:Cryopreserved", "Village:Cryopreserved", "Replicate:Village", "Replicate:Line", "Replicate:Cryopreserved")
for (group1 in vars){
print(group1)
for (group2 in vars[(grep(paste0("^",group1, "$"), vars) + 1): length(vars)]){
print(group2)
sim_results[[group1]][[group2]] <- clusterSim(geneList[[group1]], geneList[[group2]], semData=hsGO, measure="Wang", combine="BMA")
}
}
# genes2rerun <- c(character())
# for (g in unique(icc_dt$gene)){
# print(g)
# genes2rerun <- c(genes2rerun, as.character(unique(icc_dt[gene == g][P > 0.05/(nrow(icc_dt[gene == g])-1)]$gene)))
# }
# for (gene in genes2rerun){
# print(gene)
# # unlink(paste0(icc_dir,gene,"_icc.rds"))
# print(file.exists(paste0(icc_dir,gene,"_icc.rds")))
# unlink(paste0("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/variance_partition_post_review/gene_separated/fit_models/",gene,"_fitted_models.rds"))
# unlink(paste0("/directflow/SCCGGroupShare/projects/DrewNeavin/iPSC_Village/output/variance_partition_post_review/gene_separated/residuals4qtl/",gene,"_residuals4qtl.rds"))
# }
|
library(h2o)
setwd(".../Kaggle")
train = read.csv("train.csv",header=TRUE,stringsAsFactors = F)
test = read.csv("test.csv",header=TRUE,stringsAsFactors = F)
id_train=train[1]
id_test=test[1]
train = train[,-1] ## remove the 'id' col
test = test[,-1] ## remove the 'id' col
MultiLogLoss <- function(act, pred)
{
eps = 1e-15;
nr <- nrow(pred)
pred = matrix(sapply( pred, function(x) max(eps,x)), nrow = nr)
pred = matrix(sapply( pred, function(x) min(1-eps,x)), nrow = nr)
ll = sum(act*log(pred))
ll = ll * -1/(nrow(act))
return(ll);
}
train_x = train[,-ncol(train)] ## This remove 'id' column
train_x = as.matrix(train_x) ## This just change the format
train_x = matrix(as.numeric(train_x),nrow(train_x),ncol(train_x)) ## This just change the format (change to numeric)
train_y = train[,ncol(train)]
train_y = gsub('Class_','',train_y) ## This change the 'Class_' into numbers
train_y_fac = as.factor(train_y) #R's RF assums type of factor for classif
train_y_num = as.integer(train_y) #xgboost take features in [0,numOfClass)
train_y_matrix = matrix(0,nrow=nrow(train),ncol=9)
for (x in 1:nrow(train)){
train_y_matrix[x,train_y_num[x]]=1
}
train <- cbind(train_x,train_y_matrix)
test_x = test[,-ncol(test)] ## This remove 'id' column
test_x = as.matrix(test_x) ## This just change the format
test_x = matrix(as.numeric(test_x),nrow(test_x),ncol(test_x)) ## This just change the format (change to numeric)
test_y = test[,ncol(test)]
test_y = gsub('Class_','',test_y) ## This change the 'Class_' into numbers
test_y_fac = as.factor(test_y) #xgboost take features in [0,numOfClass)
test_y_num = as.integer(test_y)
test_y_matrix = matrix(0,nrow=nrow(test),ncol=9)
for (x in 1:nrow(test)){
test_y_matrix[x,test_y_num[x]]=1
}
test<- cbind(test_x,test_y_matrix)
# Launch h2o on localhost, using all cores
localH2O = h2o.init()
h2oServer <- h2o.init(nthreads = -1)
train.hex <- as.h2o(object=train)
dim(train.hex)
test.hex <- as.h2o(object=test)
dim(test.hex)
######################################################################
### load data sets and create train/validation split
######################################################################
######################################################################
### parameter tuning with random search
######################################################################
mylogloss_list=0
myparam_list=0
models <- c()
pred <- 0
for (i in 1:9) {
ptm <- proc.time()
rand_activation <- c("RectifierWithDropout", "MaxoutWithDropout")[sample(1:2,1)]
rand_numlayers <- sample(2:3,1)
rand_hidden <- c(sample(90:800, rand_numlayers, T))
rand_l1 <- runif(1, 0, 1e-3)
rand_l2 <- runif(1, 0, 1e-2)
rand_hidden_dropout <- c(runif(rand_numlayers, 0, 0.5))
rand_input_dropout <- runif(1, 0, 0.5)
rand_rho <- runif(1, 0.9, 0.999)
rand_epsilon <- runif(1, 1e-10, 1e-4)
rand_rate <- runif(1, 0.005, 0.02)
rand_rate_decay <- runif(1, 0, 0.66)
rand_momentum <- runif(1, 0, 0.5)
rand_momentum_ramp <- runif(1, 1e-7, 1e-5)
dlmodel <- h2o.deeplearning(x = 1:93,
y = 93+i,
training_frame = train.hex,
validation_frame = test.hex,
rho = rand_rho, epsilon = rand_epsilon,
rate = rand_rate,
rate_decay = rand_rate_decay,
nesterov_accelerated_gradient = T,
momentum_start = rand_momentum,
momentum_ramp = rand_momentum_ramp,
activation = rand_activation,
hidden = rand_hidden,
l1 = rand_l1,
l2 = rand_l2,
input_dropout_ratio = rand_input_dropout,
hidden_dropout_ratios = rand_hidden_dropout,
epochs = 20
)
models <- c(models, dlmodel)
pred_buffer <- as.data.frame(h2o.predict(dlmodel, train.hex))
#pred <- as.data.frame(h2o.predict(dlmodel, test.hex))
#pred_buffer <-round(pred$predict)
actual = matrix(0,nrow=nrow(train),ncol=9)
for (x in 1:nrow(train)){
actual[x,train_y_num[x]]=1
}
pred_matrix = matrix(0,nrow=nrow(actual),ncol=ncol(actual))
for (x in 1:nrow(actual)){
if ((pred_buffer[x]<10) && (pred_buffer[x]>0))
{
pred_matrix[x,pred_buffer[x]]=1
}
}
mylogloss <- MultiLogLoss(actual, pred_matrix)
ptm2 <- proc.time()-ptm
message(mylogloss, " __ calculation costs ", ptm2[1])
#myparam_list=rbind(myparam_list,param_xgb)
mylogloss_list= rbind(mylogloss_list,mylogloss)
}
|
/[R] Step1_nn_h2o_randomSearch.R
|
no_license
|
gokul180288/-Kaggle-OttoGroup
|
R
| false | false | 4,959 |
r
|
library(h2o)
setwd(".../Kaggle")
train = read.csv("train.csv",header=TRUE,stringsAsFactors = F)
test = read.csv("test.csv",header=TRUE,stringsAsFactors = F)
id_train=train[1]
id_test=test[1]
train = train[,-1] ## remove the 'id' col
test = test[,-1] ## remove the 'id' col
MultiLogLoss <- function(act, pred)
{
eps = 1e-15;
nr <- nrow(pred)
pred = matrix(sapply( pred, function(x) max(eps,x)), nrow = nr)
pred = matrix(sapply( pred, function(x) min(1-eps,x)), nrow = nr)
ll = sum(act*log(pred))
ll = ll * -1/(nrow(act))
return(ll);
}
train_x = train[,-ncol(train)] ## This remove 'id' column
train_x = as.matrix(train_x) ## This just change the format
train_x = matrix(as.numeric(train_x),nrow(train_x),ncol(train_x)) ## This just change the format (change to numeric)
train_y = train[,ncol(train)]
train_y = gsub('Class_','',train_y) ## This change the 'Class_' into numbers
train_y_fac = as.factor(train_y) #R's RF assums type of factor for classif
train_y_num = as.integer(train_y) #xgboost take features in [0,numOfClass)
train_y_matrix = matrix(0,nrow=nrow(train),ncol=9)
for (x in 1:nrow(train)){
train_y_matrix[x,train_y_num[x]]=1
}
train <- cbind(train_x,train_y_matrix)
test_x = test[,-ncol(test)] ## This remove 'id' column
test_x = as.matrix(test_x) ## This just change the format
test_x = matrix(as.numeric(test_x),nrow(test_x),ncol(test_x)) ## This just change the format (change to numeric)
test_y = test[,ncol(test)]
test_y = gsub('Class_','',test_y) ## This change the 'Class_' into numbers
test_y_fac = as.factor(test_y) #xgboost take features in [0,numOfClass)
test_y_num = as.integer(test_y)
test_y_matrix = matrix(0,nrow=nrow(test),ncol=9)
for (x in 1:nrow(test)){
test_y_matrix[x,test_y_num[x]]=1
}
test<- cbind(test_x,test_y_matrix)
# Launch h2o on localhost, using all cores
localH2O = h2o.init()
h2oServer <- h2o.init(nthreads = -1)
train.hex <- as.h2o(object=train)
dim(train.hex)
test.hex <- as.h2o(object=test)
dim(test.hex)
######################################################################
### load data sets and create train/validation split
######################################################################
######################################################################
### parameter tuning with random search
######################################################################
mylogloss_list=0
myparam_list=0
models <- c()
pred <- 0
for (i in 1:9) {
ptm <- proc.time()
rand_activation <- c("RectifierWithDropout", "MaxoutWithDropout")[sample(1:2,1)]
rand_numlayers <- sample(2:3,1)
rand_hidden <- c(sample(90:800, rand_numlayers, T))
rand_l1 <- runif(1, 0, 1e-3)
rand_l2 <- runif(1, 0, 1e-2)
rand_hidden_dropout <- c(runif(rand_numlayers, 0, 0.5))
rand_input_dropout <- runif(1, 0, 0.5)
rand_rho <- runif(1, 0.9, 0.999)
rand_epsilon <- runif(1, 1e-10, 1e-4)
rand_rate <- runif(1, 0.005, 0.02)
rand_rate_decay <- runif(1, 0, 0.66)
rand_momentum <- runif(1, 0, 0.5)
rand_momentum_ramp <- runif(1, 1e-7, 1e-5)
dlmodel <- h2o.deeplearning(x = 1:93,
y = 93+i,
training_frame = train.hex,
validation_frame = test.hex,
rho = rand_rho, epsilon = rand_epsilon,
rate = rand_rate,
rate_decay = rand_rate_decay,
nesterov_accelerated_gradient = T,
momentum_start = rand_momentum,
momentum_ramp = rand_momentum_ramp,
activation = rand_activation,
hidden = rand_hidden,
l1 = rand_l1,
l2 = rand_l2,
input_dropout_ratio = rand_input_dropout,
hidden_dropout_ratios = rand_hidden_dropout,
epochs = 20
)
models <- c(models, dlmodel)
pred_buffer <- as.data.frame(h2o.predict(dlmodel, train.hex))
#pred <- as.data.frame(h2o.predict(dlmodel, test.hex))
#pred_buffer <-round(pred$predict)
actual = matrix(0,nrow=nrow(train),ncol=9)
for (x in 1:nrow(train)){
actual[x,train_y_num[x]]=1
}
pred_matrix = matrix(0,nrow=nrow(actual),ncol=ncol(actual))
for (x in 1:nrow(actual)){
if ((pred_buffer[x]<10) && (pred_buffer[x]>0))
{
pred_matrix[x,pred_buffer[x]]=1
}
}
mylogloss <- MultiLogLoss(actual, pred_matrix)
ptm2 <- proc.time()-ptm
message(mylogloss, " __ calculation costs ", ptm2[1])
#myparam_list=rbind(myparam_list,param_xgb)
mylogloss_list= rbind(mylogloss_list,mylogloss)
}
|
# Load train data and look for good parameters using grid search on a subset of the training set
source('load_and_clean.R')
source('imputation.R')
source('typing.R')
train = full_train[1:53460, ]
test = full_train[53461:59400, ]
library(ranger)
library(caret)
# Manual Search
control <- trainControl(method="cv", number=5, search="grid")
tunegrid <- expand.grid(mtry=c(13:15))
ntree <- 1500
print(paste("CV for ", ntree, " trees"))
set.seed(1)
rf_gridsearch_1 <- train(status_group~., data=train, method="ranger", tuneGrid=tunegrid, trControl=control, num.trees=ntree, verbose=TRUE)
print(rf_gridsearch_1)
plot(rf_gridsearch_1)
|
/15_var/rf_cv.R
|
no_license
|
mbahri/pumpitup
|
R
| false | false | 632 |
r
|
# Load train data and look for good parameters using grid search on a subset of the training set
source('load_and_clean.R')
source('imputation.R')
source('typing.R')
train = full_train[1:53460, ]
test = full_train[53461:59400, ]
library(ranger)
library(caret)
# Manual Search
control <- trainControl(method="cv", number=5, search="grid")
tunegrid <- expand.grid(mtry=c(13:15))
ntree <- 1500
print(paste("CV for ", ntree, " trees"))
set.seed(1)
rf_gridsearch_1 <- train(status_group~., data=train, method="ranger", tuneGrid=tunegrid, trControl=control, num.trees=ntree, verbose=TRUE)
print(rf_gridsearch_1)
plot(rf_gridsearch_1)
|
#normal distribution mean=80, sd=10
x <- seq(40, 120, length=300)
x
y <- dnorm(x, mean=80, sd=10)
y
plot(x, y)
plot(x, y, type="l")
plot(x, y, type="l", col="red")
#lines(x, dnorm(x, mean=80, sd=25), col="blue")
#a. probability between 65~75
x2 <- seq(65, 75, length=200)
y2 <- dnorm(x2, mean=80, sd=10)
polygon(c(65,x2,75), c(0, y2, 0), col="gray")
pnorm(75, mean=80, sd=10)-pnorm(65, mean=80, sd=10)
#pnorm
#qnorm
#dnorm
#rnorm
#b. probability of over 92
#2 Ways
#First
pnorm(92, mean=80, sd=10, lower.tail=FALSE)
#Second
1-pnorm(92, mean=80, sd=10)
#c. probability of less than 68
pnorm(68, mean=80, sd=10)
#d. cutoff that seqarates the bottom 30%
qnorm(0.3, mean=80, sd=10)
#e. 80th percentile
qnorm(0.8, mean=80, sd=10)
#f. cutoffs that cantatin the middle 60%
qnorm(0.8, mean=80, sd=10)
qnorm(0.2, mean=80, sd=10)
#To verify that it is correct
80-qnorm(0.8, mean=80, sd=10)
80-qnorm(0.2, mean=80, sd=10)
|
/15. pnorm, qnorm, 정규분포, normal distribution.R
|
no_license
|
hallymer/R_Programming_Season1
|
R
| false | false | 917 |
r
|
#normal distribution mean=80, sd=10
x <- seq(40, 120, length=300)
x
y <- dnorm(x, mean=80, sd=10)
y
plot(x, y)
plot(x, y, type="l")
plot(x, y, type="l", col="red")
#lines(x, dnorm(x, mean=80, sd=25), col="blue")
#a. probability between 65~75
x2 <- seq(65, 75, length=200)
y2 <- dnorm(x2, mean=80, sd=10)
polygon(c(65,x2,75), c(0, y2, 0), col="gray")
pnorm(75, mean=80, sd=10)-pnorm(65, mean=80, sd=10)
#pnorm
#qnorm
#dnorm
#rnorm
#b. probability of over 92
#2 Ways
#First
pnorm(92, mean=80, sd=10, lower.tail=FALSE)
#Second
1-pnorm(92, mean=80, sd=10)
#c. probability of less than 68
pnorm(68, mean=80, sd=10)
#d. cutoff that seqarates the bottom 30%
qnorm(0.3, mean=80, sd=10)
#e. 80th percentile
qnorm(0.8, mean=80, sd=10)
#f. cutoffs that cantatin the middle 60%
qnorm(0.8, mean=80, sd=10)
qnorm(0.2, mean=80, sd=10)
#To verify that it is correct
80-qnorm(0.8, mean=80, sd=10)
80-qnorm(0.2, mean=80, sd=10)
|
###########################
####### DESCRIPTION #######
###########################
# Description goes here.
rm (list = ls())
suppressPackageStartupMessages(library("plyr"))
suppressPackageStartupMessages(library("doParallel"))
source("/home/antonio/Desktop/BigData/codes/functions.R") # load the functions
# Specify working directory: (The wd MUST contain 2 subfolders: "Downalod" and "Clean")
directory = "/home/antonio/Desktop/BigData/data"
setwd(paste0(directory,"/Clean"))
# Getting filenames
filename <- list.files(getwd(), pattern = ".*txt$")
#filename = filename[1:50] # only for test!
# Aggregate Data
nodes <- detectCores()
cl <- makePSOCKcluster(nodes)
registerDoParallel(cl)
a_ply(.data = filename,
.fun = aggregate_data,
.parallel = TRUE,
.margins = 1)
stopCluster(cl)
|
/06_aggregate_cleaned_data.R
|
no_license
|
ciulloA/BigData
|
R
| false | false | 807 |
r
|
###########################
####### DESCRIPTION #######
###########################
# Description goes here.
rm (list = ls())
suppressPackageStartupMessages(library("plyr"))
suppressPackageStartupMessages(library("doParallel"))
source("/home/antonio/Desktop/BigData/codes/functions.R") # load the functions
# Specify working directory: (The wd MUST contain 2 subfolders: "Downalod" and "Clean")
directory = "/home/antonio/Desktop/BigData/data"
setwd(paste0(directory,"/Clean"))
# Getting filenames
filename <- list.files(getwd(), pattern = ".*txt$")
#filename = filename[1:50] # only for test!
# Aggregate Data
nodes <- detectCores()
cl <- makePSOCKcluster(nodes)
registerDoParallel(cl)
a_ply(.data = filename,
.fun = aggregate_data,
.parallel = TRUE,
.margins = 1)
stopCluster(cl)
|
#options <- NULL
#options$DT <- 11
#options$percentile <- 5
#options$showPlots <- 0 # for show plots
#options$showPlots <- 1 # not show plots
#load("c:/temp/4Heston.Rdata")
# y <- xxx
HestonModel <- function(y,options) {
# define parameters
lastError <- 1
fSigma <- 0
nK <- 100
# find theta
varX <- array(NA, dim = options$DT)
for (i in 1:options$DT) { varX[i] <- sum((diff(y, lag = i))^2)/(length(y) - 1- i) }
lastError <- 1
fSigma <- 0
while(lastError > fSigma) {
options$DT <- options$DT -1
theta <- sum(varX[1:options$DT]*c(1:options$DT))/(options$DT*(options$DT+1)*(2*options$DT+1)/6)
lastError <- abs(varX[options$DT] - theta*options$DT)
fSigma <- 1.5*sd(varX[1:options$DT] - theta*c(1:options$DT))
}
if (options$showPlots == 0) {
windows()
plot(c(1:options$DT),varX[1:options$DT])
lines( c(1:options$DT), theta*c(1:options$DT), lwd=2, col="blue")
grid()
}
ecf <- array(0, dim = c(options$DT,nK))
k <- array(0, dim = c(options$DT,nK))
nKth <- array(0, dim = options$DT)
for (i in 1:options$DT) {
xxx <- ECF(diff(y, lag = i),nK)
ecf[i,] <- xxx[[1]]
k[i,] <- xxx[[2]]
nKth[i] <- xxx[[3]]
}
xxx <- HestonFitK(ecf, k, nKth, c(1:options$DT), theta, options)
a <- xxx[[1]]
g <- xxx[[2]]
x <- array(NA, dim = c(options$DT,101))
PDFX <- array(NA, dim = c(options$DT,101))
xAv <- array(NA, dim = c(options$DT,50))
CPDF <- array(NA, dim = c(options$DT,50))
prct <- array(NA, dim = options$DT)
for (i in 1:options$DT) {
xxx <- HestonX(a,g,theta,k[i,nKth[i]],i)
x[i,] <-xxx[[1]]
PDFX[i,] <-xxx[[2]]
xxx <- percentile(x[i,],PDFX[i,],options)
xAv[i,] <- xxx[[1]]
CPDF[i,] <- xxx[[2]]
prct[i] <- xxx[[3]]
}
if (options$showPlots == 0) {
cols <- rainbow(options$DT)
Xrange <- c(-max(abs(diff(y, lag = options$DT))), max(abs(diff(y, lag = options$DT))))
Yrange <- c(0,1)
windows()
plot(Xrange,Yrange, type = "n", xlab = "Delta Value (M$)", ylab = "CPDF",
main = bquote(paste("Heston Fit CPDF: ", gamma == .(format(g, digits = 3)), ", ",
alpha == .(format(a, digits = 3)), ", ", theta == .(format(theta, digits = 3)))))
for(i in 1:options$DT) {
ySort <- sort(diff(y, lag = i))
quantiles <- quantile(ySort, probs = c(0.01*options$percentile, 1-0.01*options$percentile))
CP <- (c(1:length(ySort))-0.5)/length(ySort)
points(ySort,CP, lwd = 2, col = cols[i])
lines(-xAv[i,],CPDF[i,], lwd = 2, col = cols[i])
lines(xAv[i,],(1-CPDF[i,]), lwd = 2, col = cols[i])
abline(v = - prct[i], lwd = 1, col = cols[i])
abline(v = prct[i], pch = 22, lwd = 1, col = cols[i])
abline(v = quantiles[1], pch = 22, lwd = 1, col = cols[i])
abline(v = quantiles[2], pch = 22, lwd = 1, col = cols[i])
}
grid()
windows()
Yrange <- c(1e-3,max(PDFX))
plot(Xrange,Yrange, type = "n", log = "y", xlab = "Delta Value (M$)", ylab = "PDF",
main = bquote(paste("Heston Fit PDF: ", gamma == .(format(g, digits = 3)), ", ",
alpha == .(format(a, digits = 3)), ", ", theta == .(format(theta, digits = 3)))))
for(i in 1: options$DT) {
lines(x[i,],PDFX[i,], lwd = 3, col = cols[i])
lines(-1*x[i,],PDFX[i,], lwd = 3, col = cols[i])
abline(v = prct[i], lwd = 3, col = cols[i])
abline(v = -prct[i], lwd = 3, col = cols[i])
}
grid()
}
return(list(a,g,x,PDFX,prct))
}
percentile <- function(x,PDFX,options) {
# browser()
CPDF <- array(0, dim = ((length(x)-1)/2))
Xav <- array(0, dim = ((length(x)-1)/2))
m <- 0
for (i in ((length(x)-1)/2):1){
m <- m + 1
X <- c(x[2*i+1],x[2*i],x[2*i-1])
Y <- c(PDFX[2*i+1],PDFX[2*i],PDFX[2*i-1])
xxx <- parabolicCoeff(X,Y)
dInt <- (X[1]-X[3])*((xxx[[1]]/3)*(X[3]*X[3] + X[3]*X[1]+ X[1]*X[1]) + (xxx[[2]]/2)*(X[3]+X[1]) + xxx[[3]])
Xav[m] <- X[3]
if( m == 1){ CPDF[m] <- dInt }
if (m > 1) {
CPDF[m] <- CPDF[m-1] +dInt
if(CPDF[m] > 0.01*options$percentile & CPDF[m-1] < 0.01*options$percentile)
xPrct <- (X[3]+(X[1]-X[3])*(CPDF[m] - 0.01*options$percentile)/dInt)
}
}
return(list(Xav,CPDF,xPrct))
}
parabolicCoeff <- function(x,y) {
R21 <- (y[2]-y[1])/(x[2]-x[1])
R32 <- (y[3]-y[2])/(x[3]-x[2])
A <- (R21-R32)/(x[1]-x[3])
B <- R21-A*(x[2]+x[1])
C <- y[1]-A*x[1]*x[1]-B*x[1]
return(list(A,B,C))
}
# Heston Function in x-space
HestonX <- function(a,g,theta,kMax,DT) {
while (PDFkHeston(a,g,theta,kMax,DT) > 1e-5) # find kMax
kMax <- kMax + 10
dx <- 0.2/kMax
xMax <- 100/kMax
dK <- 0.1/xMax
nK <- 2*round(round(kMax/dK)/2)
nKp1 <- nK + 1
k <- dK*c(0:nK)
PDFK <- array(0, dim = nKp1)
IntFun <- array(0, dim = nKp1)
for(i in 1:nKp1)
PDFK[i] <- PDFkHeston(a,g,theta,k[i],DT)
nx <- 100
dx <- xMax/nx
# find the x limit
for (j in 1:nKp1)
IntFun[j] <- cos(k[j]*xMax)*PDFK[j]
PDFxMax <- 1/pi*Simpson(IntFun,nKp1,dK)
while(PDFxMax < 2e-5){
xMax <- xMax - dx
for (j in 1:nKp1)
IntFun[j] <- cos(k[j]*xMax)*PDFK[j]
PDFxMax <- 1/pi*Simpson(IntFun,nKp1,dK)
}
while(PDFxMax > 2e-5){
xMax <- xMax + dx
for (j in 1:nKp1)
IntFun[j] <- cos(k[j]*xMax)*PDFK[j]
PDFxMax <- 1/pi*Simpson(IntFun,nKp1,dK)
}
dx <- xMax/nx
x <- dx*c(0:nx)
PDFX <- array(0, dim = (nx +1))
for( i in 1:(nx+1)) {
for (j in 1:nKp1)
IntFun[j] <- cos(k[j]*x[i])*PDFK[j]
PDFX[i] <- 1/pi*Simpson(IntFun,nKp1,dK);
}
return(list(x,PDFX))
}
Simpson <- function(intFunction,N,dx) {
indEven <- 2*c(1:((N-1)/2))
indOdd <- 1 + 2*c(1:((N-2)/2))
intRes <- (dx/3)*(intFunction[1]+intFunction[N] + 4*sum(intFunction[indEven]) + 2*sum(intFunction[indOdd]))
return(intRes)
}
# Heston Fit
HestonFitK <- function(ecf,k,nKth,DT,theta,options){
# browser()
a <- 1
nFactor <- 5
gX <- 1/sqrt(min(DT)*max(DT))
chiX <- cfVSecf(ecf,k,nKth,DT,a,gX,theta,options$showPlots)
while (nFactor > 0) {
gMin <- gX/nFactor
gMax <- gX*nFactor
chiMin <- cfVSecf(ecf,k,nKth,DT,a,gMin,theta,1)
chiMax <- cfVSecf(ecf,k,nKth,DT,a,gMax,theta,1)
nIt <- 0
if(chiMin > chiX & chiMax > chiX ) {
nFactor <- 0
while(nIt < 20){
nIt <- nIt + 1
gXX <- (gMin + gMax)/2
chiXX <- cfVSecf(ecf,k,nKth,DT,a,gXX,theta,1)
if(gXX > gX) {
if(chiXX < chiX) {
gMin <- gX
chiMin <- chiX
gX <- gXX
chiX <- chiXX
} else {
gMax <- gXX
chiMax <- chiXX
}
} else {
if(chiXX < chiX) {
gMax <- gX
chiMax <- chiX
gX <- gXX
chiX <- chiXX
} else {
gMin <- gXX
chiMin <- chiXX
}
}
if( gMax - gMin < 0.002 | nIt == 20){
gX <- (gMax+gMin)/2
chiX <- cfVSecf(ecf,k,nKth,DT,a,gX,theta,options$showPlots)
break;
}
}
} else {
if(chiMin < chiMax) {
gMax <- gX
chiMax <- gMax
gX <- gMin
chiX <- chiMin
gMin <- gMin/nFactor
chiMin <- cfVSecf(ecf,k,nKth,DT,a,gMin,theta,1)
} else {
gMin <- gX
chiMin <- chiX
gX <- gMax
chiX <- chiMax
gMax <- gMax*nFactor
chiMax <- cfVSecf(ecf,k,nKth,DT,a,gMax,theta,1)
}
}
}
return(list(a,gX,chiX))
}
# Characteristic vs Emprical Characteristic Functiotions
cfVSecf <- function(ecf,k,nKth,DT,a,g,theta,nFlagFig) {
chi <- 0
cf <- array(NA, dim = c(length(DT),max(nKth)))
# browser()
for (iDT in 1:length(DT)) {
for (iK in 1:nKth[iDT]) {
cf[iDT,iK] <- PDFkHeston(a,g,theta,k[iDT,iK],DT[iDT])
dCF <- cf[iDT,iK] - ecf[iDT,iK]
chi <- chi + dCF*dCF
}
}
if(nFlagFig == 0) {
windows()
plot(k[1,],ecf[1,], type = "n",
main = bquote(paste("CF vs ECF,", chi == .(format(chi, digits = 3)), ", ",
gamma == .(format(g, digits = 3)), ", ", alpha == .(format(a, digits = 3)))),
xlab = "k", ylab = "CF")
for (iDT in 1:length(DT)){
points(k[iDT,1:nKth[iDT]], ecf[iDT,1:nKth[iDT]], lwd = 3, col = "red")
lines(k[iDT,1:nKth[iDT]], cf[iDT,1:nKth[iDT]], lwd = 3, col = "blue")
}
grid()
}
return(chi)
}
PDFkHeston <- function(a,g,theta,k,DT) {
if (k == 0) {
Ftk <- 0
} else {
gt <- g*DT
omega <- sqrt(1 + 2*k*k*theta/(g*a))
omegaGammaTo2 <- omega*gt/2
if (omegaGammaTo2 > 10) {
Ftk <- gt/2*(1-omega) - log((omega*omega+2*omega+1)/(4*omega))
} else {
Ftk <- gt/2 - log(cosh(omegaGammaTo2) + (omega*omega + 1)/(2*omega)*sinh(omegaGammaTo2))
}
}
return(exp(a*Ftk))
}
ECF <- function(dx,nK) {
ecf <- array(0, dim = nK)
k <- array(0, dim = nK)
dxMax <- max(abs(dx))
nIt <- 1
while(nIt < 10) {
kMax <- 20*nIt/dxMax
dK <- kMax/(nK-1)
k = dK * c(0:(nK-1))
ecf[1] <- length(dx)
for (ik in (2:nK) ) {
for (idx in 1:length(dx))
ecf[ik] <- ecf[ik] + cos(k[ik]*dx[idx])
}
ecf <- ecf/ecf[1]
if( ecf[nK] < 0.1)
break
nIt <- nIt + 1
}
nKth <- nK
for (iK in 2:nK ) {
if ( ecf[iK] < 0.05 ){
nKth <- iK-1
break
}
}
return(list(ecf,k,nKth))
}
|
/R Extension/RMG/Finance/Heston/HestonModel.R
|
no_license
|
uhasan1/QLExtension-backup
|
R
| false | false | 9,299 |
r
|
#options <- NULL
#options$DT <- 11
#options$percentile <- 5
#options$showPlots <- 0 # for show plots
#options$showPlots <- 1 # not show plots
#load("c:/temp/4Heston.Rdata")
# y <- xxx
HestonModel <- function(y,options) {
# define parameters
lastError <- 1
fSigma <- 0
nK <- 100
# find theta
varX <- array(NA, dim = options$DT)
for (i in 1:options$DT) { varX[i] <- sum((diff(y, lag = i))^2)/(length(y) - 1- i) }
lastError <- 1
fSigma <- 0
while(lastError > fSigma) {
options$DT <- options$DT -1
theta <- sum(varX[1:options$DT]*c(1:options$DT))/(options$DT*(options$DT+1)*(2*options$DT+1)/6)
lastError <- abs(varX[options$DT] - theta*options$DT)
fSigma <- 1.5*sd(varX[1:options$DT] - theta*c(1:options$DT))
}
if (options$showPlots == 0) {
windows()
plot(c(1:options$DT),varX[1:options$DT])
lines( c(1:options$DT), theta*c(1:options$DT), lwd=2, col="blue")
grid()
}
ecf <- array(0, dim = c(options$DT,nK))
k <- array(0, dim = c(options$DT,nK))
nKth <- array(0, dim = options$DT)
for (i in 1:options$DT) {
xxx <- ECF(diff(y, lag = i),nK)
ecf[i,] <- xxx[[1]]
k[i,] <- xxx[[2]]
nKth[i] <- xxx[[3]]
}
xxx <- HestonFitK(ecf, k, nKth, c(1:options$DT), theta, options)
a <- xxx[[1]]
g <- xxx[[2]]
x <- array(NA, dim = c(options$DT,101))
PDFX <- array(NA, dim = c(options$DT,101))
xAv <- array(NA, dim = c(options$DT,50))
CPDF <- array(NA, dim = c(options$DT,50))
prct <- array(NA, dim = options$DT)
for (i in 1:options$DT) {
xxx <- HestonX(a,g,theta,k[i,nKth[i]],i)
x[i,] <-xxx[[1]]
PDFX[i,] <-xxx[[2]]
xxx <- percentile(x[i,],PDFX[i,],options)
xAv[i,] <- xxx[[1]]
CPDF[i,] <- xxx[[2]]
prct[i] <- xxx[[3]]
}
if (options$showPlots == 0) {
cols <- rainbow(options$DT)
Xrange <- c(-max(abs(diff(y, lag = options$DT))), max(abs(diff(y, lag = options$DT))))
Yrange <- c(0,1)
windows()
plot(Xrange,Yrange, type = "n", xlab = "Delta Value (M$)", ylab = "CPDF",
main = bquote(paste("Heston Fit CPDF: ", gamma == .(format(g, digits = 3)), ", ",
alpha == .(format(a, digits = 3)), ", ", theta == .(format(theta, digits = 3)))))
for(i in 1:options$DT) {
ySort <- sort(diff(y, lag = i))
quantiles <- quantile(ySort, probs = c(0.01*options$percentile, 1-0.01*options$percentile))
CP <- (c(1:length(ySort))-0.5)/length(ySort)
points(ySort,CP, lwd = 2, col = cols[i])
lines(-xAv[i,],CPDF[i,], lwd = 2, col = cols[i])
lines(xAv[i,],(1-CPDF[i,]), lwd = 2, col = cols[i])
abline(v = - prct[i], lwd = 1, col = cols[i])
abline(v = prct[i], pch = 22, lwd = 1, col = cols[i])
abline(v = quantiles[1], pch = 22, lwd = 1, col = cols[i])
abline(v = quantiles[2], pch = 22, lwd = 1, col = cols[i])
}
grid()
windows()
Yrange <- c(1e-3,max(PDFX))
plot(Xrange,Yrange, type = "n", log = "y", xlab = "Delta Value (M$)", ylab = "PDF",
main = bquote(paste("Heston Fit PDF: ", gamma == .(format(g, digits = 3)), ", ",
alpha == .(format(a, digits = 3)), ", ", theta == .(format(theta, digits = 3)))))
for(i in 1: options$DT) {
lines(x[i,],PDFX[i,], lwd = 3, col = cols[i])
lines(-1*x[i,],PDFX[i,], lwd = 3, col = cols[i])
abline(v = prct[i], lwd = 3, col = cols[i])
abline(v = -prct[i], lwd = 3, col = cols[i])
}
grid()
}
return(list(a,g,x,PDFX,prct))
}
percentile <- function(x,PDFX,options) {
# browser()
CPDF <- array(0, dim = ((length(x)-1)/2))
Xav <- array(0, dim = ((length(x)-1)/2))
m <- 0
for (i in ((length(x)-1)/2):1){
m <- m + 1
X <- c(x[2*i+1],x[2*i],x[2*i-1])
Y <- c(PDFX[2*i+1],PDFX[2*i],PDFX[2*i-1])
xxx <- parabolicCoeff(X,Y)
dInt <- (X[1]-X[3])*((xxx[[1]]/3)*(X[3]*X[3] + X[3]*X[1]+ X[1]*X[1]) + (xxx[[2]]/2)*(X[3]+X[1]) + xxx[[3]])
Xav[m] <- X[3]
if( m == 1){ CPDF[m] <- dInt }
if (m > 1) {
CPDF[m] <- CPDF[m-1] +dInt
if(CPDF[m] > 0.01*options$percentile & CPDF[m-1] < 0.01*options$percentile)
xPrct <- (X[3]+(X[1]-X[3])*(CPDF[m] - 0.01*options$percentile)/dInt)
}
}
return(list(Xav,CPDF,xPrct))
}
parabolicCoeff <- function(x,y) {
R21 <- (y[2]-y[1])/(x[2]-x[1])
R32 <- (y[3]-y[2])/(x[3]-x[2])
A <- (R21-R32)/(x[1]-x[3])
B <- R21-A*(x[2]+x[1])
C <- y[1]-A*x[1]*x[1]-B*x[1]
return(list(A,B,C))
}
# Heston Function in x-space
HestonX <- function(a,g,theta,kMax,DT) {
while (PDFkHeston(a,g,theta,kMax,DT) > 1e-5) # find kMax
kMax <- kMax + 10
dx <- 0.2/kMax
xMax <- 100/kMax
dK <- 0.1/xMax
nK <- 2*round(round(kMax/dK)/2)
nKp1 <- nK + 1
k <- dK*c(0:nK)
PDFK <- array(0, dim = nKp1)
IntFun <- array(0, dim = nKp1)
for(i in 1:nKp1)
PDFK[i] <- PDFkHeston(a,g,theta,k[i],DT)
nx <- 100
dx <- xMax/nx
# find the x limit
for (j in 1:nKp1)
IntFun[j] <- cos(k[j]*xMax)*PDFK[j]
PDFxMax <- 1/pi*Simpson(IntFun,nKp1,dK)
while(PDFxMax < 2e-5){
xMax <- xMax - dx
for (j in 1:nKp1)
IntFun[j] <- cos(k[j]*xMax)*PDFK[j]
PDFxMax <- 1/pi*Simpson(IntFun,nKp1,dK)
}
while(PDFxMax > 2e-5){
xMax <- xMax + dx
for (j in 1:nKp1)
IntFun[j] <- cos(k[j]*xMax)*PDFK[j]
PDFxMax <- 1/pi*Simpson(IntFun,nKp1,dK)
}
dx <- xMax/nx
x <- dx*c(0:nx)
PDFX <- array(0, dim = (nx +1))
for( i in 1:(nx+1)) {
for (j in 1:nKp1)
IntFun[j] <- cos(k[j]*x[i])*PDFK[j]
PDFX[i] <- 1/pi*Simpson(IntFun,nKp1,dK);
}
return(list(x,PDFX))
}
Simpson <- function(intFunction,N,dx) {
indEven <- 2*c(1:((N-1)/2))
indOdd <- 1 + 2*c(1:((N-2)/2))
intRes <- (dx/3)*(intFunction[1]+intFunction[N] + 4*sum(intFunction[indEven]) + 2*sum(intFunction[indOdd]))
return(intRes)
}
# Heston Fit
HestonFitK <- function(ecf,k,nKth,DT,theta,options){
# browser()
a <- 1
nFactor <- 5
gX <- 1/sqrt(min(DT)*max(DT))
chiX <- cfVSecf(ecf,k,nKth,DT,a,gX,theta,options$showPlots)
while (nFactor > 0) {
gMin <- gX/nFactor
gMax <- gX*nFactor
chiMin <- cfVSecf(ecf,k,nKth,DT,a,gMin,theta,1)
chiMax <- cfVSecf(ecf,k,nKth,DT,a,gMax,theta,1)
nIt <- 0
if(chiMin > chiX & chiMax > chiX ) {
nFactor <- 0
while(nIt < 20){
nIt <- nIt + 1
gXX <- (gMin + gMax)/2
chiXX <- cfVSecf(ecf,k,nKth,DT,a,gXX,theta,1)
if(gXX > gX) {
if(chiXX < chiX) {
gMin <- gX
chiMin <- chiX
gX <- gXX
chiX <- chiXX
} else {
gMax <- gXX
chiMax <- chiXX
}
} else {
if(chiXX < chiX) {
gMax <- gX
chiMax <- chiX
gX <- gXX
chiX <- chiXX
} else {
gMin <- gXX
chiMin <- chiXX
}
}
if( gMax - gMin < 0.002 | nIt == 20){
gX <- (gMax+gMin)/2
chiX <- cfVSecf(ecf,k,nKth,DT,a,gX,theta,options$showPlots)
break;
}
}
} else {
if(chiMin < chiMax) {
gMax <- gX
chiMax <- gMax
gX <- gMin
chiX <- chiMin
gMin <- gMin/nFactor
chiMin <- cfVSecf(ecf,k,nKth,DT,a,gMin,theta,1)
} else {
gMin <- gX
chiMin <- chiX
gX <- gMax
chiX <- chiMax
gMax <- gMax*nFactor
chiMax <- cfVSecf(ecf,k,nKth,DT,a,gMax,theta,1)
}
}
}
return(list(a,gX,chiX))
}
# Characteristic vs Emprical Characteristic Functiotions
cfVSecf <- function(ecf,k,nKth,DT,a,g,theta,nFlagFig) {
chi <- 0
cf <- array(NA, dim = c(length(DT),max(nKth)))
# browser()
for (iDT in 1:length(DT)) {
for (iK in 1:nKth[iDT]) {
cf[iDT,iK] <- PDFkHeston(a,g,theta,k[iDT,iK],DT[iDT])
dCF <- cf[iDT,iK] - ecf[iDT,iK]
chi <- chi + dCF*dCF
}
}
if(nFlagFig == 0) {
windows()
plot(k[1,],ecf[1,], type = "n",
main = bquote(paste("CF vs ECF,", chi == .(format(chi, digits = 3)), ", ",
gamma == .(format(g, digits = 3)), ", ", alpha == .(format(a, digits = 3)))),
xlab = "k", ylab = "CF")
for (iDT in 1:length(DT)){
points(k[iDT,1:nKth[iDT]], ecf[iDT,1:nKth[iDT]], lwd = 3, col = "red")
lines(k[iDT,1:nKth[iDT]], cf[iDT,1:nKth[iDT]], lwd = 3, col = "blue")
}
grid()
}
return(chi)
}
PDFkHeston <- function(a,g,theta,k,DT) {
if (k == 0) {
Ftk <- 0
} else {
gt <- g*DT
omega <- sqrt(1 + 2*k*k*theta/(g*a))
omegaGammaTo2 <- omega*gt/2
if (omegaGammaTo2 > 10) {
Ftk <- gt/2*(1-omega) - log((omega*omega+2*omega+1)/(4*omega))
} else {
Ftk <- gt/2 - log(cosh(omegaGammaTo2) + (omega*omega + 1)/(2*omega)*sinh(omegaGammaTo2))
}
}
return(exp(a*Ftk))
}
ECF <- function(dx,nK) {
ecf <- array(0, dim = nK)
k <- array(0, dim = nK)
dxMax <- max(abs(dx))
nIt <- 1
while(nIt < 10) {
kMax <- 20*nIt/dxMax
dK <- kMax/(nK-1)
k = dK * c(0:(nK-1))
ecf[1] <- length(dx)
for (ik in (2:nK) ) {
for (idx in 1:length(dx))
ecf[ik] <- ecf[ik] + cos(k[ik]*dx[idx])
}
ecf <- ecf/ecf[1]
if( ecf[nK] < 0.1)
break
nIt <- nIt + 1
}
nKth <- nK
for (iK in 2:nK ) {
if ( ecf[iK] < 0.05 ){
nKth <- iK-1
break
}
}
return(list(ecf,k,nKth))
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mcmcChain.R
\name{.infer_param_dims}
\alias{.infer_param_dims}
\title{Infer original dimensions of parameter (per iteration) from colnames}
\usage{
.infer_param_dims(cnames)
}
\arguments{
\item{cnames}{List of column names}
}
\value{
Numeric vector (nrow, ncol)
}
\description{
Used to avoid writing colnames directly to HDF5 as attribute, which fails
for large parameters (e.g. Y)
}
\keyword{internal}
|
/man/dot-infer_param_dims.Rd
|
permissive
|
JunqiangWang/BayesSpace
|
R
| false | true | 481 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mcmcChain.R
\name{.infer_param_dims}
\alias{.infer_param_dims}
\title{Infer original dimensions of parameter (per iteration) from colnames}
\usage{
.infer_param_dims(cnames)
}
\arguments{
\item{cnames}{List of column names}
}
\value{
Numeric vector (nrow, ncol)
}
\description{
Used to avoid writing colnames directly to HDF5 as attribute, which fails
for large parameters (e.g. Y)
}
\keyword{internal}
|
c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 40821
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 30289
c
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 30289
c
c Input Parameter (command line, file):
c input filename QBFLIB/Kronegger-Pfandler-Pichler/dungeon/dungeon_i10-m10-u10-v0.pddl_planlen=70.qdimacs
c output filename /tmp/dcnfAutarky.dimacs
c autarky level 1
c conformity level 0
c encoding type 2
c no.of var 14947
c no.of clauses 40821
c no.of taut cls 0
c
c Output Parameters:
c remaining no.of clauses 30289
c
c QBFLIB/Kronegger-Pfandler-Pichler/dungeon/dungeon_i10-m10-u10-v0.pddl_planlen=70.qdimacs 14947 40821 E1 [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 45 46 47 48 49 50 51 52 53 55 56 57 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 119 120 121 122 123 124 125 126 127 128 129 130 131 132 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 172 173 174 175 176 177 178 179 224 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 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696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 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/code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Experiments/Kronegger-Pfandler-Pichler/dungeon/dungeon_i10-m10-u10-v0.pddl_planlen=70/dungeon_i10-m10-u10-v0.pddl_planlen=70.R
|
no_license
|
arey0pushpa/dcnf-autarky
|
R
| false | false | 56,114 |
r
|
c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 40821
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 30289
c
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 30289
c
c Input Parameter (command line, file):
c input filename QBFLIB/Kronegger-Pfandler-Pichler/dungeon/dungeon_i10-m10-u10-v0.pddl_planlen=70.qdimacs
c output filename /tmp/dcnfAutarky.dimacs
c autarky level 1
c conformity level 0
c encoding type 2
c no.of var 14947
c no.of clauses 40821
c no.of taut cls 0
c
c Output Parameters:
c remaining no.of clauses 30289
c
c QBFLIB/Kronegger-Pfandler-Pichler/dungeon/dungeon_i10-m10-u10-v0.pddl_planlen=70.qdimacs 14947 40821 E1 [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 38 39 40 41 42 44 45 46 47 48 49 50 51 52 53 55 56 57 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 119 120 121 122 123 124 125 126 127 128 129 130 131 132 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 172 173 174 175 176 177 178 179 224 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 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|
\name{diffFiles}
\alias{diffFiles}
\title{
Names of Files Showing Differences
}
\description{
List all files that show differences between control and test output
(as red pixels).
}
\usage{
diffFiles(x)
}
\arguments{
\item{x}{
A \code{"gdiffComparison"} object, as created by
\code{\link{gdiff}} or \code{\link{gdiffCompare}}.
}
}
\value{
A character vector of file names (with paths).
}
\author{
Paul Murrell
}
\seealso{
\code{\link{gdiff}} and
\code{\link{gdiffCompare}}.
}
\examples{
f1 <- function() plot(1)
f2 <- function() plot(2)
\donttest{
result <- gdiff(list(control=f1, test=f2), name="f",
controlDir=file.path(tempdir(), "Control"),
testDir=file.path(tempdir(), "Test"),
compareDir=file.path(tempdir(), "Compare"))
result
diffFiles(result)
}
}
\keyword{ dplot }
\concept{ visual testing }
|
/man/diffFiles.Rd
|
no_license
|
pmur002/gdiff
|
R
| false | false | 877 |
rd
|
\name{diffFiles}
\alias{diffFiles}
\title{
Names of Files Showing Differences
}
\description{
List all files that show differences between control and test output
(as red pixels).
}
\usage{
diffFiles(x)
}
\arguments{
\item{x}{
A \code{"gdiffComparison"} object, as created by
\code{\link{gdiff}} or \code{\link{gdiffCompare}}.
}
}
\value{
A character vector of file names (with paths).
}
\author{
Paul Murrell
}
\seealso{
\code{\link{gdiff}} and
\code{\link{gdiffCompare}}.
}
\examples{
f1 <- function() plot(1)
f2 <- function() plot(2)
\donttest{
result <- gdiff(list(control=f1, test=f2), name="f",
controlDir=file.path(tempdir(), "Control"),
testDir=file.path(tempdir(), "Test"),
compareDir=file.path(tempdir(), "Compare"))
result
diffFiles(result)
}
}
\keyword{ dplot }
\concept{ visual testing }
|
6*5
d<-"5"
d
a<-4
a
a*2
d*2
d*2
10%%3
x <- 50
x
x <- c(50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80)
x
x <- c(150,200,210)
x
dbl_var <- c(1, 2.5, 4.5)
I will be there at 2 pm.
dbl_var <- c(1, 2.5, 4.5, "xyz")
rep(1,10)
"abc" "st+-cydtsyndsklhnl"
pi
rep(3,3)
seq(2,6, by=3)
x <- c(1,2,3,4,5)
y<- c(5,6,7,8,9)
x*y
sqrt(y)
y[5]
a=5
b=2
a%%b
x%%y
y%%x
a%%5
y[-5]
y<3
y[y<9]=2
y
Df_new<-data.frame(height1=c(150,160,149,246,235),weight=c(65,72,79,90,100))
df <- data.frame(x = 1:3, y = c("a","b", "c"))
abc <- data.frame(x = 1:3, y = c("a", "b", "c"))
df[c(2,3),2]
Df_new[c(3,5),c(1,2)]
Df_new[3,2]
Df_new[4,]
Df_new[4,1:2]
Df_new[4,c(1,3)]
Df_new[c(1,3),'height1']
df[c(1,3),c("x","y")]
Df_new$height1
|
/myfirst_project.R
|
no_license
|
jaspal52119/my_first_datascience_project
|
R
| false | false | 932 |
r
|
6*5
d<-"5"
d
a<-4
a
a*2
d*2
d*2
10%%3
x <- 50
x
x <- c(50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80,50,60,80)
x
x <- c(150,200,210)
x
dbl_var <- c(1, 2.5, 4.5)
I will be there at 2 pm.
dbl_var <- c(1, 2.5, 4.5, "xyz")
rep(1,10)
"abc" "st+-cydtsyndsklhnl"
pi
rep(3,3)
seq(2,6, by=3)
x <- c(1,2,3,4,5)
y<- c(5,6,7,8,9)
x*y
sqrt(y)
y[5]
a=5
b=2
a%%b
x%%y
y%%x
a%%5
y[-5]
y<3
y[y<9]=2
y
Df_new<-data.frame(height1=c(150,160,149,246,235),weight=c(65,72,79,90,100))
df <- data.frame(x = 1:3, y = c("a","b", "c"))
abc <- data.frame(x = 1:3, y = c("a", "b", "c"))
df[c(2,3),2]
Df_new[c(3,5),c(1,2)]
Df_new[3,2]
Df_new[4,]
Df_new[4,1:2]
Df_new[4,c(1,3)]
Df_new[c(1,3),'height1']
df[c(1,3),c("x","y")]
Df_new$height1
|
library(data.table)
library(sparkline)
# ====準備部分====
source(file = "01_Settings/Path.R", local = T, encoding = "UTF-8")
# 感染者ソーステーブルを取得
byDate <- fread(paste0(DATA_PATH, "byDate.csv"), header = T)
byDate[is.na(byDate)] <- 0
byDate$date <- lapply(byDate[, 1], function(x) {
as.Date(as.character(x), format = "%Y%m%d")
})
# 死亡データ
death <- fread(paste0(DATA_PATH, "death.csv"))
death[is.na(death)] <- 0
# 文言データを取得
lang <- fread(paste0(DATA_PATH, "lang.csv"))
langCode <- "ja"
# 都道府県
provinceCode <- fread(paste0(DATA_PATH, "prefectures.csv"))
provinceSelector <- provinceCode$id
names(provinceSelector) <- provinceCode$`name-ja`
provinceAttr <- fread(paste0(DATA_PATH, "Signate/prefMaster.csv"))
provinceAttr[, 都道府県略称 := 都道府県]
provinceAttr[, 都道府県略称 := gsub("県", "", 都道府県略称)]
provinceAttr[, 都道府県略称 := gsub("府", "", 都道府県略称)]
provinceAttr[, 都道府県略称 := gsub("東京都", "東京", 都道府県略称)]
# 色設定
lightRed <- "#F56954"
middleRed <- "#DD4B39"
darkRed <- "#B03C2D"
lightYellow <- "#F8BF76"
middleYellow <- "#F39C11"
darkYellow <- "#DB8B0A"
lightGreen <- "#00A65A"
middleGreen <- "#01A65A"
darkGreen <- "#088448"
superDarkGreen <- "#046938"
lightNavy <- "#5A6E82"
middelNavy <- "#001F3F"
darkNavy <- "#001934"
lightGrey <- "#F5F5F5"
lightBlue <- "#7BD6F5"
middleBlue <- "#00C0EF"
darkBlue <- "#00A7D0"
# ====各都道府県のサマリーテーブル====
# ランキングカラムを作成
# cumDt <- cumsum(byDate[, c(2:48, 50)])
# rankDt <- data.table(t(apply(-cumDt, 1, function(x){rank(x, ties.method = 'min')})))
# rankDt[, colnames(rankDt) := shift(.SD, fill = 0) - .SD, .SDcols = colnames(rankDt)]
#
# rankDt[rankDt == 0] <- '-'
# rankDt[, colnames(rankDt) := ifelse(.SD > 0, paste0('+', .SD), .SD), .SDcols = colnames(rankDt)]
print("新規なし継続日数カラム作成")
zeroContinuousDay <- stack(lapply(byDate[, 2:ncol(byDate)], function(region) {
continuousDay <- 0
for (x in region) {
if (x == 0) {
continuousDay <- continuousDay + 1
} else {
continuousDay <- 0
}
}
return(continuousDay - 1)
}))
print("感染確認カラム作成")
total <- colSums(byDate[, 2:ncol(byDate)])
print("新規カラム作成")
today <- colSums(byDate[nrow(byDate), 2:ncol(byDate)])
print("昨日までカラム作成")
untilToday <- colSums(byDate[1:nrow(byDate) - 1, 2:ncol(byDate)])
print("感染者推移カラム作成")
dateSpan <- 21
diffSparkline <- sapply(2:ncol(byDate), function(i) {
# 新規値
value <- byDate[(nrow(byDate) - dateSpan):nrow(byDate), i, with = F][[1]]
# 累計値
cumsumValue <- c(cumsum(byDate[, i, with = F])[(nrow(byDate) - dateSpan):nrow(byDate)])[[1]]
# 日付
date <- byDate[(nrow(byDate) - dateSpan):nrow(byDate), 1, with = F][[1]]
colorMapSetting <- rep("#E7ADA6", length(value))
colorMapSetting[length(value)] <- darkRed
namesSetting <- as.list(date)
names(namesSetting) <- 0:(length(value) - 1)
# 新規
diff <- sparkline(
values = value,
type = "bar",
chartRangeMin = 0,
width = 80,
tooltipFormat = "{{offset:names}}<br><span style='color: {{color}}'>●</span> 新規{{value}}名",
tooltipValueLookups = list(
names = namesSetting
),
colorMap = colorMapSetting
)
# 累計
cumsumSpk <- sparkline(
values = cumsumValue,
type = "line",
width = 80,
fillColor = F,
lineColor = darkRed,
tooltipFormat = "<span style='color: {{color}}'>●</span> 累計{{y}}名"
)
return(as.character(htmltools::as.tags(spk_composite(diff, cumsumSpk))))
})
print("新規回復者カラム作成")
mhlwSummary <- fread(file = "50_Data/MHLW/summary.csv")
mhlwSummary$日付 <- as.Date(as.character(mhlwSummary$日付), "%Y%m%d")
mhlwSummary[order(日付), dischargedDiff := 退院者 - shift(退院者), by = "都道府県名"]
print("回復推移")
dischargedDiffSparkline <- sapply(colnames(byDate)[2:48], function(region) {
data <- mhlwSummary[`都道府県名` == region]
# 新規
span <- nrow(data) - dateSpan
value <- data$dischargedDiff[ifelse(span < 0, 0, span):nrow(data)]
# 日付
date <- data$日付[ifelse(span < 0, 0, span):nrow(data)]
namesSetting <- as.list(date)
names(namesSetting) <- 0:(length(date) - 1)
if (length(value) > 0) {
diff <- spk_chr(
values = value,
type = "bar",
width = 80,
barColor = middleGreen,
tooltipFormat = "{{offset:names}}<br><span style='color: {{color}}'>●</span> 新規回復{{value}}名",
tooltipValueLookups = list(
names = namesSetting
)
)
} else {
diff <- NA
}
return(diff)
})
print("死亡カラム作成")
deathByRegion <- stack(colSums(death[, 2:ncol(byDate)]))
print("感染者内訳")
detailSparkLineDt <- mhlwSummary[日付 == max(日付)]
detailSparkLine <- sapply(detailSparkLineDt$都道府県名, function(region) {
# 速報値との差分処理
regionNew <- ifelse(region == "空港検疫", "検疫職員", region)
confirmed <- ifelse(total[names(total) == regionNew][[1]] > detailSparkLineDt[都道府県名 == region, 陽性者],
total[names(total) == regionNew][[1]],
detailSparkLineDt[都道府県名 == region, 陽性者]
)
spk_chr(
type = "pie",
values = c(
confirmed - sum(detailSparkLineDt[都道府県名 == region, .(入院中, 退院者, 死亡者)], na.rm = T) -
ifelse(region == "クルーズ船", 40, 0),
detailSparkLineDt[都道府県名 == region, 入院中],
detailSparkLineDt[都道府県名 == region, 退院者],
detailSparkLineDt[都道府県名 == region, 死亡者]
),
sliceColors = c(middleRed, middleYellow, middleGreen, darkNavy),
tooltipFormat = '<span style="color: {{color}}">●</span> {{offset:names}}<br>{{value}} 名 ({{percent.1}}%)',
tooltipValueLookups = list(
names = list(
"0" = "情報待ち陽性者",
"1" = "入院者",
"2" = "回復者",
"3" = "死亡者"
)
)
)
})
print("二倍時間集計")
dt <- byDate[, 2:ncol(byDate)]
halfCount <- colSums(dt) / 2
dt <- cumsum(dt)
doubleTimeDay <- lapply(seq(halfCount), function(index) {
prefDt <- dt[, index, with = F]
nrow(prefDt[c(prefDt > halfCount[index])])
})
names(doubleTimeDay) <- names(dt)
# 回復者総数
totalDischarged <- mhlwSummary[日付 == max(日付), .(都道府県名, 退院者)]
colnames(totalDischarged) <- c("region", "totalDischarged")
print("都道府県別PCRデータ作成")
mhlwSummary[, 前日比 := 検査人数 - shift(検査人数), by = c("都道府県名")]
mhlwSummary[, 週間平均移動 := round(frollmean(前日比, 7), 0), by = c("都道府県名")]
mhlwSummary[, 陽性率 := round(陽性者 / 検査人数 * 100, 1)]
pcrByRegionToday <- mhlwSummary[日付 == max(日付)]
pcrDiffSparkline <- sapply(pcrByRegionToday$都道府県名, function(region) {
data <- mhlwSummary[都道府県名 == region]
# 新規
span <- nrow(data) - dateSpan
value <- data$前日比[ifelse(span < 0, 0, span):nrow(data)]
# 日付
date <- data$日付[ifelse(span < 0, 0, span):nrow(data)]
namesSetting <- as.list(date)
names(namesSetting) <- 0:(length(date) - 1)
if (length(value) > 0) {
diff <- spk_chr(
values = value,
type = "bar",
width = 80,
barColor = middleYellow,
tooltipFormat = "{{offset:names}}<br><span style='color: {{color}}'>●</span> 新規{{value}}",
tooltipValueLookups = list(
names = namesSetting
)
)
} else {
diff <- NA
}
return(diff)
})
positiveRatioSparkline <- sapply(pcrByRegionToday$都道府県名, function(region) {
data <- mhlwSummary[都道府県名 == region]
# 新規
span <- nrow(data) - dateSpan
value <- data$陽性率[ifelse(span < 0, 0, span):nrow(data)]
# 日付
date <- data$日付[ifelse(span < 0, 0, span):nrow(data)]
namesSetting <- as.list(date)
names(namesSetting) <- 0:(length(date) - 1)
if (length(value) > 0) {
diff <- spk_chr(
values = value,
type = "line",
width = 80,
lineColor = darkRed,
fillColor = "#f2b3aa",
tooltipFormat = "{{offset:names}}<br><span style='color: {{color}}'>●</span> 陽性率：{{y}}%",
tooltipValueLookups = list(
names = namesSetting
)
)
} else {
diff <- NA
}
return(diff)
})
pcrByRegionToday$検査数推移 <- pcrDiffSparkline
pcrByRegionToday$陽性率推移 <- positiveRatioSparkline
print("テーブル作成")
totalToday <- paste(sprintf("%06d", total), total, today, sep = "|")
mergeDt <- data.table(
region = names(total),
count = total,
today = today,
totalToday = totalToday,
untilToday = untilToday,
diff = diffSparkline,
dischargeDiff = "",
detailBullet = "",
death = deathByRegion$values,
zeroContinuousDay = zeroContinuousDay$values,
doubleTimeDay = doubleTimeDay
)
mergeDt <- merge(mergeDt, totalDischarged, all.x = T, sort = F)
signateSub <- provinceAttr[, .(都道府県略称, 人口)]
colnames(signateSub) <- c("region", "population")
mergeDt <- merge(mergeDt, signateSub, all.x = T, sort = F)
mergeDt[, perMillion := round(count / (population / 1000000), 0)]
mergeDt[, perMillionDeath := round(death / (population / 1000000), 0)]
for (i in mergeDt$region) {
mergeDt[region == i]$dischargeDiff <- dischargedDiffSparkline[i][[1]]
mergeDt[region == i]$detailBullet <- detailSparkLine[i][[1]]
}
# グルーピング
groupList <- list(
"北海道・東北" = provinceAttr[都道府県コード %in% 1:7]$都道府県略称,
"関東" = provinceAttr[都道府県コード %in% 8:14]$都道府県略称,
"中部" = provinceAttr[都道府県コード %in% 15:23]$都道府県略称,
"近畿" = provinceAttr[都道府県コード %in% 24:30]$都道府県略称,
"中国" = provinceAttr[都道府県コード %in% 31:35]$都道府県略称,
"四国" = provinceAttr[都道府県コード %in% 36:39]$都道府県略称,
"九州・沖縄" = provinceAttr[都道府県コード %in% 40:47]$都道府県略称,
"他" = colnames(byDate)[(ncol(byDate) - 3):ncol(byDate)]
)
mergeDt$group = ""
for (i in seq(nrow(mergeDt))) {
mergeDt[i]$group <- names(which(lapply(groupList, function(x) { mergeDt$region[i] %in% x }) == T))
}
# 面積あたりの感染者数
area <- fread(paste0(DATA_PATH, "Collection/area.csv"))
area[, 都道府県略称 := 都道府県]
area[, 都道府県略称 := gsub("県", "", 都道府県略称)]
area[, 都道府県略称 := gsub("府", "", 都道府県略称)]
area[, 都道府県略称 := gsub("東京都", "東京", 都道府県略称)]
mergeDt <- merge(mergeDt, area, by.x = "region", by.y = "都道府県略称", all.x = T, no.dups = T, sort = F)
mergeDt[, perArea := round(sqrt(可住地面積 / count), 2)]
mergeDt[, `:=` (コード = NULL, 都道府県 = NULL, 可住地面積 = NULL, 可住地面積割合 = NULL, 宅地面積 = NULL, 宅地面積割合 = NULL)]
pcrByRegionToday[, `:=` (dischargedDiff = NULL)]
mergeDt <- merge(mergeDt, pcrByRegionToday, by.x = "region", by.y = "都道府県名", all.x = T, no.dups = T, sort = F)
active <- mergeDt$陽性者 - mergeDt$退院者 - ifelse(is.na(mergeDt$死亡者), 0, mergeDt$死亡者)
mergeDt[, `:=` (日付 = NULL, 陽性者 = NULL, 入院中 = NULL, 退院者 = NULL, 死亡者 = NULL, 確認中 = NULL, 分類 = NULL)]
mergeDt[, 百万人あたり := round(検査人数 / (population / 1000000), 0)]
mergeDt[, population := NULL]
# 現在患者数
mergeDt$active <- active
mergeDt[active < 0, active := 0] # チャーター便の単独対応
mergeDt[region == "クルーズ船", active := active - 40] # クルーズ船の単独対応
# 13個特定警戒都道府県
alertPref <-
c(
# "東京",
# "大阪",
# "北海道",
# "茨城",
# "埼玉",
# "千葉",
# "神奈川",
# "石川",
# "岐阜",
# "愛知",
# "京都",
# "兵庫",
# "福岡"
)
for(i in seq(nrow(mergeDt))) {
if (mergeDt[i]$region %in% alertPref) {
mergeDt[i]$region <- paste0("<i style='color:#DD4B39;' class=\"fa fa-exclamation-triangle\"></i>", "<span style='float:right;'>", mergeDt[i]$region, "</span>")
} else if (mergeDt[i]$active == 0 && !is.na(mergeDt[i]$active)) {
mergeDt[i]$region <- paste0("<i style='color:#01A65A;' class=\"fa fa-check-circle\"></i>", "<span style='float:right;'>", mergeDt[i]$region, "</span>")
} else {
mergeDt[i]$region <- paste0("<span style='float:right;'>", mergeDt[i]$region, "</span>")
}
}
# 自治体名前ソート用
prefNameId <- sprintf('%02d', seq(2:ncol(byDate)))
mergeDt[, region := paste0(prefNameId, "|", region)]
# オーダー
# setorder(mergeDt, - count)
# 読み取り時のエラーを回避するため
mergeDt[, diff := gsub("\\n", "", diff)]
mergeDt[, dischargeDiff := gsub("\\n", "", dischargeDiff)]
mergeDt[, detailBullet := gsub("\\n", "", detailBullet)]
mergeDt[, 検査数推移 := gsub("\\n", "", 検査数推移)]
mergeDt[, 陽性率推移 := gsub("\\n", "", 陽性率推移)]
# クルーズ船とチャーター便データ除外
# mergeDt <- mergeDt[!grepl(pattern = paste0(lang[[langCode]][35:36], collapse = "|"), x = mergeDt$region)]
print("テーブル出力")
fwrite(x = mergeDt, file = paste0(DATA_PATH, "Generated/resultSummaryTable.ja.csv"), sep = "@", quote = F)
source(file = "00_System/CreateTable.Translate.R")
# ====マップ用のデータ作成====
dt <- data.frame(date = byDate$date)
for (i in 2:ncol(byDate)) {
dt[, i] <- cumsum(byDate[, i, with = F])
}
dt <- reshape2::melt(dt, id.vars = "date")
dt <- data.table(dt)
mapDt <- dt[!(variable %in% c("クルーズ船", "伊客船", "チャーター便", "検疫職員"))]
# マップデータ用意
mapDt <- merge(x = mapDt, y = provinceCode, by.x = "variable", by.y = "name-ja", all = T)
mapDt <- merge(x = mapDt, y = provinceAttr, by.x = "variable", by.y = "都道府県略称", all = T)
# 必要なカラムを保存
mapDt <- mapDt[, .(date, variable, 都道府県, `name-en`, value, regions, lat, lng)]
# カラム名変更
colnames(mapDt) <- c("date", "ja", "full_ja", "en", "count", "regions", "lat", "lng")
fwrite(x = mapDt, file = paste0(DATA_PATH, "result.map.csv"))
|
/00_System/CreateTable.R
|
permissive
|
YTLogos/2019-ncov-japan
|
R
| false | false | 14,293 |
r
|
library(data.table)
library(sparkline)
# ====準備部分====
source(file = "01_Settings/Path.R", local = T, encoding = "UTF-8")
# 感染者ソーステーブルを取得
byDate <- fread(paste0(DATA_PATH, "byDate.csv"), header = T)
byDate[is.na(byDate)] <- 0
byDate$date <- lapply(byDate[, 1], function(x) {
as.Date(as.character(x), format = "%Y%m%d")
})
# 死亡データ
death <- fread(paste0(DATA_PATH, "death.csv"))
death[is.na(death)] <- 0
# 文言データを取得
lang <- fread(paste0(DATA_PATH, "lang.csv"))
langCode <- "ja"
# 都道府県
provinceCode <- fread(paste0(DATA_PATH, "prefectures.csv"))
provinceSelector <- provinceCode$id
names(provinceSelector) <- provinceCode$`name-ja`
provinceAttr <- fread(paste0(DATA_PATH, "Signate/prefMaster.csv"))
provinceAttr[, 都道府県略称 := 都道府県]
provinceAttr[, 都道府県略称 := gsub("県", "", 都道府県略称)]
provinceAttr[, 都道府県略称 := gsub("府", "", 都道府県略称)]
provinceAttr[, 都道府県略称 := gsub("東京都", "東京", 都道府県略称)]
# 色設定
lightRed <- "#F56954"
middleRed <- "#DD4B39"
darkRed <- "#B03C2D"
lightYellow <- "#F8BF76"
middleYellow <- "#F39C11"
darkYellow <- "#DB8B0A"
lightGreen <- "#00A65A"
middleGreen <- "#01A65A"
darkGreen <- "#088448"
superDarkGreen <- "#046938"
lightNavy <- "#5A6E82"
middelNavy <- "#001F3F"
darkNavy <- "#001934"
lightGrey <- "#F5F5F5"
lightBlue <- "#7BD6F5"
middleBlue <- "#00C0EF"
darkBlue <- "#00A7D0"
# ====各都道府県のサマリーテーブル====
# ランキングカラムを作成
# cumDt <- cumsum(byDate[, c(2:48, 50)])
# rankDt <- data.table(t(apply(-cumDt, 1, function(x){rank(x, ties.method = 'min')})))
# rankDt[, colnames(rankDt) := shift(.SD, fill = 0) - .SD, .SDcols = colnames(rankDt)]
#
# rankDt[rankDt == 0] <- '-'
# rankDt[, colnames(rankDt) := ifelse(.SD > 0, paste0('+', .SD), .SD), .SDcols = colnames(rankDt)]
print("新規なし継続日数カラム作成")
zeroContinuousDay <- stack(lapply(byDate[, 2:ncol(byDate)], function(region) {
continuousDay <- 0
for (x in region) {
if (x == 0) {
continuousDay <- continuousDay + 1
} else {
continuousDay <- 0
}
}
return(continuousDay - 1)
}))
print("感染確認カラム作成")
total <- colSums(byDate[, 2:ncol(byDate)])
print("新規カラム作成")
today <- colSums(byDate[nrow(byDate), 2:ncol(byDate)])
print("昨日までカラム作成")
untilToday <- colSums(byDate[1:nrow(byDate) - 1, 2:ncol(byDate)])
print("感染者推移カラム作成")
dateSpan <- 21
diffSparkline <- sapply(2:ncol(byDate), function(i) {
# 新規値
value <- byDate[(nrow(byDate) - dateSpan):nrow(byDate), i, with = F][[1]]
# 累計値
cumsumValue <- c(cumsum(byDate[, i, with = F])[(nrow(byDate) - dateSpan):nrow(byDate)])[[1]]
# 日付
date <- byDate[(nrow(byDate) - dateSpan):nrow(byDate), 1, with = F][[1]]
colorMapSetting <- rep("#E7ADA6", length(value))
colorMapSetting[length(value)] <- darkRed
namesSetting <- as.list(date)
names(namesSetting) <- 0:(length(value) - 1)
# 新規
diff <- sparkline(
values = value,
type = "bar",
chartRangeMin = 0,
width = 80,
tooltipFormat = "{{offset:names}}<br><span style='color: {{color}}'>●</span> 新規{{value}}名",
tooltipValueLookups = list(
names = namesSetting
),
colorMap = colorMapSetting
)
# 累計
cumsumSpk <- sparkline(
values = cumsumValue,
type = "line",
width = 80,
fillColor = F,
lineColor = darkRed,
tooltipFormat = "<span style='color: {{color}}'>●</span> 累計{{y}}名"
)
return(as.character(htmltools::as.tags(spk_composite(diff, cumsumSpk))))
})
print("新規回復者カラム作成")
mhlwSummary <- fread(file = "50_Data/MHLW/summary.csv")
mhlwSummary$日付 <- as.Date(as.character(mhlwSummary$日付), "%Y%m%d")
mhlwSummary[order(日付), dischargedDiff := 退院者 - shift(退院者), by = "都道府県名"]
print("回復推移")
dischargedDiffSparkline <- sapply(colnames(byDate)[2:48], function(region) {
data <- mhlwSummary[`都道府県名` == region]
# 新規
span <- nrow(data) - dateSpan
value <- data$dischargedDiff[ifelse(span < 0, 0, span):nrow(data)]
# 日付
date <- data$日付[ifelse(span < 0, 0, span):nrow(data)]
namesSetting <- as.list(date)
names(namesSetting) <- 0:(length(date) - 1)
if (length(value) > 0) {
diff <- spk_chr(
values = value,
type = "bar",
width = 80,
barColor = middleGreen,
tooltipFormat = "{{offset:names}}<br><span style='color: {{color}}'>●</span> 新規回復{{value}}名",
tooltipValueLookups = list(
names = namesSetting
)
)
} else {
diff <- NA
}
return(diff)
})
print("死亡カラム作成")
deathByRegion <- stack(colSums(death[, 2:ncol(byDate)]))
print("感染者内訳")
detailSparkLineDt <- mhlwSummary[日付 == max(日付)]
detailSparkLine <- sapply(detailSparkLineDt$都道府県名, function(region) {
# 速報値との差分処理
regionNew <- ifelse(region == "空港検疫", "検疫職員", region)
confirmed <- ifelse(total[names(total) == regionNew][[1]] > detailSparkLineDt[都道府県名 == region, 陽性者],
total[names(total) == regionNew][[1]],
detailSparkLineDt[都道府県名 == region, 陽性者]
)
spk_chr(
type = "pie",
values = c(
confirmed - sum(detailSparkLineDt[都道府県名 == region, .(入院中, 退院者, 死亡者)], na.rm = T) -
ifelse(region == "クルーズ船", 40, 0),
detailSparkLineDt[都道府県名 == region, 入院中],
detailSparkLineDt[都道府県名 == region, 退院者],
detailSparkLineDt[都道府県名 == region, 死亡者]
),
sliceColors = c(middleRed, middleYellow, middleGreen, darkNavy),
tooltipFormat = '<span style="color: {{color}}">●</span> {{offset:names}}<br>{{value}} 名 ({{percent.1}}%)',
tooltipValueLookups = list(
names = list(
"0" = "情報待ち陽性者",
"1" = "入院者",
"2" = "回復者",
"3" = "死亡者"
)
)
)
})
print("二倍時間集計")
dt <- byDate[, 2:ncol(byDate)]
halfCount <- colSums(dt) / 2
dt <- cumsum(dt)
doubleTimeDay <- lapply(seq(halfCount), function(index) {
prefDt <- dt[, index, with = F]
nrow(prefDt[c(prefDt > halfCount[index])])
})
names(doubleTimeDay) <- names(dt)
# 回復者総数
totalDischarged <- mhlwSummary[日付 == max(日付), .(都道府県名, 退院者)]
colnames(totalDischarged) <- c("region", "totalDischarged")
print("都道府県別PCRデータ作成")
mhlwSummary[, 前日比 := 検査人数 - shift(検査人数), by = c("都道府県名")]
mhlwSummary[, 週間平均移動 := round(frollmean(前日比, 7), 0), by = c("都道府県名")]
mhlwSummary[, 陽性率 := round(陽性者 / 検査人数 * 100, 1)]
pcrByRegionToday <- mhlwSummary[日付 == max(日付)]
pcrDiffSparkline <- sapply(pcrByRegionToday$都道府県名, function(region) {
data <- mhlwSummary[都道府県名 == region]
# 新規
span <- nrow(data) - dateSpan
value <- data$前日比[ifelse(span < 0, 0, span):nrow(data)]
# 日付
date <- data$日付[ifelse(span < 0, 0, span):nrow(data)]
namesSetting <- as.list(date)
names(namesSetting) <- 0:(length(date) - 1)
if (length(value) > 0) {
diff <- spk_chr(
values = value,
type = "bar",
width = 80,
barColor = middleYellow,
tooltipFormat = "{{offset:names}}<br><span style='color: {{color}}'>●</span> 新規{{value}}",
tooltipValueLookups = list(
names = namesSetting
)
)
} else {
diff <- NA
}
return(diff)
})
positiveRatioSparkline <- sapply(pcrByRegionToday$都道府県名, function(region) {
data <- mhlwSummary[都道府県名 == region]
# 新規
span <- nrow(data) - dateSpan
value <- data$陽性率[ifelse(span < 0, 0, span):nrow(data)]
# 日付
date <- data$日付[ifelse(span < 0, 0, span):nrow(data)]
namesSetting <- as.list(date)
names(namesSetting) <- 0:(length(date) - 1)
if (length(value) > 0) {
diff <- spk_chr(
values = value,
type = "line",
width = 80,
lineColor = darkRed,
fillColor = "#f2b3aa",
tooltipFormat = "{{offset:names}}<br><span style='color: {{color}}'>●</span> 陽性率：{{y}}%",
tooltipValueLookups = list(
names = namesSetting
)
)
} else {
diff <- NA
}
return(diff)
})
pcrByRegionToday$検査数推移 <- pcrDiffSparkline
pcrByRegionToday$陽性率推移 <- positiveRatioSparkline
print("テーブル作成")
totalToday <- paste(sprintf("%06d", total), total, today, sep = "|")
mergeDt <- data.table(
region = names(total),
count = total,
today = today,
totalToday = totalToday,
untilToday = untilToday,
diff = diffSparkline,
dischargeDiff = "",
detailBullet = "",
death = deathByRegion$values,
zeroContinuousDay = zeroContinuousDay$values,
doubleTimeDay = doubleTimeDay
)
mergeDt <- merge(mergeDt, totalDischarged, all.x = T, sort = F)
signateSub <- provinceAttr[, .(都道府県略称, 人口)]
colnames(signateSub) <- c("region", "population")
mergeDt <- merge(mergeDt, signateSub, all.x = T, sort = F)
mergeDt[, perMillion := round(count / (population / 1000000), 0)]
mergeDt[, perMillionDeath := round(death / (population / 1000000), 0)]
for (i in mergeDt$region) {
mergeDt[region == i]$dischargeDiff <- dischargedDiffSparkline[i][[1]]
mergeDt[region == i]$detailBullet <- detailSparkLine[i][[1]]
}
# グルーピング
groupList <- list(
"北海道・東北" = provinceAttr[都道府県コード %in% 1:7]$都道府県略称,
"関東" = provinceAttr[都道府県コード %in% 8:14]$都道府県略称,
"中部" = provinceAttr[都道府県コード %in% 15:23]$都道府県略称,
"近畿" = provinceAttr[都道府県コード %in% 24:30]$都道府県略称,
"中国" = provinceAttr[都道府県コード %in% 31:35]$都道府県略称,
"四国" = provinceAttr[都道府県コード %in% 36:39]$都道府県略称,
"九州・沖縄" = provinceAttr[都道府県コード %in% 40:47]$都道府県略称,
"他" = colnames(byDate)[(ncol(byDate) - 3):ncol(byDate)]
)
mergeDt$group = ""
for (i in seq(nrow(mergeDt))) {
mergeDt[i]$group <- names(which(lapply(groupList, function(x) { mergeDt$region[i] %in% x }) == T))
}
# 面積あたりの感染者数
area <- fread(paste0(DATA_PATH, "Collection/area.csv"))
area[, 都道府県略称 := 都道府県]
area[, 都道府県略称 := gsub("県", "", 都道府県略称)]
area[, 都道府県略称 := gsub("府", "", 都道府県略称)]
area[, 都道府県略称 := gsub("東京都", "東京", 都道府県略称)]
mergeDt <- merge(mergeDt, area, by.x = "region", by.y = "都道府県略称", all.x = T, no.dups = T, sort = F)
mergeDt[, perArea := round(sqrt(可住地面積 / count), 2)]
mergeDt[, `:=` (コード = NULL, 都道府県 = NULL, 可住地面積 = NULL, 可住地面積割合 = NULL, 宅地面積 = NULL, 宅地面積割合 = NULL)]
pcrByRegionToday[, `:=` (dischargedDiff = NULL)]
mergeDt <- merge(mergeDt, pcrByRegionToday, by.x = "region", by.y = "都道府県名", all.x = T, no.dups = T, sort = F)
active <- mergeDt$陽性者 - mergeDt$退院者 - ifelse(is.na(mergeDt$死亡者), 0, mergeDt$死亡者)
mergeDt[, `:=` (日付 = NULL, 陽性者 = NULL, 入院中 = NULL, 退院者 = NULL, 死亡者 = NULL, 確認中 = NULL, 分類 = NULL)]
mergeDt[, 百万人あたり := round(検査人数 / (population / 1000000), 0)]
mergeDt[, population := NULL]
# 現在患者数
mergeDt$active <- active
mergeDt[active < 0, active := 0] # チャーター便の単独対応
mergeDt[region == "クルーズ船", active := active - 40] # クルーズ船の単独対応
# 13個特定警戒都道府県
alertPref <-
c(
# "東京",
# "大阪",
# "北海道",
# "茨城",
# "埼玉",
# "千葉",
# "神奈川",
# "石川",
# "岐阜",
# "愛知",
# "京都",
# "兵庫",
# "福岡"
)
for(i in seq(nrow(mergeDt))) {
if (mergeDt[i]$region %in% alertPref) {
mergeDt[i]$region <- paste0("<i style='color:#DD4B39;' class=\"fa fa-exclamation-triangle\"></i>", "<span style='float:right;'>", mergeDt[i]$region, "</span>")
} else if (mergeDt[i]$active == 0 && !is.na(mergeDt[i]$active)) {
mergeDt[i]$region <- paste0("<i style='color:#01A65A;' class=\"fa fa-check-circle\"></i>", "<span style='float:right;'>", mergeDt[i]$region, "</span>")
} else {
mergeDt[i]$region <- paste0("<span style='float:right;'>", mergeDt[i]$region, "</span>")
}
}
# 自治体名前ソート用
prefNameId <- sprintf('%02d', seq(2:ncol(byDate)))
mergeDt[, region := paste0(prefNameId, "|", region)]
# オーダー
# setorder(mergeDt, - count)
# 読み取り時のエラーを回避するため
mergeDt[, diff := gsub("\\n", "", diff)]
mergeDt[, dischargeDiff := gsub("\\n", "", dischargeDiff)]
mergeDt[, detailBullet := gsub("\\n", "", detailBullet)]
mergeDt[, 検査数推移 := gsub("\\n", "", 検査数推移)]
mergeDt[, 陽性率推移 := gsub("\\n", "", 陽性率推移)]
# クルーズ船とチャーター便データ除外
# mergeDt <- mergeDt[!grepl(pattern = paste0(lang[[langCode]][35:36], collapse = "|"), x = mergeDt$region)]
print("テーブル出力")
fwrite(x = mergeDt, file = paste0(DATA_PATH, "Generated/resultSummaryTable.ja.csv"), sep = "@", quote = F)
source(file = "00_System/CreateTable.Translate.R")
# ====マップ用のデータ作成====
dt <- data.frame(date = byDate$date)
for (i in 2:ncol(byDate)) {
dt[, i] <- cumsum(byDate[, i, with = F])
}
dt <- reshape2::melt(dt, id.vars = "date")
dt <- data.table(dt)
mapDt <- dt[!(variable %in% c("クルーズ船", "伊客船", "チャーター便", "検疫職員"))]
# マップデータ用意
mapDt <- merge(x = mapDt, y = provinceCode, by.x = "variable", by.y = "name-ja", all = T)
mapDt <- merge(x = mapDt, y = provinceAttr, by.x = "variable", by.y = "都道府県略称", all = T)
# 必要なカラムを保存
mapDt <- mapDt[, .(date, variable, 都道府県, `name-en`, value, regions, lat, lng)]
# カラム名変更
colnames(mapDt) <- c("date", "ja", "full_ja", "en", "count", "regions", "lat", "lng")
fwrite(x = mapDt, file = paste0(DATA_PATH, "result.map.csv"))
|
## ---- geographic-WGS84, echo=FALSE, message=FALSE, results='hide'--------
# read grat shapefile
worldGrat30 <- readOGR(dsn="../../Global/Boundaries/ne_110m_graticules_all",
layer="ne_110m_graticules_30")
# convert to dataframe
worldGrat30_df <- fortify(worldGrat30)
#import box
wgs84Box <- readOGR("../../Global/Boundaries/ne_110m_graticules_all",
layer="ne_110m_wgs84_bounding_box")
wgs84Box_df<- fortify(wgs84Box)
#plot data
ggplot(wgs84Box_df, aes(long,lat, group=group)) +
geom_polygon(fill="white") +
xlab("Longitude (Degrees)") + ylab("Latitude (Degrees)") +
ggtitle("World Map - Geographic WGS84 (lat/lon)") +
geom_polygon(data=worldBound_df, aes(long,lat, group=group, fill=hole))+
geom_path(data=worldGrat30, aes(long, lat, group=group, fill=NULL), linetype="dashed", color="grey50") +
labs(title="World map + graticule (longlat)") +
coord_equal() +
scale_fill_manual(values=c("black", "white"), guide="none") # change colors &
|
/code/R/dc-spatio-temporal-intro/05-geographic-vs-projected-CRS.R
|
no_license
|
statkclee/NEON-R-Spatio-Temporal-Data-and-Management-Intro
|
R
| false | false | 1,008 |
r
|
## ---- geographic-WGS84, echo=FALSE, message=FALSE, results='hide'--------
# read grat shapefile
worldGrat30 <- readOGR(dsn="../../Global/Boundaries/ne_110m_graticules_all",
layer="ne_110m_graticules_30")
# convert to dataframe
worldGrat30_df <- fortify(worldGrat30)
#import box
wgs84Box <- readOGR("../../Global/Boundaries/ne_110m_graticules_all",
layer="ne_110m_wgs84_bounding_box")
wgs84Box_df<- fortify(wgs84Box)
#plot data
ggplot(wgs84Box_df, aes(long,lat, group=group)) +
geom_polygon(fill="white") +
xlab("Longitude (Degrees)") + ylab("Latitude (Degrees)") +
ggtitle("World Map - Geographic WGS84 (lat/lon)") +
geom_polygon(data=worldBound_df, aes(long,lat, group=group, fill=hole))+
geom_path(data=worldGrat30, aes(long, lat, group=group, fill=NULL), linetype="dashed", color="grey50") +
labs(title="World map + graticule (longlat)") +
coord_equal() +
scale_fill_manual(values=c("black", "white"), guide="none") # change colors &
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{rx_state}
\alias{rx_state}
\title{State regex}
\format{A character string (length 1).}
\usage{
rx_state
}
\description{
The regex string to extract state string preceding ZIP code.
}
\keyword{datasets}
|
/man/rx_state.Rd
|
permissive
|
Yanqi-Xu/campfin
|
R
| false | true | 308 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{rx_state}
\alias{rx_state}
\title{State regex}
\format{A character string (length 1).}
\usage{
rx_state
}
\description{
The regex string to extract state string preceding ZIP code.
}
\keyword{datasets}
|
shinyUI(
fluidPage(
# output
plotOutput("grafico"),
# código para aparecer uma imagem
div(img(src = "img_maneira.png", width = "50%"), style = "text-align: center"),
br(), br(),
# código para baixar um arquivo
a(href = "pdf_interessante.pdf", "Clique aqui", target="_blank"), "parar baixar um pdf interessante."
)
)
|
/shiny/leitura e imagens/ui.R
|
no_license
|
daianemarcolino/dataVisualizationR
|
R
| false | false | 385 |
r
|
shinyUI(
fluidPage(
# output
plotOutput("grafico"),
# código para aparecer uma imagem
div(img(src = "img_maneira.png", width = "50%"), style = "text-align: center"),
br(), br(),
# código para baixar um arquivo
a(href = "pdf_interessante.pdf", "Clique aqui", target="_blank"), "parar baixar um pdf interessante."
)
)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/5.2-build.R
\name{buildANN}
\alias{buildANN}
\title{Build Artificial Neural Network Model}
\usage{
buildANN(object, top = 0, ...)
}
\arguments{
\item{object}{An \code{ExprsArray} object. The training set.}
\item{top}{A numeric scalar or character vector. A numeric scalar indicates
the number of top features that should undergo feature selection. A character vector
indicates specifically which features by name should undergo feature selection.
Set \code{top = 0} to include all features. A numeric vector can also be used
to indicate specific features by location, similar to a character vector.}
\item{...}{Arguments passed to the detailed function.}
}
\value{
Returns an \code{ExprsModel} object.
}
\description{
\code{buildANN} builds a model using the \code{nnet} function
from the \code{nnet} package.
}
|
/man/buildANN.Rd
|
no_license
|
tpq/exprso
|
R
| false | true | 893 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/5.2-build.R
\name{buildANN}
\alias{buildANN}
\title{Build Artificial Neural Network Model}
\usage{
buildANN(object, top = 0, ...)
}
\arguments{
\item{object}{An \code{ExprsArray} object. The training set.}
\item{top}{A numeric scalar or character vector. A numeric scalar indicates
the number of top features that should undergo feature selection. A character vector
indicates specifically which features by name should undergo feature selection.
Set \code{top = 0} to include all features. A numeric vector can also be used
to indicate specific features by location, similar to a character vector.}
\item{...}{Arguments passed to the detailed function.}
}
\value{
Returns an \code{ExprsModel} object.
}
\description{
\code{buildANN} builds a model using the \code{nnet} function
from the \code{nnet} package.
}
|
library("magrittr")
# helper to shoot a review
webshoot_review <- function(review_url, package_name, path){
comment_id <- stringr::str_replace(review_url, ".*issuecomment", "issuecomment")
webshot::webshot(review_url,
selector = ".js-comment-container",
file = paste0(package, ".png"))
}
# helper to shoot the whole thread
webshoot_thread <- function(issue_url, package_name, path){
webshot::webshot(issue_url,
selector = "#show_issue",
file = paste0(path, package, "_whole_issue.png"))
}
# helper to shoot the upper info
webshoot_upper <- function(issue_url, package_name, path){
webshot::webshot(issue_url,
selector = "#partial-discussion-header",
file = paste0(path, package, "_upper.png"),
expand = c(10, 10, 600, 10))
}
# helper to shoot the first answer
webshoot_first_response <- function(issue_url, package_name, path){
webshot::webshot(issue_url,
selector = ".js-comment-container:nth-of-type(2)",
file = paste0(path, package, "_first_response.png"),
expand = c(10, 10, 10, 10))
}
shoot_package_onboarding <- function(package_name, path = "screenshots/"){
# airtable data
airtable <- airtabler::airtable("appZIB8hgtvjoV99D", "Reviews")
airtable <- airtable$Reviews$select_all()
review_info <- dplyr::filter(airtable, package == package_name)
issue_url <- dplyr::pull(review_info, onboarding_url) %>% unique()
review_urls <- dplyr::pull(review_info, review_url)
webshoot_thread(issue_url, package_name, path)
webshoot_meta(issue_url, package_name, path)
purrr::walk(review_urls, webshoot_review, package_name, path)
}
|
/B_analysts_sources_github/maelle/shoot_onboarding/webshoot_onboarding.R
|
no_license
|
Irbis3/crantasticScrapper
|
R
| false | false | 1,753 |
r
|
library("magrittr")
# helper to shoot a review
webshoot_review <- function(review_url, package_name, path){
comment_id <- stringr::str_replace(review_url, ".*issuecomment", "issuecomment")
webshot::webshot(review_url,
selector = ".js-comment-container",
file = paste0(package, ".png"))
}
# helper to shoot the whole thread
webshoot_thread <- function(issue_url, package_name, path){
webshot::webshot(issue_url,
selector = "#show_issue",
file = paste0(path, package, "_whole_issue.png"))
}
# helper to shoot the upper info
webshoot_upper <- function(issue_url, package_name, path){
webshot::webshot(issue_url,
selector = "#partial-discussion-header",
file = paste0(path, package, "_upper.png"),
expand = c(10, 10, 600, 10))
}
# helper to shoot the first answer
webshoot_first_response <- function(issue_url, package_name, path){
webshot::webshot(issue_url,
selector = ".js-comment-container:nth-of-type(2)",
file = paste0(path, package, "_first_response.png"),
expand = c(10, 10, 10, 10))
}
shoot_package_onboarding <- function(package_name, path = "screenshots/"){
# airtable data
airtable <- airtabler::airtable("appZIB8hgtvjoV99D", "Reviews")
airtable <- airtable$Reviews$select_all()
review_info <- dplyr::filter(airtable, package == package_name)
issue_url <- dplyr::pull(review_info, onboarding_url) %>% unique()
review_urls <- dplyr::pull(review_info, review_url)
webshoot_thread(issue_url, package_name, path)
webshoot_meta(issue_url, package_name, path)
purrr::walk(review_urls, webshoot_review, package_name, path)
}
|
#Example 3, section 4.2, page 230
#if u=(2,3,-1,2) is a vector in R4 and c=-2, then find c*u :
u<-c(2,3,-1,2)
c=-2
final<- c*u
print(final)
|
/ashiq/R codes/chapter 4/example3_sec4_2.R
|
no_license
|
sahridhaya/BitPlease
|
R
| false | false | 141 |
r
|
#Example 3, section 4.2, page 230
#if u=(2,3,-1,2) is a vector in R4 and c=-2, then find c*u :
u<-c(2,3,-1,2)
c=-2
final<- c*u
print(final)
|
#' @title Sample Data for Multivariate Small Area Estimation with Difference Benchmarking with clustering
#' @description Dataset to simulate difference benchmarking of Multivariate Fay Herriot model for non-sampled area using clustering
#' This data is generated base on multivariate Fay Herriot model by these following steps:
#' \enumerate{
#' \item Generate explanatory variables \code{X1 and X2}. Take \eqn{\mu_{X1}}{\muX1} = \eqn{\mu_{X1}}{\muX1} = 10, \eqn{\sigma_{X11}}{\sigmaX11}=1, \eqn{\sigma_{X2}}{\sigmaX22}=2, and \eqn{\rho_{x}}{\rhox}= 1/2.
#' \cr Sampling error \code{e} is generated with the following \eqn{\sigma_{e11}}{\sigmae11} = 0.15, \eqn{\sigma_{e22}}{\sigmae22} = 0.25, \eqn{\sigma_{e33}}{\sigmae33} = 0.35, and \eqn{\rho_{e}}{\rhoe} = 1/2.
#' \cr For random effect \code{u}, we set \eqn{\sigma_{u11}}{\sigmau11}= 0.2, \eqn{\sigma_{u22}}{\sigmau22}= 0.6, and \eqn{\sigma_{u33}}{\sigmau33}= 1.8.
#' \cr For the weight we generate \code{w1 w2 w3} by set the \code{w1 ~ U(25,30)} , \code{w2 ~ U(25,30)}, \code{w3 ~ U(25,30)}
#' \cr Calculate direct estimation \code{Y1 Y2 Y3} where \eqn{Y_{i}}{Yi} = \eqn{X * \beta + u_{i} + e_{i}}{X\beta+ui+ei}
#' \cr \code{cl1 cl2 cl3} were obtained using K-Means clustering from the explanatory variables.
#' \item Then combine the direct estimations \code{Y1 Y2 Y3}, explanatory variables \code{X1 X2}, weights \code{w1 w2 w3}, and sampling varians covarians \code{v1 v12 v13 v2 v23 v3} in a data frame then named as datamsaeDB
#' }
#'
#' @format A data frame with 30 rows and 18 variables:
#' \describe{
#' \item{clY1}{cluster information of Y1}
#' \item{clY2}{cluster information of Y2}
#' \item{clY3}{cluster information of Y3}
#' \item{nonsample}{A column with logical values, \code{TRUE} if the area is non-sampled}
#' \item{Y1}{Direct Estimation of Y1}
#' \item{Y2}{Direct Estimation of Y2}
#' \item{Y3}{Direct Estimation of Y3}
#' \item{X1}{Auxiliary variable of X1}
#' \item{X2}{Auxiliary variable of X2}
#' \item{w1}{Known proportion of units in small areas of Y1}
#' \item{w2}{Known proportion of units in small areas of Y2}
#' \item{w3}{Known proportion of units in small areas of Y3}
#' \item{v1}{Sampling Variance of Y1}
#' \item{v12}{Sampling Covariance of Y1 and Y2}
#' \item{v13}{Sampling Covariance of Y1 and Y3}
#' \item{v2}{Sampling Variance of Y2}
#' \item{v23}{Sampling Covariance of Y2 and Y3}
#' \item{v3}{Sampling Variance of Y3}
#' }
#'
"datamsaeDBns"
|
/R/datamsaeDBns.R
|
no_license
|
cran/msaeDB
|
R
| false | false | 2,542 |
r
|
#' @title Sample Data for Multivariate Small Area Estimation with Difference Benchmarking with clustering
#' @description Dataset to simulate difference benchmarking of Multivariate Fay Herriot model for non-sampled area using clustering
#' This data is generated base on multivariate Fay Herriot model by these following steps:
#' \enumerate{
#' \item Generate explanatory variables \code{X1 and X2}. Take \eqn{\mu_{X1}}{\muX1} = \eqn{\mu_{X1}}{\muX1} = 10, \eqn{\sigma_{X11}}{\sigmaX11}=1, \eqn{\sigma_{X2}}{\sigmaX22}=2, and \eqn{\rho_{x}}{\rhox}= 1/2.
#' \cr Sampling error \code{e} is generated with the following \eqn{\sigma_{e11}}{\sigmae11} = 0.15, \eqn{\sigma_{e22}}{\sigmae22} = 0.25, \eqn{\sigma_{e33}}{\sigmae33} = 0.35, and \eqn{\rho_{e}}{\rhoe} = 1/2.
#' \cr For random effect \code{u}, we set \eqn{\sigma_{u11}}{\sigmau11}= 0.2, \eqn{\sigma_{u22}}{\sigmau22}= 0.6, and \eqn{\sigma_{u33}}{\sigmau33}= 1.8.
#' \cr For the weight we generate \code{w1 w2 w3} by set the \code{w1 ~ U(25,30)} , \code{w2 ~ U(25,30)}, \code{w3 ~ U(25,30)}
#' \cr Calculate direct estimation \code{Y1 Y2 Y3} where \eqn{Y_{i}}{Yi} = \eqn{X * \beta + u_{i} + e_{i}}{X\beta+ui+ei}
#' \cr \code{cl1 cl2 cl3} were obtained using K-Means clustering from the explanatory variables.
#' \item Then combine the direct estimations \code{Y1 Y2 Y3}, explanatory variables \code{X1 X2}, weights \code{w1 w2 w3}, and sampling varians covarians \code{v1 v12 v13 v2 v23 v3} in a data frame then named as datamsaeDB
#' }
#'
#' @format A data frame with 30 rows and 18 variables:
#' \describe{
#' \item{clY1}{cluster information of Y1}
#' \item{clY2}{cluster information of Y2}
#' \item{clY3}{cluster information of Y3}
#' \item{nonsample}{A column with logical values, \code{TRUE} if the area is non-sampled}
#' \item{Y1}{Direct Estimation of Y1}
#' \item{Y2}{Direct Estimation of Y2}
#' \item{Y3}{Direct Estimation of Y3}
#' \item{X1}{Auxiliary variable of X1}
#' \item{X2}{Auxiliary variable of X2}
#' \item{w1}{Known proportion of units in small areas of Y1}
#' \item{w2}{Known proportion of units in small areas of Y2}
#' \item{w3}{Known proportion of units in small areas of Y3}
#' \item{v1}{Sampling Variance of Y1}
#' \item{v12}{Sampling Covariance of Y1 and Y2}
#' \item{v13}{Sampling Covariance of Y1 and Y3}
#' \item{v2}{Sampling Variance of Y2}
#' \item{v23}{Sampling Covariance of Y2 and Y3}
#' \item{v3}{Sampling Variance of Y3}
#' }
#'
"datamsaeDBns"
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/profanity_by.R
\name{profanity_by}
\alias{profanity_by}
\title{Profanity Rate By Groups}
\usage{
profanity_by(text.var, by = NULL, group.names, ...)
}
\arguments{
\item{text.var}{The text variable. Also takes a \code{profanityr} or
\code{profanity_by} object.}
\item{by}{The grouping variable(s). Default \code{NULL} uses the original
row/element indices; if you used a column of 12 rows for \code{text.var}
these 12 rows will be used as the grouping variable. Also takes a single
grouping variable or a list of 1 or more grouping variables.}
\item{group.names}{A vector of names that corresponds to group. Generally
for internal use.}
\item{\ldots}{Other arguments passed to \code{\link[sentimentr]{profanity}}.}
}
\value{
Returns a \pkg{data.table} with grouping variables plus:
\itemize{
\item element_id - The id number of the original vector passed to \code{profanity}
\item sentence_id - The id number of the sentences within each \code{element_id}
\item word_count - Word count \code{\link[base]{sum}}med by grouping variable
\item profanity_count - The number of profanities used by grouping variable
\item sd - Standard deviation (\code{\link[stats]{sd}}) of the sentence level profanity rate by grouping variable
\item ave_profanity - Profanity rate
}
}
\description{
Approximate the profanity of text by grouping variable(s). For a
full description of the profanity detection algorithm see
\code{\link[sentimentr]{profanity}}. See \code{\link[sentimentr]{profanity}}
for more details about the algorithm, the profanity/valence shifter keys
that can be passed into the function, and other arguments that can be passed.
}
\section{Chaining}{
See the \code{\link[sentimentr]{sentiment_by}} for details about \pkg{sentimentr} chaining.
}
\examples{
\dontrun{
bw <- sample(lexicon::profanity_alvarez, 4)
mytext <- c(
sprintf('do you like this \%s? It is \%s. But I hate really bad dogs', bw[1], bw[2]),
'I am the best friend.',
NA,
sprintf('I \%s hate this \%s', bw[3], bw[4]),
"Do you really like it? I'm not happy"
)
## works on a character vector but not the preferred method avoiding the
## repeated cost of doing sentence boundary disambiguation every time
## `profanity` is run
profanity(mytext)
profanity_by(mytext)
## preferred method avoiding paying the cost
mytext <- get_sentences(mytext)
profanity_by(mytext)
get_sentences(profanity_by(mytext))
(myprofanity <- profanity_by(mytext))
stats::setNames(get_sentences(profanity_by(mytext)),
round(myprofanity[["ave_profanity"]], 3))
brady <- get_sentences(crowdflower_deflategate)
library(data.table)
bp <- profanity_by(brady)
crowdflower_deflategate[bp[ave_profanity > 0,]$element_id, ]
vulgars <- bp[["ave_profanity"]] > 0
stats::setNames(get_sentences(bp)[vulgars],
round(bp[["ave_profanity"]][vulgars], 3))
bt <- data.table(crowdflower_deflategate)[,
source := ifelse(grepl('^RT', text), 'retweet', 'OP')][,
belichick := grepl('\\\\bb[A-Za-z]+l[A-Za-z]*ch', text, ignore.case = TRUE)][]
prof_bel <- with(bt, profanity_by(text, by = list(source, belichick)))
plot(prof_bel)
}
}
\seealso{
Other profanity functions:
\code{\link{profanity}()}
}
\concept{profanity functions}
|
/man/profanity_by.Rd
|
no_license
|
cran/sentimentr
|
R
| false | true | 3,394 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/profanity_by.R
\name{profanity_by}
\alias{profanity_by}
\title{Profanity Rate By Groups}
\usage{
profanity_by(text.var, by = NULL, group.names, ...)
}
\arguments{
\item{text.var}{The text variable. Also takes a \code{profanityr} or
\code{profanity_by} object.}
\item{by}{The grouping variable(s). Default \code{NULL} uses the original
row/element indices; if you used a column of 12 rows for \code{text.var}
these 12 rows will be used as the grouping variable. Also takes a single
grouping variable or a list of 1 or more grouping variables.}
\item{group.names}{A vector of names that corresponds to group. Generally
for internal use.}
\item{\ldots}{Other arguments passed to \code{\link[sentimentr]{profanity}}.}
}
\value{
Returns a \pkg{data.table} with grouping variables plus:
\itemize{
\item element_id - The id number of the original vector passed to \code{profanity}
\item sentence_id - The id number of the sentences within each \code{element_id}
\item word_count - Word count \code{\link[base]{sum}}med by grouping variable
\item profanity_count - The number of profanities used by grouping variable
\item sd - Standard deviation (\code{\link[stats]{sd}}) of the sentence level profanity rate by grouping variable
\item ave_profanity - Profanity rate
}
}
\description{
Approximate the profanity of text by grouping variable(s). For a
full description of the profanity detection algorithm see
\code{\link[sentimentr]{profanity}}. See \code{\link[sentimentr]{profanity}}
for more details about the algorithm, the profanity/valence shifter keys
that can be passed into the function, and other arguments that can be passed.
}
\section{Chaining}{
See the \code{\link[sentimentr]{sentiment_by}} for details about \pkg{sentimentr} chaining.
}
\examples{
\dontrun{
bw <- sample(lexicon::profanity_alvarez, 4)
mytext <- c(
sprintf('do you like this \%s? It is \%s. But I hate really bad dogs', bw[1], bw[2]),
'I am the best friend.',
NA,
sprintf('I \%s hate this \%s', bw[3], bw[4]),
"Do you really like it? I'm not happy"
)
## works on a character vector but not the preferred method avoiding the
## repeated cost of doing sentence boundary disambiguation every time
## `profanity` is run
profanity(mytext)
profanity_by(mytext)
## preferred method avoiding paying the cost
mytext <- get_sentences(mytext)
profanity_by(mytext)
get_sentences(profanity_by(mytext))
(myprofanity <- profanity_by(mytext))
stats::setNames(get_sentences(profanity_by(mytext)),
round(myprofanity[["ave_profanity"]], 3))
brady <- get_sentences(crowdflower_deflategate)
library(data.table)
bp <- profanity_by(brady)
crowdflower_deflategate[bp[ave_profanity > 0,]$element_id, ]
vulgars <- bp[["ave_profanity"]] > 0
stats::setNames(get_sentences(bp)[vulgars],
round(bp[["ave_profanity"]][vulgars], 3))
bt <- data.table(crowdflower_deflategate)[,
source := ifelse(grepl('^RT', text), 'retweet', 'OP')][,
belichick := grepl('\\\\bb[A-Za-z]+l[A-Za-z]*ch', text, ignore.case = TRUE)][]
prof_bel <- with(bt, profanity_by(text, by = list(source, belichick)))
plot(prof_bel)
}
}
\seealso{
Other profanity functions:
\code{\link{profanity}()}
}
\concept{profanity functions}
|
#' Groups
#'
#' Function to get groups of users.
#'
#' @param email Email
#' @param apikey Your api key from settings in interface
#' @param domain Your domain in AmoCRM (xxx in xxx.amocrm.ru)
#' @param auth_list List with auth data, you can build from AmoAuthList
#' @include query_functions.R
#' @include unnest_functions.R
#' @export
#' @importFrom stats setNames
#' @importFrom httr GET
#' @importFrom httr content
#' @import dplyr
#' @return You'll get a groups.
#'
#' @references
#' Please \strong{READ} this:
#' \href{https://github.com/grkhr/amocrm/blob/master/md/AmoGroups.md}{Function documentation in Russian on GitHub}
#'
#' Also nice to read:
#' \href{https://www.amocrm.ru/developers/content/api/account}{AmoCRM official documentation}
#'
#' @examples
#' \dontrun{
#' groups <- AmoGroups(auth_list = auth_list)
#' }
AmoGroups <- function(email = NULL, apikey = NULL, domain = NULL, auth_list = NULL) {
if (!is.null(auth_list)) {
email <- auth_list$email
apikey <- auth_list$apikey
domain <- auth_list$domain
}
auth <- AmoAuth(email, apikey, domain, verbose=F)
if (auth != T) stop(auth)
answer <- GET(paste0("https://",domain,".amocrm.ru/api/v2/account?with=groups"))
dataRaw <- content(answer, "parsed", "application/json")
groups <- dataRaw$`_embedded`$groups
groups_df <- bind_rows(groups) %>% as.data.frame()
return(groups_df)
}
|
/R/AmoGroups.R
|
no_license
|
grkhr/amocrm
|
R
| false | false | 1,378 |
r
|
#' Groups
#'
#' Function to get groups of users.
#'
#' @param email Email
#' @param apikey Your api key from settings in interface
#' @param domain Your domain in AmoCRM (xxx in xxx.amocrm.ru)
#' @param auth_list List with auth data, you can build from AmoAuthList
#' @include query_functions.R
#' @include unnest_functions.R
#' @export
#' @importFrom stats setNames
#' @importFrom httr GET
#' @importFrom httr content
#' @import dplyr
#' @return You'll get a groups.
#'
#' @references
#' Please \strong{READ} this:
#' \href{https://github.com/grkhr/amocrm/blob/master/md/AmoGroups.md}{Function documentation in Russian on GitHub}
#'
#' Also nice to read:
#' \href{https://www.amocrm.ru/developers/content/api/account}{AmoCRM official documentation}
#'
#' @examples
#' \dontrun{
#' groups <- AmoGroups(auth_list = auth_list)
#' }
AmoGroups <- function(email = NULL, apikey = NULL, domain = NULL, auth_list = NULL) {
if (!is.null(auth_list)) {
email <- auth_list$email
apikey <- auth_list$apikey
domain <- auth_list$domain
}
auth <- AmoAuth(email, apikey, domain, verbose=F)
if (auth != T) stop(auth)
answer <- GET(paste0("https://",domain,".amocrm.ru/api/v2/account?with=groups"))
dataRaw <- content(answer, "parsed", "application/json")
groups <- dataRaw$`_embedded`$groups
groups_df <- bind_rows(groups) %>% as.data.frame()
return(groups_df)
}
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/auth_request.R
\name{auth_put_post_delete}
\alias{auth_put_post_delete}
\title{POST, PUT, or DELETE a resource with authenticated credentials.}
\usage{
auth_put_post_delete(method, url, encode = "multipart", body = as.list(NA))
}
\arguments{
\item{method}{a string indicating which HTTP method to use (post, put, or delete)}
\item{url}{The URL to be accessed via authenticated PUT}
\item{encode}{the type of encoding to use for the PUT body, defaults to 'multipart'}
\item{body}{a list of data to be included in the body of the PUT request}
}
\value{
the response object from the method
}
\description{
POST, PUT, or DELETE data to a URL using an HTTP request using authentication credentials
provided in a client certificate. Authenticated access depends on the suggested
PKIplus package. If the PKIplus package is not installed, then the request fails.
}
|
/dataone/man/auth_put_post_delete.Rd
|
permissive
|
KillEdision/rdataone
|
R
| false | false | 948 |
rd
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/auth_request.R
\name{auth_put_post_delete}
\alias{auth_put_post_delete}
\title{POST, PUT, or DELETE a resource with authenticated credentials.}
\usage{
auth_put_post_delete(method, url, encode = "multipart", body = as.list(NA))
}
\arguments{
\item{method}{a string indicating which HTTP method to use (post, put, or delete)}
\item{url}{The URL to be accessed via authenticated PUT}
\item{encode}{the type of encoding to use for the PUT body, defaults to 'multipart'}
\item{body}{a list of data to be included in the body of the PUT request}
}
\value{
the response object from the method
}
\description{
POST, PUT, or DELETE data to a URL using an HTTP request using authentication credentials
provided in a client certificate. Authenticated access depends on the suggested
PKIplus package. If the PKIplus package is not installed, then the request fails.
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read_msgf_data.R
\name{read_msgf_data}
\alias{read_msgf_data}
\title{Reading MSGF Results. Generic.}
\usage{
read_msgf_data(path_to_MSGF_results, suffix = NULL)
}
\arguments{
\item{path_to_MSGF_results}{(path string) to directory with MSGF results for all datasets}
}
\value{
(MSnID) MSnID object
}
\description{
Reading MSGF output from a single directory
}
\examples{
path_to_MSGF_results <- system.file("extdata/global/msgf_output", package = "PlexedPiperTestData")
msnid <- read_msgf_data(path_to_MSGF_results)
print(msnid)
head(MSnID::psms(msnid))
}
|
/man/read_msgf_data.Rd
|
no_license
|
vladpetyuk/PlexedPiper
|
R
| false | true | 633 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read_msgf_data.R
\name{read_msgf_data}
\alias{read_msgf_data}
\title{Reading MSGF Results. Generic.}
\usage{
read_msgf_data(path_to_MSGF_results, suffix = NULL)
}
\arguments{
\item{path_to_MSGF_results}{(path string) to directory with MSGF results for all datasets}
}
\value{
(MSnID) MSnID object
}
\description{
Reading MSGF output from a single directory
}
\examples{
path_to_MSGF_results <- system.file("extdata/global/msgf_output", package = "PlexedPiperTestData")
msnid <- read_msgf_data(path_to_MSGF_results)
print(msnid)
head(MSnID::psms(msnid))
}
|
# 3行以下で終わる分析の例：集計編|R言語ではじめるプログラミングとデータ分析
# 馬場真哉
# サンプルサイズの取得 --------------------------------------------------------------
# サンプルサイズの取得
sales_beef <- read.csv("2-9-1-sales-beef.csv")
nrow(sales_beef)
# 度数 ------------------------------------------------------------
# 2カテゴリの場合の度数
fish_chicken <- read.csv("2-9-5-fish-chicken.csv")
table(fish_chicken$sex)
table(fish_chicken$choice)
# memo：3カテゴリ以上の場合
favorite_food <- read.csv("2-9-6-favorite-food.csv")
table(favorite_food$sex)
table(favorite_food$favorite_food)
# 累積度数 -----------------------------------------------------------------
# 累積度数
favorite_food <- read.csv("2-9-6-favorite-food.csv")
freq <- table(favorite_food$favorite_food)
cumsum(freq)
# memo：関数を入れ子にする
# 以下のコードでも実行結果は変わらない
favorite_food <- read.csv("2-9-6-favorite-food.csv")
cumsum(table(favorite_food$favorite_food))
# memo：cumsum関数の補足
# 「１」が5つあるデータの累積値
cumsum(c(1, 1, 1, 1, 1))
# 「１から５の等差数列」の累積値
cumsum(c(1, 2, 3, 4, 5))
# 相対度数 --------------------------------------------------------------------
# 相対度数
favorite_food <- read.csv("2-9-6-favorite-food.csv")
freq <- table(favorite_food$favorite_food)
prop.table(freq)
# memo：prop.tableの補足
# prop.tableの例
prop.table(c(20, 30, 50))
# prop.tableを使わない方法
c(20, 30, 50) / sum(c(20, 30, 50))
# memo：累積相対度数
favorite_food <- read.csv("2-9-6-favorite-food.csv")
freq <- table(favorite_food$favorite_food)
prop <- prop.table(freq)
cumsum(prop)
# クロス集計 ----------------------------------------------------------
# 2カテゴリの場合のクロス集計
fish_chicken <- read.csv("2-9-5-fish-chicken.csv")
table(fish_chicken)
# memo：3カテゴリの場合
favorite_food <- read.csv("2-9-6-favorite-food.csv")
table(favorite_food)
# memo：prob.tableを使って合計値が1になるようにする
favorite_food <- read.csv("2-9-6-favorite-food.csv")
freq_cross <- table(favorite_food)
prop.table(freq_cross)
# クロス集計表⇔整然データの変換 ---------------------------------------------------------
# クロス集計表をデータフレームにする
favorite_food <- read.csv("2-9-6-favorite-food.csv")
freq_cross <- table(favorite_food)
as.data.frame(freq_cross)
# memo：データフレームをまたクロス集計表に変換する
# クロス集計(xtabs)
freq_df <- as.data.frame(freq_cross)
xtabs(
formula = Freq ~ sex + favorite_food,
data = freq_df
)
# 合計値 ---------------------------------------------------------------------
# 合計値
sales_beef <- read.csv("2-9-1-sales-beef.csv")
sum(sales_beef$beef)
# memo：2列以上あるデータに対する合計値の計算
sales_population <- read.csv("2-9-3-sales-population.csv")
sum(sales_population$beef)
sum(sales_population$resident_population)
# 平均値 ---------------------------------------------------------------------
# 平均値
sales_beef <- read.csv("2-9-1-sales-beef.csv")
mean(sales_beef$beef)
# memo：mean関数を使わないで平均値を計算する
sum_sales <- sum(sales_beef$beef)
sample_size <- nrow(sales_beef)
sum_sales / sample_size
# 不偏分散 --------------------------------------------------------------------
# 不偏分散
sales_beef <- read.csv("2-9-1-sales-beef.csv")
var(sales_beef$beef)
# memo：var関数を使わないで不偏分散を計算する
mean_sales <- mean(sales_beef$beef)
sample_size <- nrow(sales_beef)
sum((sales_beef$beef - mean_sales) ^ 2) / (sample_size - 1)
# 標準偏差 --------------------------------------------------------------------
# 標準偏差
sales_beef <- read.csv("2-9-1-sales-beef.csv")
sd(sales_beef$beef)
# memo：sd関数を使わないで標準偏差を計算する
var_sales <- var(sales_beef$beef)
sqrt(var_sales)
# データの並び替え ----------------------------------------------------------------
# データの並び替え
sales_beef <- read.csv("2-9-1-sales-beef.csv")
sort(sales_beef$beef)
# memo：降順に並び替える
sort(sales_beef$beef, decreasing = TRUE)
# memo：順位の取得と並び替え
# 元のデータ
sales_beef$beef
# 順位(昇順)
order(sales_beef$beef)
# 並び替え
sales_beef$beef[order(sales_beef$beef)]
# 最小・最大値 ------------------------------------------------------------------
# 最小・最大値
sales_beef <- read.csv("2-9-1-sales-beef.csv")
min(sales_beef$beef)
max(sales_beef$beef)
# memo：データの範囲を取得する
range_sales <- range(sales_beef$beef)
range_sales
# 最小値の取得
range_sales[1]
# memo：minやmax関数を使わないで、最小・最大を求める
sort(sales_beef$beef)[1]
sort(sales_beef$beef)[nrow(sales_beef)]
# 中央値 ---------------------------------------------------------------------
# 中央値
sales_beef <- read.csv("2-9-1-sales-beef.csv")
median(sales_beef$beef)
# memo：中央値の計算例
median(1:5)
median(1:4)
(30.9 + 31.6) / 2
# sort関数を使って中央値を求める
n <- nrow(sales_beef)
sorted <- sort(sales_beef$beef)
(sorted[n / 2] + sorted[(n / 2) + 1]) / 2
# パーセント点 ------------------------------------------------------------------
# パーセント点(分位点)
sales_beef <- read.csv("2-9-1-sales-beef.csv")
quantile(sales_beef$beef, probs = c(0.25, 0.75))
# memo：quantile関数を使って中央値を求める
quantile(sales_beef$beef, probs = 0.5)
# memo：パーセント点の計算例
quantile(0:100, probs = c(0.25, 0.5, 0.75))
quantile(0:10, probs = c(0.25, 0.5, 0.75))
# 四分位範囲 -------------------------------------------------------------------
# 四分位範囲
sales_beef <- read.csv("2-9-1-sales-beef.csv")
IQR(sales_beef$beef)
# memo：IQR関数を使わないで四分位範囲を求める
q1 <- quantile(sales_beef$beef, probs = 0.25)
q3 <- quantile(sales_beef$beef, probs = 0.75)
q3 - q1
# 要約統計量 -------------------------------------------------------------------
# 要約統計量
sales_beef <- read.csv("2-9-1-sales-beef.csv")
summary(sales_beef$beef)
# memo：列名を指定しない場合
summary(sales_beef)
# memo：2列以上のデータに対して適用した場合
sales_meat <- read.csv("2-9-4-sales-meat.csv")
summary(sales_meat)
# 行や列に対する一括処理(apply) ---------------------------------------------------------------
# 列に対する一括処理
sales_population <- read.csv("2-9-3-sales-population.csv")
apply(sales_population, 2, sum)
# memo：行に対する一括処理
sales_population <- read.csv("2-9-3-sales-population.csv")
apply(sales_population, 1, sum)
# リストに対する一括処理(lapply) -----------------------------------------------------
# リストを入力、リストを出力
sales_population <- read.csv("2-9-3-sales-population.csv")
lapply(sales_population, sum)
# リストに対する一括処理(sapply) -----------------------------------------------------
# リストを入力、行列を出力
sales_population <- read.csv("2-9-3-sales-population.csv")
sapply(sales_population, sum)
# カテゴリ別の集計値(tapply) --------------------------------------------------------------
# カテゴリ別の平均値
sales_meat <- read.csv("2-9-4-sales-meat.csv")
tapply(sales_meat$sales, sales_meat$category, mean)
# memo：カテゴリ別の要約統計量
tapply(sales_meat$sales, sales_meat$category, summary)
# 2つ以上のカテゴリ別に分けた集計値(tapply) -------------------------------------------------------
# ウールの種類別、糸の張力別の切断数合計値を得る
tapply(warpbreaks$breaks,
list(warpbreaks$wool, warpbreaks$tension),
mean)
|
/book-data/2-9-analysis-example-aggregation.R
|
no_license
|
katzkawai/book-r-programming-intro
|
R
| false | false | 7,975 |
r
|
# 3行以下で終わる分析の例：集計編|R言語ではじめるプログラミングとデータ分析
# 馬場真哉
# サンプルサイズの取得 --------------------------------------------------------------
# サンプルサイズの取得
sales_beef <- read.csv("2-9-1-sales-beef.csv")
nrow(sales_beef)
# 度数 ------------------------------------------------------------
# 2カテゴリの場合の度数
fish_chicken <- read.csv("2-9-5-fish-chicken.csv")
table(fish_chicken$sex)
table(fish_chicken$choice)
# memo：3カテゴリ以上の場合
favorite_food <- read.csv("2-9-6-favorite-food.csv")
table(favorite_food$sex)
table(favorite_food$favorite_food)
# 累積度数 -----------------------------------------------------------------
# 累積度数
favorite_food <- read.csv("2-9-6-favorite-food.csv")
freq <- table(favorite_food$favorite_food)
cumsum(freq)
# memo：関数を入れ子にする
# 以下のコードでも実行結果は変わらない
favorite_food <- read.csv("2-9-6-favorite-food.csv")
cumsum(table(favorite_food$favorite_food))
# memo：cumsum関数の補足
# 「１」が5つあるデータの累積値
cumsum(c(1, 1, 1, 1, 1))
# 「１から５の等差数列」の累積値
cumsum(c(1, 2, 3, 4, 5))
# 相対度数 --------------------------------------------------------------------
# 相対度数
favorite_food <- read.csv("2-9-6-favorite-food.csv")
freq <- table(favorite_food$favorite_food)
prop.table(freq)
# memo：prop.tableの補足
# prop.tableの例
prop.table(c(20, 30, 50))
# prop.tableを使わない方法
c(20, 30, 50) / sum(c(20, 30, 50))
# memo：累積相対度数
favorite_food <- read.csv("2-9-6-favorite-food.csv")
freq <- table(favorite_food$favorite_food)
prop <- prop.table(freq)
cumsum(prop)
# クロス集計 ----------------------------------------------------------
# 2カテゴリの場合のクロス集計
fish_chicken <- read.csv("2-9-5-fish-chicken.csv")
table(fish_chicken)
# memo：3カテゴリの場合
favorite_food <- read.csv("2-9-6-favorite-food.csv")
table(favorite_food)
# memo：prob.tableを使って合計値が1になるようにする
favorite_food <- read.csv("2-9-6-favorite-food.csv")
freq_cross <- table(favorite_food)
prop.table(freq_cross)
# クロス集計表⇔整然データの変換 ---------------------------------------------------------
# クロス集計表をデータフレームにする
favorite_food <- read.csv("2-9-6-favorite-food.csv")
freq_cross <- table(favorite_food)
as.data.frame(freq_cross)
# memo：データフレームをまたクロス集計表に変換する
# クロス集計(xtabs)
freq_df <- as.data.frame(freq_cross)
xtabs(
formula = Freq ~ sex + favorite_food,
data = freq_df
)
# 合計値 ---------------------------------------------------------------------
# 合計値
sales_beef <- read.csv("2-9-1-sales-beef.csv")
sum(sales_beef$beef)
# memo：2列以上あるデータに対する合計値の計算
sales_population <- read.csv("2-9-3-sales-population.csv")
sum(sales_population$beef)
sum(sales_population$resident_population)
# 平均値 ---------------------------------------------------------------------
# 平均値
sales_beef <- read.csv("2-9-1-sales-beef.csv")
mean(sales_beef$beef)
# memo：mean関数を使わないで平均値を計算する
sum_sales <- sum(sales_beef$beef)
sample_size <- nrow(sales_beef)
sum_sales / sample_size
# 不偏分散 --------------------------------------------------------------------
# 不偏分散
sales_beef <- read.csv("2-9-1-sales-beef.csv")
var(sales_beef$beef)
# memo：var関数を使わないで不偏分散を計算する
mean_sales <- mean(sales_beef$beef)
sample_size <- nrow(sales_beef)
sum((sales_beef$beef - mean_sales) ^ 2) / (sample_size - 1)
# 標準偏差 --------------------------------------------------------------------
# 標準偏差
sales_beef <- read.csv("2-9-1-sales-beef.csv")
sd(sales_beef$beef)
# memo：sd関数を使わないで標準偏差を計算する
var_sales <- var(sales_beef$beef)
sqrt(var_sales)
# データの並び替え ----------------------------------------------------------------
# データの並び替え
sales_beef <- read.csv("2-9-1-sales-beef.csv")
sort(sales_beef$beef)
# memo：降順に並び替える
sort(sales_beef$beef, decreasing = TRUE)
# memo：順位の取得と並び替え
# 元のデータ
sales_beef$beef
# 順位(昇順)
order(sales_beef$beef)
# 並び替え
sales_beef$beef[order(sales_beef$beef)]
# 最小・最大値 ------------------------------------------------------------------
# 最小・最大値
sales_beef <- read.csv("2-9-1-sales-beef.csv")
min(sales_beef$beef)
max(sales_beef$beef)
# memo：データの範囲を取得する
range_sales <- range(sales_beef$beef)
range_sales
# 最小値の取得
range_sales[1]
# memo：minやmax関数を使わないで、最小・最大を求める
sort(sales_beef$beef)[1]
sort(sales_beef$beef)[nrow(sales_beef)]
# 中央値 ---------------------------------------------------------------------
# 中央値
sales_beef <- read.csv("2-9-1-sales-beef.csv")
median(sales_beef$beef)
# memo：中央値の計算例
median(1:5)
median(1:4)
(30.9 + 31.6) / 2
# sort関数を使って中央値を求める
n <- nrow(sales_beef)
sorted <- sort(sales_beef$beef)
(sorted[n / 2] + sorted[(n / 2) + 1]) / 2
# パーセント点 ------------------------------------------------------------------
# パーセント点(分位点)
sales_beef <- read.csv("2-9-1-sales-beef.csv")
quantile(sales_beef$beef, probs = c(0.25, 0.75))
# memo：quantile関数を使って中央値を求める
quantile(sales_beef$beef, probs = 0.5)
# memo：パーセント点の計算例
quantile(0:100, probs = c(0.25, 0.5, 0.75))
quantile(0:10, probs = c(0.25, 0.5, 0.75))
# 四分位範囲 -------------------------------------------------------------------
# 四分位範囲
sales_beef <- read.csv("2-9-1-sales-beef.csv")
IQR(sales_beef$beef)
# memo：IQR関数を使わないで四分位範囲を求める
q1 <- quantile(sales_beef$beef, probs = 0.25)
q3 <- quantile(sales_beef$beef, probs = 0.75)
q3 - q1
# 要約統計量 -------------------------------------------------------------------
# 要約統計量
sales_beef <- read.csv("2-9-1-sales-beef.csv")
summary(sales_beef$beef)
# memo：列名を指定しない場合
summary(sales_beef)
# memo：2列以上のデータに対して適用した場合
sales_meat <- read.csv("2-9-4-sales-meat.csv")
summary(sales_meat)
# 行や列に対する一括処理(apply) ---------------------------------------------------------------
# 列に対する一括処理
sales_population <- read.csv("2-9-3-sales-population.csv")
apply(sales_population, 2, sum)
# memo：行に対する一括処理
sales_population <- read.csv("2-9-3-sales-population.csv")
apply(sales_population, 1, sum)
# リストに対する一括処理(lapply) -----------------------------------------------------
# リストを入力、リストを出力
sales_population <- read.csv("2-9-3-sales-population.csv")
lapply(sales_population, sum)
# リストに対する一括処理(sapply) -----------------------------------------------------
# リストを入力、行列を出力
sales_population <- read.csv("2-9-3-sales-population.csv")
sapply(sales_population, sum)
# カテゴリ別の集計値(tapply) --------------------------------------------------------------
# カテゴリ別の平均値
sales_meat <- read.csv("2-9-4-sales-meat.csv")
tapply(sales_meat$sales, sales_meat$category, mean)
# memo：カテゴリ別の要約統計量
tapply(sales_meat$sales, sales_meat$category, summary)
# 2つ以上のカテゴリ別に分けた集計値(tapply) -------------------------------------------------------
# ウールの種類別、糸の張力別の切断数合計値を得る
tapply(warpbreaks$breaks,
list(warpbreaks$wool, warpbreaks$tension),
mean)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/animate_annual.R
\name{animate_annual}
\alias{animate_annual}
\title{Plot an animation of forest change within a given area of interest (AOI)}
\usage{
animate_annual(aoi, gfc_stack, out_dir = getwd(),
out_basename = "gfc_animation", site_name = "", type = "html",
height = 3, width = 3, dpi = 300)
}
\arguments{
\item{aoi}{one or more AOI polygons as a \code{SpatialPolygonsDataFrame}
object. If there is a 'label' field in the dataframe, it will be used to
label the polygons in the plots. If the AOI is not in the WGS84 geographic
coordinate system, it will be reprojected to WGS84.}
\item{gfc_stack}{a GFC product subset as a
\code{RasterStack} (as output by \code{\link{annual_stack}})}
\item{out_dir}{folder for animation output}
\item{out_basename}{basename to use when naming animation files}
\item{site_name}{name of the site (used in making title)}
\item{type}{type of animation to make. Can be either "gif" or "html"}
\item{height}{desired height of the animation GIF in inches}
\item{width}{desired width of the animation GIF in inches}
\item{dpi}{dots per inch for the output image}
}
\description{
Produces an animation of annual forest change in the area bounded by the
extent of a given AOI, or AOIs. The AOI polygon(s) is(are) also plotted on
the image. The \code{gfc_stack} must be pre-calculated using the
\code{\link{annual_stack}} function. The animation can be either an animated
GIF (if \code{type} is set to 'gif') or a series of '.png' files with a
corresponding '.html' webpage showing a simple viewer and the forest change
animation (if \code{type} is set to 'html'). The HTML option is recommended
as it requires no additional software to produce it. The animated GIF option
will only work if the imagemagicK software package is installed beforehand
(this is done outside of R).
}
\seealso{
\code{\link{annual_stack}}
}
|
/man/animate_annual.Rd
|
no_license
|
yfinegold/gfcanalysis
|
R
| false | true | 1,953 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/animate_annual.R
\name{animate_annual}
\alias{animate_annual}
\title{Plot an animation of forest change within a given area of interest (AOI)}
\usage{
animate_annual(aoi, gfc_stack, out_dir = getwd(),
out_basename = "gfc_animation", site_name = "", type = "html",
height = 3, width = 3, dpi = 300)
}
\arguments{
\item{aoi}{one or more AOI polygons as a \code{SpatialPolygonsDataFrame}
object. If there is a 'label' field in the dataframe, it will be used to
label the polygons in the plots. If the AOI is not in the WGS84 geographic
coordinate system, it will be reprojected to WGS84.}
\item{gfc_stack}{a GFC product subset as a
\code{RasterStack} (as output by \code{\link{annual_stack}})}
\item{out_dir}{folder for animation output}
\item{out_basename}{basename to use when naming animation files}
\item{site_name}{name of the site (used in making title)}
\item{type}{type of animation to make. Can be either "gif" or "html"}
\item{height}{desired height of the animation GIF in inches}
\item{width}{desired width of the animation GIF in inches}
\item{dpi}{dots per inch for the output image}
}
\description{
Produces an animation of annual forest change in the area bounded by the
extent of a given AOI, or AOIs. The AOI polygon(s) is(are) also plotted on
the image. The \code{gfc_stack} must be pre-calculated using the
\code{\link{annual_stack}} function. The animation can be either an animated
GIF (if \code{type} is set to 'gif') or a series of '.png' files with a
corresponding '.html' webpage showing a simple viewer and the forest change
animation (if \code{type} is set to 'html'). The HTML option is recommended
as it requires no additional software to produce it. The animated GIF option
will only work if the imagemagicK software package is installed beforehand
(this is done outside of R).
}
\seealso{
\code{\link{annual_stack}}
}
|
load_dataset <- function(fname)
{
read.csv(fname,header = TRUE,stringsAsFactors = FALSE)
}
|
/load_dataset.R
|
no_license
|
JAIR-VG/dissreg-tools
|
R
| false | false | 98 |
r
|
load_dataset <- function(fname)
{
read.csv(fname,header = TRUE,stringsAsFactors = FALSE)
}
|
# Table of VaR estimates for historical and simulated data
H2_VaR_table <- function(){
# Create matrix with the relative VaR estimates then name rows and cols
H2_VaR <- matrix(c(H2_VaR_1W_sim, H2_VaR_1M_sim, H2_VaR_2M_sim, H2_VaR_1W_hist, H2_VaR_1M_hist, H2_VaR_2M_hist, abs(H2_VaR_1W_hist - H2_VaR_1W_sim), abs(H2_VaR_1M_hist - H2_VaR_1M_sim), abs(H2_VaR_2M_hist - H2_VaR_2M_sim) ), nrow=3, ncol=3,byrow=TRUE)
H2_VaR <- H2_VaR %>% as.data.frame()
colnames(H2_VaR) <- c("One-Week", "One-Month", "Two-Months")
rownames(H2_VaR) <- c("FHS","Historical", "Difference")
# convert matrix into table
xtable(H2_VaR, caption = NULL, label = NULL, align = NULL, digits = 5,
display = NULL, auto = FALSE)
}
|
/Final Project/code/H2_VaR_table.R
|
no_license
|
Richard19leigh93/Financial-Econometrics
|
R
| false | false | 738 |
r
|
# Table of VaR estimates for historical and simulated data
H2_VaR_table <- function(){
# Create matrix with the relative VaR estimates then name rows and cols
H2_VaR <- matrix(c(H2_VaR_1W_sim, H2_VaR_1M_sim, H2_VaR_2M_sim, H2_VaR_1W_hist, H2_VaR_1M_hist, H2_VaR_2M_hist, abs(H2_VaR_1W_hist - H2_VaR_1W_sim), abs(H2_VaR_1M_hist - H2_VaR_1M_sim), abs(H2_VaR_2M_hist - H2_VaR_2M_sim) ), nrow=3, ncol=3,byrow=TRUE)
H2_VaR <- H2_VaR %>% as.data.frame()
colnames(H2_VaR) <- c("One-Week", "One-Month", "Two-Months")
rownames(H2_VaR) <- c("FHS","Historical", "Difference")
# convert matrix into table
xtable(H2_VaR, caption = NULL, label = NULL, align = NULL, digits = 5,
display = NULL, auto = FALSE)
}
|
/多元统计分析/综合评价.r
|
no_license
|
tiny-boat/my-R-code
|
R
| false | false | 2,060 |
r
| ||
#################Dotplots for SNPS###################
#takes 2 columns: gene and position.
Pathway_histo_pi3k_Akt <- read.csv(file = "Pathway_histo_pi3k-Akt.csv")
e <- ggplot(Pathway_histo_pi3k_Akt, aes(x = Gene, y = Distance, fill = Gene))
#genes
e + geom_dotplot(binaxis = "y", stackdir = "center", aes(color = Gene), method = "histodot", dotsize = 0.5, stackratio = 0.9, show.legend = FALSE) +
# theme_minimal() +
theme_clean() +
theme(axis.text.y = element_text(face = "italic"), axis.text=element_text(size=15, colour = "#595959"), axis.title=element_text(size=16, face = "bold", , colour = "#595959" )) +
labs(y = "Distance from gene", x = "Gene", title = "PI3K-AKT") +
coord_flip() +
ylim(-100000, 100000)
#GWAS
e + geom_dotplot(binaxis = "y", stackdir = "centerwhole", aes(color = GWAS), fill = "white", method = "histodot", dotsize = 0.15, stackratio = 1, show.legend = FALSE) +
# theme_minimal() +
theme_clean() +
theme(axis.text.y = element_text(face = "italic")) +
labs(y = "Distance from gene", x = "GWAS", title = "SNP Distribution") +
coord_flip() +
ylim(-100000, 100000)
#violin genes
e + geom_violin(trim = FALSE) +
geom_dotplot(binaxis = "y", stackdir ="center", dotsize = 0.5, show.legend = FALSE) +
stat_summary(fun.data = "mean_sdl", fun.args = list(mult = 1), geom = "pointrange", color = "red", shape = 11) +
labs(y = "Distance from gene", x = "Gene", title = "PI3K-AKT") +
theme(axis.text.x = element_text(face = "italic")) +
ylim(-100000, 100000)
#HOFR
e <- ggplot(HOFR_dot, aes(x = GWAS, y = HOFR_dot$`Distance from Gene`))
e + geom_dotplot(binaxis = "y", stackdir = "centerwhole", color = "#B93442", fill = "#B93442", method = "histodot", dotsize = 0.3, stackratio = 1, show.legend = FALSE) +
# theme_minimal() +
theme_clean() +
theme(axis.text.y = element_text(face = "italic")) +
labs(y = "Distance from gene", x = "GWAS", title = "SNP Distribution") +
coord_flip() +
ylim(-100000, 100000)
#CH
e <- ggplot(CH_dot, aes(x = GWAS, y = CH_dot$`Distance from Gene`))
e + geom_dotplot(binaxis = "y", stackdir = "centerwhole", color = "#3453B9", fill = "#3453B9", method = "histodot", dotsize = 0.3, stackratio = 1, show.legend = FALSE) +
# theme_minimal() +
theme_clean() +
theme(axis.text.y = element_text(face = "italic")) +
labs(y = "Distance from gene", x = "GWAS", title = "SNP Distribution") +
coord_flip() +
ylim(-100000, 100000)
#LM
e <- ggplot(LM_dot, aes(x = GWAS, y = LM_dot$`Distance from Gene`))
e + geom_dotplot(binaxis = "y", stackdir = "centerwhole", color = "#3BAF43", fill = "#3BAF43", method = "histodot", dotsize = 0.3, stackratio = 1, show.legend = FALSE) +
# theme_minimal() +
theme_clean() +
theme(axis.text.y = element_text(face = "italic")) +
labs(y = "Distance from gene", x = "GWAS", title = "SNP Distribution") +
coord_flip() +
ylim(-100000, 100000)
|
/R_scripts/PLOT_Dot_plot_SNPS.R
|
no_license
|
ThomasHall1688/Bovine_multi-omic_integration
|
R
| false | false | 2,858 |
r
|
#################Dotplots for SNPS###################
#takes 2 columns: gene and position.
Pathway_histo_pi3k_Akt <- read.csv(file = "Pathway_histo_pi3k-Akt.csv")
e <- ggplot(Pathway_histo_pi3k_Akt, aes(x = Gene, y = Distance, fill = Gene))
#genes
e + geom_dotplot(binaxis = "y", stackdir = "center", aes(color = Gene), method = "histodot", dotsize = 0.5, stackratio = 0.9, show.legend = FALSE) +
# theme_minimal() +
theme_clean() +
theme(axis.text.y = element_text(face = "italic"), axis.text=element_text(size=15, colour = "#595959"), axis.title=element_text(size=16, face = "bold", , colour = "#595959" )) +
labs(y = "Distance from gene", x = "Gene", title = "PI3K-AKT") +
coord_flip() +
ylim(-100000, 100000)
#GWAS
e + geom_dotplot(binaxis = "y", stackdir = "centerwhole", aes(color = GWAS), fill = "white", method = "histodot", dotsize = 0.15, stackratio = 1, show.legend = FALSE) +
# theme_minimal() +
theme_clean() +
theme(axis.text.y = element_text(face = "italic")) +
labs(y = "Distance from gene", x = "GWAS", title = "SNP Distribution") +
coord_flip() +
ylim(-100000, 100000)
#violin genes
e + geom_violin(trim = FALSE) +
geom_dotplot(binaxis = "y", stackdir ="center", dotsize = 0.5, show.legend = FALSE) +
stat_summary(fun.data = "mean_sdl", fun.args = list(mult = 1), geom = "pointrange", color = "red", shape = 11) +
labs(y = "Distance from gene", x = "Gene", title = "PI3K-AKT") +
theme(axis.text.x = element_text(face = "italic")) +
ylim(-100000, 100000)
#HOFR
e <- ggplot(HOFR_dot, aes(x = GWAS, y = HOFR_dot$`Distance from Gene`))
e + geom_dotplot(binaxis = "y", stackdir = "centerwhole", color = "#B93442", fill = "#B93442", method = "histodot", dotsize = 0.3, stackratio = 1, show.legend = FALSE) +
# theme_minimal() +
theme_clean() +
theme(axis.text.y = element_text(face = "italic")) +
labs(y = "Distance from gene", x = "GWAS", title = "SNP Distribution") +
coord_flip() +
ylim(-100000, 100000)
#CH
e <- ggplot(CH_dot, aes(x = GWAS, y = CH_dot$`Distance from Gene`))
e + geom_dotplot(binaxis = "y", stackdir = "centerwhole", color = "#3453B9", fill = "#3453B9", method = "histodot", dotsize = 0.3, stackratio = 1, show.legend = FALSE) +
# theme_minimal() +
theme_clean() +
theme(axis.text.y = element_text(face = "italic")) +
labs(y = "Distance from gene", x = "GWAS", title = "SNP Distribution") +
coord_flip() +
ylim(-100000, 100000)
#LM
e <- ggplot(LM_dot, aes(x = GWAS, y = LM_dot$`Distance from Gene`))
e + geom_dotplot(binaxis = "y", stackdir = "centerwhole", color = "#3BAF43", fill = "#3BAF43", method = "histodot", dotsize = 0.3, stackratio = 1, show.legend = FALSE) +
# theme_minimal() +
theme_clean() +
theme(axis.text.y = element_text(face = "italic")) +
labs(y = "Distance from gene", x = "GWAS", title = "SNP Distribution") +
coord_flip() +
ylim(-100000, 100000)
|
# Text analysis of Trump's tweets confirms he writes only the (angrier) Android half
# By David Robinson at http://varianceexplained.org/r/trump-tweets/
# Code: https://github.com/dgrtwo/dgrtwo.github.com/blob/master/_R/2016-08-09-trump-tweets.Rmd
library(dplyr)
library(purrr)
library(twitteR)
library(tidyr)
library(lubridate)
library(scales)
#library(magrittr)
library(stringr)
library(tidytext)
library(broom)
library(ggplot2)
# You'd need to set global options with an authenticated app
#setup_twitter_oauth(getOption("twitter_consumer_key"),
#getOption("twitter_consumer_secret"),
#getOption("twitter_access_token"),
#getOption("twitter_access_token_secret"))
# We can request only 3200 tweets at a time; it will return fewer
# depending on the API
#trump_tweets <- userTimeline("realDonaldTrump", n = 3200)
#trump_tweets_df <- tbl_df(map_df(trump_tweets, as.data.frame))
load(url("http://varianceexplained.org/files/trump_tweets_df.rda"))
tweets <- trump_tweets_df %>%
select(id, statusSource, text, created) %>%
tidyr::extract(statusSource, "source", "Twitter for (.*?)<") %>%
filter(source %in% c("iPhone", "Android"))
# Hour of day
tweets %>%
count(source, hour = hour(with_tz(created, "EST"))) %>%
mutate(percent = n / sum(n)) %>%
ggplot(aes(hour, percent, color = source)) +
geom_line() +
scale_y_continuous(labels = percent_format()) +
labs(x = "Hour of day (EST)",
y = "% of tweets",
color = "")
# tweets start with "
tweets %>%
count(source,
quoted = ifelse(str_detect(text, '^"'), "Quoted", "Not quoted")) %>%
ggplot(aes(source, n, fill = quoted)) +
geom_bar(stat = "identity", position = "dodge") +
labs(x = "", y = "Number of tweets", fill = "") +
ggtitle('Whether tweets start with a quotation mark (")')
# Picture/link
tweet_picture_counts <- tweets %>%
filter(!str_detect(text, '^"')) %>%
count(source,
picture = ifelse(str_detect(text, "t.co"),
"Picture/link", "No picture/link"))
ggplot(tweet_picture_counts, aes(source, n, fill = picture)) +
geom_bar(stat = "identity", position = "dodge") +
labs(x = "", y = "Number of tweets", fill = "")
spr <- tweet_picture_counts %>%
spread(source, n) %>%
mutate_each(funs(. / sum(.)), Android, iPhone)
rr <- spr$iPhone[2] / spr$Android[2]
# comparison of words
reg <- "([^A-Za-z\\d#@']|'(?![A-Za-z\\d#@]))"
tweet_words <- tweets %>%
filter(!str_detect(text, '^"')) %>%
mutate(text = str_replace_all(text, "https://t.co/[A-Za-z\\d]+|&", "")) %>%
unnest_tokens(word, text, token = "regex", pattern = reg) %>%
filter(!word %in% stop_words$word,
str_detect(word, "[a-z]"))
tweet_words
tweet_words %>%
count(word, sort = TRUE) %>%
head(20) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n)) +
geom_bar(stat = "identity") +
ylab("Occurrences") +
coord_flip()
# phone ratio
android_iphone_ratios <- tweet_words %>%
count(word, source) %>%
filter(sum(n) >= 5) %>%
spread(source, n, fill = 0) %>%
ungroup() %>%
mutate_each(funs((. + 1) / sum(. + 1)), -word) %>%
mutate(logratio = log2(Android / iPhone)) %>%
arrange(desc(logratio))
android_iphone_ratios %>%
group_by(logratio > 0) %>%
top_n(15, abs(logratio)) %>%
ungroup() %>%
mutate(word = reorder(word, logratio)) %>%
ggplot(aes(word, logratio, fill = logratio < 0)) +
geom_bar(stat = "identity") +
coord_flip() +
ylab("Android / iPhone log ratio") +
scale_fill_manual(name = "", labels = c("Android", "iPhone"),
values = c("red", "lightblue"))
# sentiment
nrc <- sentiments %>%
filter(lexicon == "nrc") %>%
dplyr::select(word, sentiment)
nrc
# sentiment by phone
sources <- tweet_words %>%
group_by(source) %>%
mutate(total_words = n()) %>%
ungroup() %>%
distinct(id, source, total_words)
by_source_sentiment <- tweet_words %>%
inner_join(nrc, by = "word") %>%
count(sentiment, id) %>%
ungroup() %>%
complete(sentiment, id, fill = list(n = 0)) %>%
inner_join(sources) %>%
group_by(source, sentiment, total_words) %>%
summarize(words = sum(n)) %>%
ungroup()
head(by_source_sentiment)
# We then want to measure how much more likely the Android account is to use an emotionally-charged term relative to the iPhone account. Since this is count data, we can use a Poisson test to measure the difference:
sentiment_differences <- by_source_sentiment %>%
group_by(sentiment) %>%
do(tidy(poisson.test(.$words, .$total_words)))
sentiment_differences
sentiment_differences %>%
ungroup() %>%
mutate(sentiment = reorder(sentiment, estimate)) %>%
mutate_each(funs(. - 1), estimate, conf.low, conf.high) %>%
ggplot(aes(estimate, sentiment)) +
geom_point() +
geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
scale_x_continuous(labels = percent_format()) +
labs(x = "% increase in Android relative to iPhone",
y = "Sentiment")
# We're especially interested in which words drove this different in sentiment. Let's consider the words with the largest changes within each category:
android_iphone_ratios %>%
inner_join(nrc, by = "word") %>%
filter(!sentiment %in% c("positive", "negative")) %>%
mutate(sentiment = reorder(sentiment, -logratio),
word = reorder(word, -logratio)) %>%
group_by(sentiment) %>%
top_n(10, abs(logratio)) %>%
ungroup() %>%
ggplot(aes(word, logratio, fill = logratio < 0)) +
facet_wrap(~ sentiment, scales = "free", nrow = 2) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(x = "", y = "Android / iPhone log ratio") +
scale_fill_manual(name = "", labels = c("Android", "iPhone"),
values = c("red", "lightblue"))
|
/has-new-digital-research-methods/1-donald-trump-tweet-analysis.r
|
no_license
|
peterdalle/presentations
|
R
| false | false | 5,772 |
r
|
# Text analysis of Trump's tweets confirms he writes only the (angrier) Android half
# By David Robinson at http://varianceexplained.org/r/trump-tweets/
# Code: https://github.com/dgrtwo/dgrtwo.github.com/blob/master/_R/2016-08-09-trump-tweets.Rmd
library(dplyr)
library(purrr)
library(twitteR)
library(tidyr)
library(lubridate)
library(scales)
#library(magrittr)
library(stringr)
library(tidytext)
library(broom)
library(ggplot2)
# You'd need to set global options with an authenticated app
#setup_twitter_oauth(getOption("twitter_consumer_key"),
#getOption("twitter_consumer_secret"),
#getOption("twitter_access_token"),
#getOption("twitter_access_token_secret"))
# We can request only 3200 tweets at a time; it will return fewer
# depending on the API
#trump_tweets <- userTimeline("realDonaldTrump", n = 3200)
#trump_tweets_df <- tbl_df(map_df(trump_tweets, as.data.frame))
load(url("http://varianceexplained.org/files/trump_tweets_df.rda"))
tweets <- trump_tweets_df %>%
select(id, statusSource, text, created) %>%
tidyr::extract(statusSource, "source", "Twitter for (.*?)<") %>%
filter(source %in% c("iPhone", "Android"))
# Hour of day
tweets %>%
count(source, hour = hour(with_tz(created, "EST"))) %>%
mutate(percent = n / sum(n)) %>%
ggplot(aes(hour, percent, color = source)) +
geom_line() +
scale_y_continuous(labels = percent_format()) +
labs(x = "Hour of day (EST)",
y = "% of tweets",
color = "")
# tweets start with "
tweets %>%
count(source,
quoted = ifelse(str_detect(text, '^"'), "Quoted", "Not quoted")) %>%
ggplot(aes(source, n, fill = quoted)) +
geom_bar(stat = "identity", position = "dodge") +
labs(x = "", y = "Number of tweets", fill = "") +
ggtitle('Whether tweets start with a quotation mark (")')
# Picture/link
tweet_picture_counts <- tweets %>%
filter(!str_detect(text, '^"')) %>%
count(source,
picture = ifelse(str_detect(text, "t.co"),
"Picture/link", "No picture/link"))
ggplot(tweet_picture_counts, aes(source, n, fill = picture)) +
geom_bar(stat = "identity", position = "dodge") +
labs(x = "", y = "Number of tweets", fill = "")
spr <- tweet_picture_counts %>%
spread(source, n) %>%
mutate_each(funs(. / sum(.)), Android, iPhone)
rr <- spr$iPhone[2] / spr$Android[2]
# comparison of words
reg <- "([^A-Za-z\\d#@']|'(?![A-Za-z\\d#@]))"
tweet_words <- tweets %>%
filter(!str_detect(text, '^"')) %>%
mutate(text = str_replace_all(text, "https://t.co/[A-Za-z\\d]+|&", "")) %>%
unnest_tokens(word, text, token = "regex", pattern = reg) %>%
filter(!word %in% stop_words$word,
str_detect(word, "[a-z]"))
tweet_words
tweet_words %>%
count(word, sort = TRUE) %>%
head(20) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n)) +
geom_bar(stat = "identity") +
ylab("Occurrences") +
coord_flip()
# phone ratio
android_iphone_ratios <- tweet_words %>%
count(word, source) %>%
filter(sum(n) >= 5) %>%
spread(source, n, fill = 0) %>%
ungroup() %>%
mutate_each(funs((. + 1) / sum(. + 1)), -word) %>%
mutate(logratio = log2(Android / iPhone)) %>%
arrange(desc(logratio))
android_iphone_ratios %>%
group_by(logratio > 0) %>%
top_n(15, abs(logratio)) %>%
ungroup() %>%
mutate(word = reorder(word, logratio)) %>%
ggplot(aes(word, logratio, fill = logratio < 0)) +
geom_bar(stat = "identity") +
coord_flip() +
ylab("Android / iPhone log ratio") +
scale_fill_manual(name = "", labels = c("Android", "iPhone"),
values = c("red", "lightblue"))
# sentiment
nrc <- sentiments %>%
filter(lexicon == "nrc") %>%
dplyr::select(word, sentiment)
nrc
# sentiment by phone
sources <- tweet_words %>%
group_by(source) %>%
mutate(total_words = n()) %>%
ungroup() %>%
distinct(id, source, total_words)
by_source_sentiment <- tweet_words %>%
inner_join(nrc, by = "word") %>%
count(sentiment, id) %>%
ungroup() %>%
complete(sentiment, id, fill = list(n = 0)) %>%
inner_join(sources) %>%
group_by(source, sentiment, total_words) %>%
summarize(words = sum(n)) %>%
ungroup()
head(by_source_sentiment)
# We then want to measure how much more likely the Android account is to use an emotionally-charged term relative to the iPhone account. Since this is count data, we can use a Poisson test to measure the difference:
sentiment_differences <- by_source_sentiment %>%
group_by(sentiment) %>%
do(tidy(poisson.test(.$words, .$total_words)))
sentiment_differences
sentiment_differences %>%
ungroup() %>%
mutate(sentiment = reorder(sentiment, estimate)) %>%
mutate_each(funs(. - 1), estimate, conf.low, conf.high) %>%
ggplot(aes(estimate, sentiment)) +
geom_point() +
geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
scale_x_continuous(labels = percent_format()) +
labs(x = "% increase in Android relative to iPhone",
y = "Sentiment")
# We're especially interested in which words drove this different in sentiment. Let's consider the words with the largest changes within each category:
android_iphone_ratios %>%
inner_join(nrc, by = "word") %>%
filter(!sentiment %in% c("positive", "negative")) %>%
mutate(sentiment = reorder(sentiment, -logratio),
word = reorder(word, -logratio)) %>%
group_by(sentiment) %>%
top_n(10, abs(logratio)) %>%
ungroup() %>%
ggplot(aes(word, logratio, fill = logratio < 0)) +
facet_wrap(~ sentiment, scales = "free", nrow = 2) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(x = "", y = "Android / iPhone log ratio") +
scale_fill_manual(name = "", labels = c("Android", "iPhone"),
values = c("red", "lightblue"))
|
#' Function to run CHILD from the R interface.
#'
#' The function executes a CHILD model run from the R console and creates a
#' S4-object \code{\link{CHILD}} with all output files. The function requires a
#' compiled version of CHILD and an appropriate input-file.
#'
#' Currently, this function is of preliminary stage. It requires a compiled
#' version of CHILD, present at a specified path or the current working
#' directory. Compiled versions for MS Windows 32 Bit and some Mac OS may be
#' downloaded from CSDMS (http://csdms.colorado.edu/wiki/Model:CHILD, scroll
#' down to download, fill the form fields, choose a recent release version and
#' you will find the compiled versions in ChildExercises/Executables_v10).
#' Existing output files will be overwritten without prompting, so be careful
#' and check before running.
#'
#' @param inputfile (character scalar) Name of the input file, with extention.
#'
#' @param outfiles (logical scalar) Argument to keep (\code{\link{TRUE}}) or
#' delete (\code{\link{FALSE}}) the original output files of the CHILD run,
#' default is \code{\link{FALSE}}.
#'
#' @param path (character scalar) Optional path to the compiled CHILD
#' executable. If not specified, it is assumed that the executable is present
#' in the current working directory.
#'
#' @return A S4-object with all CHILD run output files.
#'
#' @author Michael Dietze
#' @seealso \code{\link{read.CHILD}}, \code{\link{read.IN}},
#' \code{\link{write.IN}}
#' @references CSDMS website. http://csdms.colorado.edu/wiki/Model:CHILD.\cr
#' Tucker, GE. 2010. CHILD Users Guide for version R9.4.1.
#' http://csdms.colorado.edu/mediawiki/images/Child_users_guide.pdf \cr Tucker,
#' GE., Lancaster, ST., Gasparini, NM., Bras, RL. 2001. The Channel-Hillslope
#' Integrated Landscape Development (CHILD) Model. In Harmon, RS., Doe, W.W.
#' III (eds). Landscape Erosion and Evolution Modeling. Kluwer Academic/Plenum
#' Publishers, pp. 349-388.
#' @keywords CHILD
#' @examples
#'
#' # To run example code uncomment it (delete hashes)
#'
#' # You will need a compiled version of CHILD either in
#' # the working directory or under a known path.
#'
#' # hillslope1 <- run.CHILD(dataset = "hillslope1",
#' # inputfile = "hillslope1.in")
#'
#'
#' @export run.CHILD
run.CHILD <- function(
inputfile,
outfiles = FALSE,
path
) {
## read model output name from in-file
infilecontent <- readLines(inputfile)
modelname = as.character(infilecontent[grep("OUTFILENAME:",
infilecontent) + 1])
## set path to child executable
if(missing(path) == TRUE) path <- ""
## run CHILD via system command
system(paste(path,
"child ",
inputfile,
sep = "")) # run child
## read output files to result variable
result <- read.CHILD(modelname)
## optionally, remove CHILD output files from working directory
if(outfiles == FALSE) {
filestoremove <- list.files()[grepl(modelname,
list.files(),
fixed = TRUE)]
filestoremove <- filestoremove[filestoremove != inputfile]
file.remove(c(filestoremove, "run.time"))
}
return(result)
}
|
/R/run.CHILD.R
|
no_license
|
coffeemuggler/RCHILD
|
R
| false | false | 3,208 |
r
|
#' Function to run CHILD from the R interface.
#'
#' The function executes a CHILD model run from the R console and creates a
#' S4-object \code{\link{CHILD}} with all output files. The function requires a
#' compiled version of CHILD and an appropriate input-file.
#'
#' Currently, this function is of preliminary stage. It requires a compiled
#' version of CHILD, present at a specified path or the current working
#' directory. Compiled versions for MS Windows 32 Bit and some Mac OS may be
#' downloaded from CSDMS (http://csdms.colorado.edu/wiki/Model:CHILD, scroll
#' down to download, fill the form fields, choose a recent release version and
#' you will find the compiled versions in ChildExercises/Executables_v10).
#' Existing output files will be overwritten without prompting, so be careful
#' and check before running.
#'
#' @param inputfile (character scalar) Name of the input file, with extention.
#'
#' @param outfiles (logical scalar) Argument to keep (\code{\link{TRUE}}) or
#' delete (\code{\link{FALSE}}) the original output files of the CHILD run,
#' default is \code{\link{FALSE}}.
#'
#' @param path (character scalar) Optional path to the compiled CHILD
#' executable. If not specified, it is assumed that the executable is present
#' in the current working directory.
#'
#' @return A S4-object with all CHILD run output files.
#'
#' @author Michael Dietze
#' @seealso \code{\link{read.CHILD}}, \code{\link{read.IN}},
#' \code{\link{write.IN}}
#' @references CSDMS website. http://csdms.colorado.edu/wiki/Model:CHILD.\cr
#' Tucker, GE. 2010. CHILD Users Guide for version R9.4.1.
#' http://csdms.colorado.edu/mediawiki/images/Child_users_guide.pdf \cr Tucker,
#' GE., Lancaster, ST., Gasparini, NM., Bras, RL. 2001. The Channel-Hillslope
#' Integrated Landscape Development (CHILD) Model. In Harmon, RS., Doe, W.W.
#' III (eds). Landscape Erosion and Evolution Modeling. Kluwer Academic/Plenum
#' Publishers, pp. 349-388.
#' @keywords CHILD
#' @examples
#'
#' # To run example code uncomment it (delete hashes)
#'
#' # You will need a compiled version of CHILD either in
#' # the working directory or under a known path.
#'
#' # hillslope1 <- run.CHILD(dataset = "hillslope1",
#' # inputfile = "hillslope1.in")
#'
#'
#' @export run.CHILD
run.CHILD <- function(
inputfile,
outfiles = FALSE,
path
) {
## read model output name from in-file
infilecontent <- readLines(inputfile)
modelname = as.character(infilecontent[grep("OUTFILENAME:",
infilecontent) + 1])
## set path to child executable
if(missing(path) == TRUE) path <- ""
## run CHILD via system command
system(paste(path,
"child ",
inputfile,
sep = "")) # run child
## read output files to result variable
result <- read.CHILD(modelname)
## optionally, remove CHILD output files from working directory
if(outfiles == FALSE) {
filestoremove <- list.files()[grepl(modelname,
list.files(),
fixed = TRUE)]
filestoremove <- filestoremove[filestoremove != inputfile]
file.remove(c(filestoremove, "run.time"))
}
return(result)
}
|
testlist <- list(metric = 0L, vec = NULL, vec = NULL, w_vec = structure(0, .Dim = c(1L, 1L)), y_vec = structure(c(5.6445088053016e-312, 7.2911220195564e-304, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(7L, 10L)))
result <- do.call(UniIsoRegression:::reg_2d,testlist)
str(result)
|
/UniIsoRegression/inst/testfiles/reg_2d/libFuzzer_reg_2d/reg_2d_valgrind_files/1612737061-test.R
|
no_license
|
akhikolla/updatedatatype-list1
|
R
| false | false | 453 |
r
|
testlist <- list(metric = 0L, vec = NULL, vec = NULL, w_vec = structure(0, .Dim = c(1L, 1L)), y_vec = structure(c(5.6445088053016e-312, 7.2911220195564e-304, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(7L, 10L)))
result <- do.call(UniIsoRegression:::reg_2d,testlist)
str(result)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MizerParams-class.R
\name{emptyParams}
\alias{emptyParams}
\title{Create empty MizerParams object of the right size}
\usage{
emptyParams(
species_params,
gear_params = data.frame(),
no_w = 100,
min_w = 0.001,
max_w = NA,
min_w_pp = 1e-12
)
}
\arguments{
\item{species_params}{A data frame of species-specific parameter values.}
\item{gear_params}{A data frame with gear-specific parameter values.}
\item{no_w}{The number of size bins in the consumer spectrum.}
\item{min_w}{Sets the size of the eggs of all species for which this is not
given in the \code{w_min} column of the \code{species_params} dataframe.}
\item{max_w}{The largest size of the consumer spectrum. By default this is
set to the largest \code{w_inf} specified in the \code{species_params} data
frame.}
\item{min_w_pp}{The smallest size of the resource spectrum.}
}
\value{
An empty but valid MizerParams object
}
\description{
An internal function.
Sets up a valid \linkS4class{MizerParams} object with all the slots
initialised and given dimension names, but with some slots left empty. This
function is to be used by other functions to set up full parameter objects.
}
\section{Size grid}{
A size grid is created so that
the log-sizes are equally spaced. The spacing is chosen so that there will be
\code{no_w} fish size bins, with the smallest starting at \code{min_w} and the largest
starting at \code{max_w}. For \code{w_full} additional size bins are added below
\code{min_w}, with the same log size. The number of extra bins is such that
\code{min_w_pp} comes to lie within the smallest bin.
}
\section{Changes to species params}{
The \code{species_params} slot of the returned MizerParams object may differ
slightly from the data frame supplied as argument to this function because
default values are set for
\verb{w_min, w_inf, alpha, gear, interaction_resource}.
}
\seealso{
See \code{\link[=newMultispeciesParams]{newMultispeciesParams()}} for a function that fills
the slots left empty by this function.
}
|
/man/emptyParams.Rd
|
no_license
|
patricksykes/mizer
|
R
| false | true | 2,085 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MizerParams-class.R
\name{emptyParams}
\alias{emptyParams}
\title{Create empty MizerParams object of the right size}
\usage{
emptyParams(
species_params,
gear_params = data.frame(),
no_w = 100,
min_w = 0.001,
max_w = NA,
min_w_pp = 1e-12
)
}
\arguments{
\item{species_params}{A data frame of species-specific parameter values.}
\item{gear_params}{A data frame with gear-specific parameter values.}
\item{no_w}{The number of size bins in the consumer spectrum.}
\item{min_w}{Sets the size of the eggs of all species for which this is not
given in the \code{w_min} column of the \code{species_params} dataframe.}
\item{max_w}{The largest size of the consumer spectrum. By default this is
set to the largest \code{w_inf} specified in the \code{species_params} data
frame.}
\item{min_w_pp}{The smallest size of the resource spectrum.}
}
\value{
An empty but valid MizerParams object
}
\description{
An internal function.
Sets up a valid \linkS4class{MizerParams} object with all the slots
initialised and given dimension names, but with some slots left empty. This
function is to be used by other functions to set up full parameter objects.
}
\section{Size grid}{
A size grid is created so that
the log-sizes are equally spaced. The spacing is chosen so that there will be
\code{no_w} fish size bins, with the smallest starting at \code{min_w} and the largest
starting at \code{max_w}. For \code{w_full} additional size bins are added below
\code{min_w}, with the same log size. The number of extra bins is such that
\code{min_w_pp} comes to lie within the smallest bin.
}
\section{Changes to species params}{
The \code{species_params} slot of the returned MizerParams object may differ
slightly from the data frame supplied as argument to this function because
default values are set for
\verb{w_min, w_inf, alpha, gear, interaction_resource}.
}
\seealso{
See \code{\link[=newMultispeciesParams]{newMultispeciesParams()}} for a function that fills
the slots left empty by this function.
}
|
testlist <- list(G = numeric(0), Rn = numeric(0), atmp = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ra = numeric(0), relh = -1.72131968218895e+83, rs = numeric(0), temp = c(8.5728629954997e-312, 1.56898424065867e+82, 8.96970809549085e-158, -1.3258495253834e-113, 2.79620616433656e-119, -6.80033518839696e+41, 2.68298522855314e-211, 1444042902784.06, 6.68889884134308e+51, -4.05003163986346e-308, -3.52601820453991e+43, -1.49815227045093e+197, -2.61605817623304e+76, -1.18078903777423e-90, 9.66231645734968e+111, -5.58551357556946e+160, 2.00994342527714e-162, 1.81541609400943e-79, 7.89363005545926e+139, 2.3317908961407e-93, 2.16562581831091e+161))
result <- do.call(meteor:::ET0_PenmanMonteith,testlist)
str(result)
|
/meteor/inst/testfiles/ET0_PenmanMonteith/AFL_ET0_PenmanMonteith/ET0_PenmanMonteith_valgrind_files/1615839057-test.R
|
no_license
|
akhikolla/updatedatatype-list3
|
R
| false | false | 826 |
r
|
testlist <- list(G = numeric(0), Rn = numeric(0), atmp = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ra = numeric(0), relh = -1.72131968218895e+83, rs = numeric(0), temp = c(8.5728629954997e-312, 1.56898424065867e+82, 8.96970809549085e-158, -1.3258495253834e-113, 2.79620616433656e-119, -6.80033518839696e+41, 2.68298522855314e-211, 1444042902784.06, 6.68889884134308e+51, -4.05003163986346e-308, -3.52601820453991e+43, -1.49815227045093e+197, -2.61605817623304e+76, -1.18078903777423e-90, 9.66231645734968e+111, -5.58551357556946e+160, 2.00994342527714e-162, 1.81541609400943e-79, 7.89363005545926e+139, 2.3317908961407e-93, 2.16562581831091e+161))
result <- do.call(meteor:::ET0_PenmanMonteith,testlist)
str(result)
|
# aggregate 연습문제
# aggregate(출력 열 ~ 기준 열, 데이터, 통계함수)
head(Fruits,3)
Fruits
ls(Fruits) # 필드명 출력
# 1. 과일별 판매액의 평균을 구하시오
aggregate(Sales ~ Fruit, Fruits, mean)
# 2. 과일별 비용의 최대값을 구하시오
aggregate(Expenses ~ Fruit, Fruits, max)
# 3. 과일별 수익의 최소값을 구하시오
aggregate(Profit ~ Fruit, Fruits, min)
# 4. 위치별 판매금액의 평균을 구하시오
aggregate(Sales ~ Location, Fruits, mean)
# 5. 과일별 수익의 합계를 구하시오
aggregate(Profit ~ Fruit, Fruits, sum)
# 6. 연도별 판매금액의 합계를 구하시오
aggregate(Sales ~ Year, Fruits, sum)
# 7. 과일별 평균 비용을 구하시오
aggregate(Expenses ~ Fruit, Fruits, mean)
# 8. 지역 + 과일별 수익의 평균을 구하시오
aggregate(Profit ~ Location, Fruits, mean)
# 9. 지역 + 연도별 판매액의 합계를 구하시오
aggregate(Sales ~ Location + Year, Fruits, sum)
# 10. 지역 + 과일별 비용의 합계를 구하시오
aggregate(Expenses ~ Location + Fruit, Fruits, sum)
# 11. 지역 + 연도별 수익의 최대값을 구하시오
aggregate(Profit ~ Location + Year, Fruits, max)
# aggregate() 연습문제2
# 1. 1-4호선승하차승객수.csv 파일을 df_subway로 불러오세요. (stringsAsFactors=F)
df_subway<-read.csv('data\\1-4호선승하차승객수.csv',stringsAsFactors=F)
View(df_subway)
ls(df_subway)
# 2. 노선 번호별 총 승차 인원수를 구하시오
aggregate(승차 ~ 노선번호, df_subway, sum)
# 3. 노선 번호별 총 하차 인원수를 구하시오
aggregate(하차 ~ 노선번호, df_subway, sum)
# 4. 노선 번호별 승차 인원수 평균을 구하시오
aggregate(승차 ~ 노선번호, df_subway, mean)
# 5. 노선 번호별 하차 인원수 최소값을 구하시오
aggregate(하차 ~ 노선번호, df_subway, min)
# 6. 노선 번호별 승차, 하차 각 최대값을 구하시오
aggregate(cbind(승차,하차) ~ 노선번호, df_subway, max)
# 7. 노선 번호별 승차, 하차 각 최소값을 구하시오
aggregate(cbind(승차,하차) ~ 노선번호, df_subway, min)
# 8. 노선 번호별 승하차 총 인원수를 구하시오
aggregate(승차+하차 ~ 노선번호, df_subway, sum)
# apply() 연습문제
# 1. data1.csv 파일을 data1으로 불러오세요
data1<-read.csv("data\\data1.csv", stringsAsFactors = F)
View(data1)
ls(data1)
# 2. 연도별 합계를 모두 구하시오
apply(data1[,-1],2,sum)
# 3. 연령별 합계를 모두 구하시오
apply(data1[,-1],1,sum)
# 4. 2008년부터 2012년까지 연도별 합계를 각각 구하시오
apply(data1[,10:14],2,sum)
apply(data1[,10:(length(data1)-1)],2,sum)
# 5. 2008년부터 2012년까지 연령별 합계를 각각 구하시오
apply(data1[,10:14],1,sum)
# 6. 2000년, 2005년, 2010년의 연도별 합계를 각각 구해 d1에 대입하시오
d1<-apply(data1[,c(2,7,12)],2,sum)
d1
# 7. 2000년, 2005년, 2010년의 연령별 합계를 각각 구해 d2에 대입하시오
d2<-apply(data1[,c(2,7,12)],1,sum)
d2
# sapply() 연습문제
# 1. 연도별 합계를 모두 구하시오
sapply(data1[,-1],sum)
class(sapply(data1[,-1],sum))
# 출력결과 numeric->벡터형태
# 2. 2008년부터 2012년까지 연도별 합계를 각각 구하시오
sapply(data1[,10:14],sum)
lapply(data1[,10:14],sum)
class(lapply(data1[,10:14],sum)) # list 형태
unlist(lapply(data1[,10:14],sum)) # 벡터 형태로
# 3. 2000년, 2005년, 2010년 연도별 합계를 각각 구하시오
sapply(data1[,c(2,7,12)],sum) # 벡터 형태로 출력됨
# apply() / sapply() / tapply() 연습문제
# 1. 1-4호선승하차승객수.csv 파일을 data2로 불러오세요
data2<-read.csv("data\\1-4호선승하차승객수.csv", stringsAsFactors=F)
View(data2)
attach(data2)
# 2. 노선 번호별 총 승차 인원수를 구하시오
tapply(승차,노선번호,sum)
# 3. 노선 번호별 총 하차 인원수를 구하시오
tapply(하차,노선번호,sum)
# 4. 노선 번호에 상관없이 총 승차 인원수 및 총 하차 인원수를 구하시오
apply(data2[,3:4],2,sum)
sapply(data2[,3:4],sum)
sapply(data2[3:4],sum) # sapply가 열 계산을 하기때문에 인덱싱에서 ,를 생략할수있다
# 5. 노선 번호별 총 승차 인원수 및 총 하차 인원수의 평균을 구하시오
tapply(승차,노선번호,mean)
tapply(하차,노선번호,mean)
# tapply(cbind(하차,승차),노선번호,mean) -> 에러 뜸
tapply(승차+하차, 노선번호, mean) # 총 승하차 인원의 노선별 평균
# 6. 노선 번호에 상관없이 승차 및 하차 인원수 최소값을 각각 구하시오
sapply(data2[3:4],min)
sapply(data2[,c(3,4)],min)
# 7. 노선 번호별 인원수의 최소값을 구하시오
tapply(승차+하차,노선번호,min)
# 노선 번호별 하차인원수 최소값
tapply(하차, 노선번호, min)
# 노선 번호별 승차인원수 최소값
tapply(승차, 노선번호, min)
# stringr 패키지 연습문제1
mystr<-'홍길동15강감찬25이순신35김유신45'
# 1. mystr문자열의 길이를 구해 len 변수에 저장하고 변수를 확인하시오
len<-str_length(mystr)
len
# 2. '김유신'의 위치를 파악한다
str_locate(mystr,"김유신")
# 3. mystr의 앞에서 4글자를 추출한다
str_sub(mystr,start=1,end=4)
str_sub(mystr,1,4)
# 4. mystr의 5번째부터 끝까지 추출한다
str_sub(mystr,start=5)
str_sub(mystr,5)
# stringr 패키지 연습문제2
mystr<-'hello world'
# 1. mystr 변수의 모든 글자를 대문자로 변환 후 mystr 변수에 저장하고 변수 내용을 출력하시오
mystr<-str_to_upper(mystr)
mystr
# 2. mystr 변수의 모든 글자를 소문자로 변환 후 mystr변수에 저장하고 변수 내용을 출력하시오
mystr<-str_to_lower(mystr)
mystr
# 3. 문자열 변수 mystr의 값에 대해서 단어의 첫 글자를 대문자로 변환한다
str_to_title(mystr)
# stringr 패키지 연습문제3
mystr='hong1234'
# 1. mystr변수의 값 중 hong을 kang으로 대체하시오
str_replace(mystr,'hong','kang')
# 2. mystr변수의 값 중 1234를 5678로 대체하시오
str_replace(mystr,'1234','5678')
# 3. mystr의 내용과 'choi9876'을 결합하시오
str_c(mystr,"choi9876")
# stringr 패키지 연습문제4
mystr='kim,lee,choi,park'
# 1. mystr 내용을 ,를 기준으로 분리하시오.
str_split(mystr,",")
# 2. human 목록들에 대하여 구분자를 /로 연결하시오.
human<-c("홍길동10","박영희20","김철수30","이몽룡40")
str_c(human,collapse = "/")
paste(human,collapse = "/")
# stringr 패키지 연습문제5
fruits<-c('apple','Apple','banana','Pineapple',"Java",'jungle')
# 1. 대문자P가 있는 단어 찾기
str_detect(fruits,'P')
# 2. 첫 글자가 소문자 b인 단어 찾기
str_detect(fruits,'^b')
# 3. 끝나는 글자가 소문자 e인 단어 찾기
str_detect(fruits,'e$')
# 4. 시작하는 글자가 대문자 J나 소문자 j인 단어 찾기
str_detect(fruits,'^[Jj]')
# 5. 단어에 소문자 l과 대문자 P가 들어있는 단어 찾기
str_detect(fruits,'[lp]')
# stringr 패키지 연습문제6
|
/script/0702연습문제.R
|
no_license
|
valborgs/RStudy
|
R
| false | false | 7,115 |
r
|
# aggregate 연습문제
# aggregate(출력 열 ~ 기준 열, 데이터, 통계함수)
head(Fruits,3)
Fruits
ls(Fruits) # 필드명 출력
# 1. 과일별 판매액의 평균을 구하시오
aggregate(Sales ~ Fruit, Fruits, mean)
# 2. 과일별 비용의 최대값을 구하시오
aggregate(Expenses ~ Fruit, Fruits, max)
# 3. 과일별 수익의 최소값을 구하시오
aggregate(Profit ~ Fruit, Fruits, min)
# 4. 위치별 판매금액의 평균을 구하시오
aggregate(Sales ~ Location, Fruits, mean)
# 5. 과일별 수익의 합계를 구하시오
aggregate(Profit ~ Fruit, Fruits, sum)
# 6. 연도별 판매금액의 합계를 구하시오
aggregate(Sales ~ Year, Fruits, sum)
# 7. 과일별 평균 비용을 구하시오
aggregate(Expenses ~ Fruit, Fruits, mean)
# 8. 지역 + 과일별 수익의 평균을 구하시오
aggregate(Profit ~ Location, Fruits, mean)
# 9. 지역 + 연도별 판매액의 합계를 구하시오
aggregate(Sales ~ Location + Year, Fruits, sum)
# 10. 지역 + 과일별 비용의 합계를 구하시오
aggregate(Expenses ~ Location + Fruit, Fruits, sum)
# 11. 지역 + 연도별 수익의 최대값을 구하시오
aggregate(Profit ~ Location + Year, Fruits, max)
# aggregate() 연습문제2
# 1. 1-4호선승하차승객수.csv 파일을 df_subway로 불러오세요. (stringsAsFactors=F)
df_subway<-read.csv('data\\1-4호선승하차승객수.csv',stringsAsFactors=F)
View(df_subway)
ls(df_subway)
# 2. 노선 번호별 총 승차 인원수를 구하시오
aggregate(승차 ~ 노선번호, df_subway, sum)
# 3. 노선 번호별 총 하차 인원수를 구하시오
aggregate(하차 ~ 노선번호, df_subway, sum)
# 4. 노선 번호별 승차 인원수 평균을 구하시오
aggregate(승차 ~ 노선번호, df_subway, mean)
# 5. 노선 번호별 하차 인원수 최소값을 구하시오
aggregate(하차 ~ 노선번호, df_subway, min)
# 6. 노선 번호별 승차, 하차 각 최대값을 구하시오
aggregate(cbind(승차,하차) ~ 노선번호, df_subway, max)
# 7. 노선 번호별 승차, 하차 각 최소값을 구하시오
aggregate(cbind(승차,하차) ~ 노선번호, df_subway, min)
# 8. 노선 번호별 승하차 총 인원수를 구하시오
aggregate(승차+하차 ~ 노선번호, df_subway, sum)
# apply() 연습문제
# 1. data1.csv 파일을 data1으로 불러오세요
data1<-read.csv("data\\data1.csv", stringsAsFactors = F)
View(data1)
ls(data1)
# 2. 연도별 합계를 모두 구하시오
apply(data1[,-1],2,sum)
# 3. 연령별 합계를 모두 구하시오
apply(data1[,-1],1,sum)
# 4. 2008년부터 2012년까지 연도별 합계를 각각 구하시오
apply(data1[,10:14],2,sum)
apply(data1[,10:(length(data1)-1)],2,sum)
# 5. 2008년부터 2012년까지 연령별 합계를 각각 구하시오
apply(data1[,10:14],1,sum)
# 6. 2000년, 2005년, 2010년의 연도별 합계를 각각 구해 d1에 대입하시오
d1<-apply(data1[,c(2,7,12)],2,sum)
d1
# 7. 2000년, 2005년, 2010년의 연령별 합계를 각각 구해 d2에 대입하시오
d2<-apply(data1[,c(2,7,12)],1,sum)
d2
# sapply() 연습문제
# 1. 연도별 합계를 모두 구하시오
sapply(data1[,-1],sum)
class(sapply(data1[,-1],sum))
# 출력결과 numeric->벡터형태
# 2. 2008년부터 2012년까지 연도별 합계를 각각 구하시오
sapply(data1[,10:14],sum)
lapply(data1[,10:14],sum)
class(lapply(data1[,10:14],sum)) # list 형태
unlist(lapply(data1[,10:14],sum)) # 벡터 형태로
# 3. 2000년, 2005년, 2010년 연도별 합계를 각각 구하시오
sapply(data1[,c(2,7,12)],sum) # 벡터 형태로 출력됨
# apply() / sapply() / tapply() 연습문제
# 1. 1-4호선승하차승객수.csv 파일을 data2로 불러오세요
data2<-read.csv("data\\1-4호선승하차승객수.csv", stringsAsFactors=F)
View(data2)
attach(data2)
# 2. 노선 번호별 총 승차 인원수를 구하시오
tapply(승차,노선번호,sum)
# 3. 노선 번호별 총 하차 인원수를 구하시오
tapply(하차,노선번호,sum)
# 4. 노선 번호에 상관없이 총 승차 인원수 및 총 하차 인원수를 구하시오
apply(data2[,3:4],2,sum)
sapply(data2[,3:4],sum)
sapply(data2[3:4],sum) # sapply가 열 계산을 하기때문에 인덱싱에서 ,를 생략할수있다
# 5. 노선 번호별 총 승차 인원수 및 총 하차 인원수의 평균을 구하시오
tapply(승차,노선번호,mean)
tapply(하차,노선번호,mean)
# tapply(cbind(하차,승차),노선번호,mean) -> 에러 뜸
tapply(승차+하차, 노선번호, mean) # 총 승하차 인원의 노선별 평균
# 6. 노선 번호에 상관없이 승차 및 하차 인원수 최소값을 각각 구하시오
sapply(data2[3:4],min)
sapply(data2[,c(3,4)],min)
# 7. 노선 번호별 인원수의 최소값을 구하시오
tapply(승차+하차,노선번호,min)
# 노선 번호별 하차인원수 최소값
tapply(하차, 노선번호, min)
# 노선 번호별 승차인원수 최소값
tapply(승차, 노선번호, min)
# stringr 패키지 연습문제1
mystr<-'홍길동15강감찬25이순신35김유신45'
# 1. mystr문자열의 길이를 구해 len 변수에 저장하고 변수를 확인하시오
len<-str_length(mystr)
len
# 2. '김유신'의 위치를 파악한다
str_locate(mystr,"김유신")
# 3. mystr의 앞에서 4글자를 추출한다
str_sub(mystr,start=1,end=4)
str_sub(mystr,1,4)
# 4. mystr의 5번째부터 끝까지 추출한다
str_sub(mystr,start=5)
str_sub(mystr,5)
# stringr 패키지 연습문제2
mystr<-'hello world'
# 1. mystr 변수의 모든 글자를 대문자로 변환 후 mystr 변수에 저장하고 변수 내용을 출력하시오
mystr<-str_to_upper(mystr)
mystr
# 2. mystr 변수의 모든 글자를 소문자로 변환 후 mystr변수에 저장하고 변수 내용을 출력하시오
mystr<-str_to_lower(mystr)
mystr
# 3. 문자열 변수 mystr의 값에 대해서 단어의 첫 글자를 대문자로 변환한다
str_to_title(mystr)
# stringr 패키지 연습문제3
mystr='hong1234'
# 1. mystr변수의 값 중 hong을 kang으로 대체하시오
str_replace(mystr,'hong','kang')
# 2. mystr변수의 값 중 1234를 5678로 대체하시오
str_replace(mystr,'1234','5678')
# 3. mystr의 내용과 'choi9876'을 결합하시오
str_c(mystr,"choi9876")
# stringr 패키지 연습문제4
mystr='kim,lee,choi,park'
# 1. mystr 내용을 ,를 기준으로 분리하시오.
str_split(mystr,",")
# 2. human 목록들에 대하여 구분자를 /로 연결하시오.
human<-c("홍길동10","박영희20","김철수30","이몽룡40")
str_c(human,collapse = "/")
paste(human,collapse = "/")
# stringr 패키지 연습문제5
fruits<-c('apple','Apple','banana','Pineapple',"Java",'jungle')
# 1. 대문자P가 있는 단어 찾기
str_detect(fruits,'P')
# 2. 첫 글자가 소문자 b인 단어 찾기
str_detect(fruits,'^b')
# 3. 끝나는 글자가 소문자 e인 단어 찾기
str_detect(fruits,'e$')
# 4. 시작하는 글자가 대문자 J나 소문자 j인 단어 찾기
str_detect(fruits,'^[Jj]')
# 5. 단어에 소문자 l과 대문자 P가 들어있는 단어 찾기
str_detect(fruits,'[lp]')
# stringr 패키지 연습문제6
|
library(fastcluster)
library(gplots)
library("pheatmap")
args=commandArgs(trailingOnly=TRUE)
i=1
name=as.character(args[i]); i=i+1
DEmatrix=NULL
fileEnd=as.character(args[i]); i=i+1
while (i <= length(args))
{
#original code for use with fpkm
x=read.delim(as.character(args[i]), header=F); i=i+1
DEmatrix=cbind(DEmatrix,as.numeric(x[,4]))
}
coords=x[,1:3]
cent=round((coords[,3]+coords[,2])/2)
coords[,2]=cent
coords[,3]=cent+1
myCol <- c(colorRampPalette(c("white", "green4"))(6))
myBreaks <- c(-.2, -.001, .2, .4, .6, .8, 1)
#y=kmeans(DEmatrix,32, iter.max = 50, nstart=100)
#clusters=y$cluster
sums=apply(DEmatrix,1,max)
#DEmatrix=DEmatrix[sums > 20,]
#ordr=hclust(dist(DEmatrix))$order
#ordr=seq(length(DEmatrix[,1]))
#ordr=order(DEmatrix[,2]-DEmatrix[,1], decreasing=T)
ordr=order(DEmatrix[,2], decreasing=T)
#png(paste("heatmap_", name, ".png", sep=""), height = 2500, width = 2500, res=2500)
png(paste("heatmap_", name, ".png", sep=""), height = 3000, width = 3000, res=2500)
#ordr=order(clusters, apply(abs(DEmatrix), 1, max))
#colnames(DEmatrix)=gsub("WTAdultIntestinalEpithelium", "Intestine", gsub(fileEnd, "", args[-c(1,2)]))
colnames(DEmatrix)=c("a", "b")
pheatmap(DEmatrix[ordr,], border_color=NA, color=myCol, breaks=myBreaks, cluster_rows=FALSE, cluster_cols=FALSE, scale="none", fontsize = 12, fontsize_col=20, legend=F)
dev.off()
write.table(cbind(coords[ordr,], seq(length.out = length(ordr))), paste(name, ".bed", sep=""), row.names=F, col.names=F, quote=F, sep="\t")
|
/BSseq_mouseIntestine/getHeatmaps_methylation.R
|
no_license
|
xjyx/Scripts-for-Kremsky-Corces-2020-Genome-Biology-paper
|
R
| false | false | 1,493 |
r
|
library(fastcluster)
library(gplots)
library("pheatmap")
args=commandArgs(trailingOnly=TRUE)
i=1
name=as.character(args[i]); i=i+1
DEmatrix=NULL
fileEnd=as.character(args[i]); i=i+1
while (i <= length(args))
{
#original code for use with fpkm
x=read.delim(as.character(args[i]), header=F); i=i+1
DEmatrix=cbind(DEmatrix,as.numeric(x[,4]))
}
coords=x[,1:3]
cent=round((coords[,3]+coords[,2])/2)
coords[,2]=cent
coords[,3]=cent+1
myCol <- c(colorRampPalette(c("white", "green4"))(6))
myBreaks <- c(-.2, -.001, .2, .4, .6, .8, 1)
#y=kmeans(DEmatrix,32, iter.max = 50, nstart=100)
#clusters=y$cluster
sums=apply(DEmatrix,1,max)
#DEmatrix=DEmatrix[sums > 20,]
#ordr=hclust(dist(DEmatrix))$order
#ordr=seq(length(DEmatrix[,1]))
#ordr=order(DEmatrix[,2]-DEmatrix[,1], decreasing=T)
ordr=order(DEmatrix[,2], decreasing=T)
#png(paste("heatmap_", name, ".png", sep=""), height = 2500, width = 2500, res=2500)
png(paste("heatmap_", name, ".png", sep=""), height = 3000, width = 3000, res=2500)
#ordr=order(clusters, apply(abs(DEmatrix), 1, max))
#colnames(DEmatrix)=gsub("WTAdultIntestinalEpithelium", "Intestine", gsub(fileEnd, "", args[-c(1,2)]))
colnames(DEmatrix)=c("a", "b")
pheatmap(DEmatrix[ordr,], border_color=NA, color=myCol, breaks=myBreaks, cluster_rows=FALSE, cluster_cols=FALSE, scale="none", fontsize = 12, fontsize_col=20, legend=F)
dev.off()
write.table(cbind(coords[ordr,], seq(length.out = length(ordr))), paste(name, ".bed", sep=""), row.names=F, col.names=F, quote=F, sep="\t")
|
# Source depdencies -------------------------------------------
source("global_variable.R")
source("global_dependencies.R")
source("set_curlOptions.R")
# Connections --------------------------------------------------
sdy269 <- CreateConnection("SDY269", verbose = TRUE)
sdy34 <- suppressMessages(CreateConnection("SDY34", verbose = TRUE))
# Helper Functions ---------------------------------------------
try_gei <- function(con){
tryCatch(
con$GeneExpressionInputs(),
warning = function(w) return(w),
error = function(e) return(e)
)
}
# Tests --------------------------------------------------------
context("GeneExpressionInputs")
test_that("returns GE inputs df if study has inputs", {
res <- try_gei(sdy269)
expect_true( (dim(res)[1] > 0) & (dim(res)[2] > 0) )
})
test_that("returns error if study does not have inputs", {
res <- try_gei(sdy34)
expect_true( res$message == "Gene Expression Inputs not found for study.")
})
# cleanup ------------------------------------------------------
if(exists("netrc_file")){
file.remove(netrc_file)
}
|
/tests/testthat/test-GeneExpresssionInputs.R
|
no_license
|
wangdi2014/ImmuneSpaceR
|
R
| false | false | 1,079 |
r
|
# Source depdencies -------------------------------------------
source("global_variable.R")
source("global_dependencies.R")
source("set_curlOptions.R")
# Connections --------------------------------------------------
sdy269 <- CreateConnection("SDY269", verbose = TRUE)
sdy34 <- suppressMessages(CreateConnection("SDY34", verbose = TRUE))
# Helper Functions ---------------------------------------------
try_gei <- function(con){
tryCatch(
con$GeneExpressionInputs(),
warning = function(w) return(w),
error = function(e) return(e)
)
}
# Tests --------------------------------------------------------
context("GeneExpressionInputs")
test_that("returns GE inputs df if study has inputs", {
res <- try_gei(sdy269)
expect_true( (dim(res)[1] > 0) & (dim(res)[2] > 0) )
})
test_that("returns error if study does not have inputs", {
res <- try_gei(sdy34)
expect_true( res$message == "Gene Expression Inputs not found for study.")
})
# cleanup ------------------------------------------------------
if(exists("netrc_file")){
file.remove(netrc_file)
}
|
getCapitalGainMonth <- function(ticker = "GOOG", from = "2013-01-01", to=Sys.time()){
mydata <- yahoodata(ticker, from, to);
names(mydata) <- c("Symbol","Value","Date","Time","Name");
num<-dim(mydata)[1];
gainf<-matrix(ncol=2,nrow=floor((num-1)/20));
for(i in 1:num-1){
if((i/20)==floor(i/20)){
gainf[i/20,1]<- 100*((mydata[i+1,2]-mydata[i,2])/mydata[i+1,2]);
gainf[i/20,2]<-mydata[i+1,3];
}}
gainf<-as.data.frame(gainf);
colnames(gainf)<-c("Value","Date");
return(gainf);
}
|
/R/getCapitalGainMonth.R
|
no_license
|
fcluzeau/stock
|
R
| false | false | 477 |
r
|
getCapitalGainMonth <- function(ticker = "GOOG", from = "2013-01-01", to=Sys.time()){
mydata <- yahoodata(ticker, from, to);
names(mydata) <- c("Symbol","Value","Date","Time","Name");
num<-dim(mydata)[1];
gainf<-matrix(ncol=2,nrow=floor((num-1)/20));
for(i in 1:num-1){
if((i/20)==floor(i/20)){
gainf[i/20,1]<- 100*((mydata[i+1,2]-mydata[i,2])/mydata[i+1,2]);
gainf[i/20,2]<-mydata[i+1,3];
}}
gainf<-as.data.frame(gainf);
colnames(gainf)<-c("Value","Date");
return(gainf);
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/csnapWhisker.R
\name{csnapWhisker}
\alias{csnapWhisker}
\title{csnapWhisker()}
\usage{
csnapWhisker(tre_files_path)
}
\arguments{
\item{tre_files_path}{A path to where your .tre files (assumes you have AIC, Saturated, BIC, Gene, and Uniform). Future edits will make this generalized.}
}
\description{
Create a Compare Shared Node Ages Plot (CSNAP) with whiskers
}
\keyword{ancestry}
|
/man/csnapWhisker.Rd
|
no_license
|
palautatan/csnap
|
R
| false | true | 461 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/csnapWhisker.R
\name{csnapWhisker}
\alias{csnapWhisker}
\title{csnapWhisker()}
\usage{
csnapWhisker(tre_files_path)
}
\arguments{
\item{tre_files_path}{A path to where your .tre files (assumes you have AIC, Saturated, BIC, Gene, and Uniform). Future edits will make this generalized.}
}
\description{
Create a Compare Shared Node Ages Plot (CSNAP) with whiskers
}
\keyword{ancestry}
|
########## SURVEY COMPARISON PROJECT MODEL DATAFRAME CREATION ##########
##########
##########
# This file combines fish observation data from three survey types (SVC,
# transect, and roving) with associated survey metadata, habitat traits, and
# species traits before calculating density and average depth across
# observations and log transforming density for further analyses.
##########
##########
# AUTHOR: Iris M. George
# DATE OF CREATION: 2021-06-07
##########
##########
# Set-Up =======================================================================
# packages
library(plyr)
library(tidyverse)
library(here)
# data
fish_data <- read_csv(here("./dataframes/fish_dataframe.csv"))
SVC <- read_csv(here("./clean_data/SVC_data.csv"))
prey_meta <- read_csv(here("./clean_data/prey_metadata.csv"))
pred_meta <- read_csv(here("./clean_data/pred_metadata.csv"))
traits <- read_csv(here("./clean_data/fish_traits.csv"))
vert_relief <- read_csv(here("./clean_data/vertical_relief.csv"))
# Joining Survey Metadata ======================================================
# The following joins metadata associated with each survey type (SVC, transect
# and roving) to the full fish dataframe.
# Joining SVC Metadata:
# select wanted metadata columns: session, site, date, diver, habitat,
# cylinder_area, max_depth, octocoral, stony coral
SVC_meta <- SVC[,c(1,3,4,12,16,17,33,34)]
# aggregate rows by session
SVC_meta <- SVC_meta %>% group_by(session, site, SVC_date, SVC_habitat) %>%
summarise_each(funs(mean))
# rename area column
SVC_meta <- SVC_meta %>% rename(SVC_area = SVC_cylinder_area)
# join meta data to fish data
SVC_full <- join(fish_data, SVC_meta, by = NULL, type = "left", match = "first")
# Joining Transect Metadata:
# select wanted metadata columns: session, site, date, transect_area, depth
prey_meta <- prey_meta[,c(1,4,5,7,15)]
# rename columns
prey_meta <- prey_meta %>% rename(prey_depth = prey_depth_m)
prey_meta <- prey_meta %>% rename(prey_area = prey_tran_area)
# want to aggregate depth by mean and area by sum: splitting up
# transform depth and area columns from character to numeric
prey_meta <- transform(prey_meta, prey_depth = as.numeric(prey_depth),
prey_area = as.numeric(prey_area))
# remove area column from full transect meta
prey_depth <- prey_meta[,c(1:4)]
# aggregate depth rows by session
prey_depth <- aggregate(.~session+site+prey_date, prey_depth, mean)
# remove depth column from full transect meta
prey_area <- prey_meta[,c(1:3,5)]
# aggregate area rows by session
prey_area <- aggregate(.~session+site+prey_date, prey_area, sum)
# join transect depth and area to make full transect metadata
prey_meta <- join(prey_depth, prey_area, by = NULL, type = "full",
match = "all")
# join transect metadata to fish and SVC dataframe
SVCprey_full <- join(SVC_full, prey_meta, by = NULL, type = "left",
match = "first")
# Joining Roving Metadata:
# select wanted metadata columns: session, site, date, transect_area, depth
pred_meta <- pred_meta[,c(1,4,8,17,21)]
# rename columns
pred_meta <- pred_meta %>% rename(pred_depth = pred_depth_ft)
pred_meta <- pred_meta %>% rename(pred_area = pred_trans_area)
# want to aggregate depth by mean and area by sum: splitting up
# remove area column
pred_depth <- pred_meta[,c(1:4)]
# aggregate depth rows by session
pred_depth <- aggregate(.~session+site+pred_date, pred_depth, mean)
# remove depth column
pred_area <- pred_meta[,c(1:3,5)]
# aggregate area rows by session
pred_area <- aggregate(.~session+site+pred_date, pred_area, sum)
# join roving depth and area to create full roving metadata
pred_meta <- join(pred_depth, pred_area, by = NULL, type = "full",
match = "all")
# join roving metadata to fish, SVC, and transect dataframe
fish_meta <- join(SVCprey_full, pred_meta, by = NULL, type = "left",
match = "first")
# Joining Vertical Relief Data =================================================
# The following section aggregates vertical relief measures to a mean value per
# survey site, then joins these measures to the fish and survey metadata
# dataframe.
# select site and vert relief columns
vert_relief <- vert_relief[,c(1,7)]
# aggregate to site mean
vert_relief <- aggregate(relief_cm~site, vert_relief, mean)
# change KL-P30 to KL-30
vert_relief$site[vert_relief$site == "KL-P30"] <- "KL-30"
# join vertical relief measure to each site
fish_meta <- join(fish_meta, vert_relief, by = NULL, type = "left",
match = "first")
# Joining Species' Trait Data ==================================================
# The following joins adult fish traits used in further analyses to the survey
# metadata and fish dataframe.
# filter for adult lifestage
fish_traits <- filter(traits, lifestage == "adult")
# select relevant columns: latin names, predator presence, nocturnal,
# max_length, position, behaviour, colouration, cryptic_behaviour, shape,
# trophic position
fish_traits <- fish_traits[,c(1:4,7,18,36,38,39,60,61,70,74)]
# rename columns
fish_traits <- fish_traits %>% rename(colouration = colouration_cat3)
fish_traits <- fish_traits %>% rename(species = common_name)
# join fish trait data to meta and fish dataframe
full_dataframe <- join(fish_meta, fish_traits, by = NULL, type = "full",
match = "all")
# remove un-recorded species
sessions <- unique(full_dataframe$session)
length(sessions)
na_values <- which(is.na(full_dataframe), arr.ind=TRUE)
# replace roving NAs with 0s
full_dataframe$pred_date[is.na(full_dataframe$pred_date)] <- "2014-01-01"
full_dataframe$pred_depth[is.na(full_dataframe$pred_depth)] <- 0
full_dataframe$pred_area[is.na(full_dataframe$pred_area)] <- 0
# remove NA values
full_dataframe <- na.omit(full_dataframe)
# Density Calculation ==========================================================
# The following calculates fish densities for each of the three survey types
# (SVC, transect, and roving) for each single observation. It then calculates
# the density differences between each of the survey types for each observation.
# SVC density calculation
full_dataframe$SVC_density <-
full_dataframe$SVC_abundance/full_dataframe$SVC_area
# transect survey density calculation
full_dataframe$prey_density <-
full_dataframe$prey_abundance/full_dataframe$prey_area
# roving survey density calculation
full_dataframe$pred_density <-
full_dataframe$pred_abundance/full_dataframe$pred_area
# SVC - transect density difference calculation
full_dataframe$SVC_prey_difference <-
full_dataframe$SVC_density - full_dataframe$prey_density
# SVC - roving density difference calculation
full_dataframe$SVC_pred_difference <-
full_dataframe$SVC_density - full_dataframe$pred_density
# transect - roving density difference calculation
full_dataframe$prey_pred_difference <-
full_dataframe$prey_density - full_dataframe$pred_density
# Dataframe Edits ==============================================================
# The following provides some additional edits to the full dataframe to produce
# a final version.
# re-order columns
full_dataframe <- full_dataframe[,c(2,1,21:23,3,4,8,10,13,14,21,25:33,9,11,12,
34,15:17,35,18:20,36:39)]
# re-name columns
full_dataframe <- full_dataframe %>% rename(habitat = SVC_habitat)
full_dataframe <- full_dataframe %>% rename(species_order = order)
# SVC vs. Transect Survey Dataframe ============================================
# The following creates a dataframe specific to observations within SVC and
# transect surveys.
# select SVC and transect columns
SVCprey <- full_dataframe[,c(1:29,34)]
# calculate total density for SVC and transect surveys
SVCprey$total_density <- SVCprey$SVC_density+SVCprey$prey_density
# remove rows where total density = 0
SVCprey_data <- SVCprey[SVCprey$total_density !=0,]
# remove trumpetfish (only species in order)
SVCprey_data <- SVCprey_data[SVCprey_data$species_order !="Syngnathiformes",]
# remove silversides (only species in order)
SVCprey_data <- SVCprey_data[SVCprey_data$species_order !="Atheriniformes",]
# remove eyed flounder (only depressiform species)
SVCprey_data <- SVCprey_data[SVCprey_data$species_order !="Pleuronectiformes",]
# remove mackerel scad (only pelagic species)
SVCprey_data <- SVCprey_data[SVCprey_data$species !="mackerel scad",]
# remove eels
SVCprey_data <- SVCprey_data[SVCprey_data$species_order !="Anguilliformes",]
# remove sessions with un-matched dates between surveys
SVCprey_data <- SVCprey_data[SVCprey_data$session !=178,]
SVCprey_data <- SVCprey_data[SVCprey_data$session !=179,]
SVCprey_data <- SVCprey_data[SVCprey_data$session !=180,]
SVCprey_data <- SVCprey_data[SVCprey_data$session !=268,]
# SVC vs. Roving Survey Dataframe ==============================================
# The following creates a dataframe specific to observations within SVC and
# roving surveys.
# select SVC and roving columns
SVCpred <- full_dataframe[,c(1:25,30:33,35)]
# filter for species recorded on predator surveys
SVCpred <- filter(SVCpred, predator_presence == 1)
# calculate total density for SVC and roving surveys
SVCpred$total_density <- SVCpred$SVC_density+SVCpred$pred_density
# remove rows where total density = 0
SVCpred_data <- SVCpred[SVCpred$total_density !=0,]
# remove trumpetfish (only species in order)
SVCpred_data <- SVCpred_data[SVCpred_data$species_order !="Syngnathiformes",]
# remove gray snapper (inconsistently reported)
SVCpred_data <- SVCpred_data[SVCpred_data$species !="gray snapper",]
# remove amberjack (only schooling species)
SVCpred_data <- SVCpred_data[SVCpred_data$species !="amberjack",]
# remove black margate (only compressiform species)
# SVCpred_data <- SVCpred_data[SVCpred_data$species !="black margate",]
# remove sessions with un-matched dates between surveys
SVCpred_data <- SVCpred_data[SVCpred_data$session !=178,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=179,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=180,]
# remove sessions with no roving data
SVCpred_data <- SVCpred_data[SVCpred_data$session !=264,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=265,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=266,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=267,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=268,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=269,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=270,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=271,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=272,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=273,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=274,]
# remove NA values
SVCpred_data <- na.omit(SVCpred_data)
# Density Log Transformation ===================================================
# Density differences between the survey types did not meet normality
# assumptions required for analyses, so the following conducts a log
# transformation of raw densities before taking the difference to improved
# normality.
# calculate log SVC density in SVC vs. transect dataframe
log_SVCdensity <- log(SVCprey_data$SVC_density + 0.001)
# calculate log transect density
log_preydensity <- log(SVCprey_data$prey_density + 0.001)
# histogram of SVC vs. transect log density differences
hist(log_SVCdensity-log_preydensity)
# calculate SVC vs. transect log density difference
SVCprey_data$log_difference <- log_SVCdensity-log_preydensity
# calculate log SVC density in SVC vs. roving dataframe
log_SVCdensity2 <- log(SVCpred_data$SVC_density + 0.001)
# calculate log roving density
log_preddensity <- log(SVCpred_data$pred_density + 0.001)
# histogram of SVC vs. roving log density differences
hist(log_SVCdensity2-log_preddensity)
# calculate SVC vs. roving log density difference
SVCpred_data$log_difference <- log_SVCdensity2-log_preddensity
# Average Depth Calculation ====================================================
# The following calculates average depth values for each session between the two
# survey types in each dataframe (SVC vs. transect and SVC vs. roving).
# SVC vs. transect dataframe average depth calculation
SVCprey_data$average_depth <- (SVCprey_data$SVC_max_depth +
SVCprey_data$prey_depth)/2
# SVC vs. roving dataframe average depth calculation
SVCpred_data$average_depth <- (SVCpred_data$SVC_max_depth +
SVCpred_data$pred_depth)/2
# export SVC vs. transect survey dataframe
write_csv(SVCprey_data, here("./dataframes/SVCprey_dataframe.csv"))
# export SVC vs. roving survey dataframe
write_csv(SVCpred_data, here("./dataframes/SVCpred_dataframe.csv"))
|
/src/model_dataframes.R
|
no_license
|
iris-george/Reef-Survey-Comparison
|
R
| false | false | 12,839 |
r
|
########## SURVEY COMPARISON PROJECT MODEL DATAFRAME CREATION ##########
##########
##########
# This file combines fish observation data from three survey types (SVC,
# transect, and roving) with associated survey metadata, habitat traits, and
# species traits before calculating density and average depth across
# observations and log transforming density for further analyses.
##########
##########
# AUTHOR: Iris M. George
# DATE OF CREATION: 2021-06-07
##########
##########
# Set-Up =======================================================================
# packages
library(plyr)
library(tidyverse)
library(here)
# data
fish_data <- read_csv(here("./dataframes/fish_dataframe.csv"))
SVC <- read_csv(here("./clean_data/SVC_data.csv"))
prey_meta <- read_csv(here("./clean_data/prey_metadata.csv"))
pred_meta <- read_csv(here("./clean_data/pred_metadata.csv"))
traits <- read_csv(here("./clean_data/fish_traits.csv"))
vert_relief <- read_csv(here("./clean_data/vertical_relief.csv"))
# Joining Survey Metadata ======================================================
# The following joins metadata associated with each survey type (SVC, transect
# and roving) to the full fish dataframe.
# Joining SVC Metadata:
# select wanted metadata columns: session, site, date, diver, habitat,
# cylinder_area, max_depth, octocoral, stony coral
SVC_meta <- SVC[,c(1,3,4,12,16,17,33,34)]
# aggregate rows by session
SVC_meta <- SVC_meta %>% group_by(session, site, SVC_date, SVC_habitat) %>%
summarise_each(funs(mean))
# rename area column
SVC_meta <- SVC_meta %>% rename(SVC_area = SVC_cylinder_area)
# join meta data to fish data
SVC_full <- join(fish_data, SVC_meta, by = NULL, type = "left", match = "first")
# Joining Transect Metadata:
# select wanted metadata columns: session, site, date, transect_area, depth
prey_meta <- prey_meta[,c(1,4,5,7,15)]
# rename columns
prey_meta <- prey_meta %>% rename(prey_depth = prey_depth_m)
prey_meta <- prey_meta %>% rename(prey_area = prey_tran_area)
# want to aggregate depth by mean and area by sum: splitting up
# transform depth and area columns from character to numeric
prey_meta <- transform(prey_meta, prey_depth = as.numeric(prey_depth),
prey_area = as.numeric(prey_area))
# remove area column from full transect meta
prey_depth <- prey_meta[,c(1:4)]
# aggregate depth rows by session
prey_depth <- aggregate(.~session+site+prey_date, prey_depth, mean)
# remove depth column from full transect meta
prey_area <- prey_meta[,c(1:3,5)]
# aggregate area rows by session
prey_area <- aggregate(.~session+site+prey_date, prey_area, sum)
# join transect depth and area to make full transect metadata
prey_meta <- join(prey_depth, prey_area, by = NULL, type = "full",
match = "all")
# join transect metadata to fish and SVC dataframe
SVCprey_full <- join(SVC_full, prey_meta, by = NULL, type = "left",
match = "first")
# Joining Roving Metadata:
# select wanted metadata columns: session, site, date, transect_area, depth
pred_meta <- pred_meta[,c(1,4,8,17,21)]
# rename columns
pred_meta <- pred_meta %>% rename(pred_depth = pred_depth_ft)
pred_meta <- pred_meta %>% rename(pred_area = pred_trans_area)
# want to aggregate depth by mean and area by sum: splitting up
# remove area column
pred_depth <- pred_meta[,c(1:4)]
# aggregate depth rows by session
pred_depth <- aggregate(.~session+site+pred_date, pred_depth, mean)
# remove depth column
pred_area <- pred_meta[,c(1:3,5)]
# aggregate area rows by session
pred_area <- aggregate(.~session+site+pred_date, pred_area, sum)
# join roving depth and area to create full roving metadata
pred_meta <- join(pred_depth, pred_area, by = NULL, type = "full",
match = "all")
# join roving metadata to fish, SVC, and transect dataframe
fish_meta <- join(SVCprey_full, pred_meta, by = NULL, type = "left",
match = "first")
# Joining Vertical Relief Data =================================================
# The following section aggregates vertical relief measures to a mean value per
# survey site, then joins these measures to the fish and survey metadata
# dataframe.
# select site and vert relief columns
vert_relief <- vert_relief[,c(1,7)]
# aggregate to site mean
vert_relief <- aggregate(relief_cm~site, vert_relief, mean)
# change KL-P30 to KL-30
vert_relief$site[vert_relief$site == "KL-P30"] <- "KL-30"
# join vertical relief measure to each site
fish_meta <- join(fish_meta, vert_relief, by = NULL, type = "left",
match = "first")
# Joining Species' Trait Data ==================================================
# The following joins adult fish traits used in further analyses to the survey
# metadata and fish dataframe.
# filter for adult lifestage
fish_traits <- filter(traits, lifestage == "adult")
# select relevant columns: latin names, predator presence, nocturnal,
# max_length, position, behaviour, colouration, cryptic_behaviour, shape,
# trophic position
fish_traits <- fish_traits[,c(1:4,7,18,36,38,39,60,61,70,74)]
# rename columns
fish_traits <- fish_traits %>% rename(colouration = colouration_cat3)
fish_traits <- fish_traits %>% rename(species = common_name)
# join fish trait data to meta and fish dataframe
full_dataframe <- join(fish_meta, fish_traits, by = NULL, type = "full",
match = "all")
# remove un-recorded species
sessions <- unique(full_dataframe$session)
length(sessions)
na_values <- which(is.na(full_dataframe), arr.ind=TRUE)
# replace roving NAs with 0s
full_dataframe$pred_date[is.na(full_dataframe$pred_date)] <- "2014-01-01"
full_dataframe$pred_depth[is.na(full_dataframe$pred_depth)] <- 0
full_dataframe$pred_area[is.na(full_dataframe$pred_area)] <- 0
# remove NA values
full_dataframe <- na.omit(full_dataframe)
# Density Calculation ==========================================================
# The following calculates fish densities for each of the three survey types
# (SVC, transect, and roving) for each single observation. It then calculates
# the density differences between each of the survey types for each observation.
# SVC density calculation
full_dataframe$SVC_density <-
full_dataframe$SVC_abundance/full_dataframe$SVC_area
# transect survey density calculation
full_dataframe$prey_density <-
full_dataframe$prey_abundance/full_dataframe$prey_area
# roving survey density calculation
full_dataframe$pred_density <-
full_dataframe$pred_abundance/full_dataframe$pred_area
# SVC - transect density difference calculation
full_dataframe$SVC_prey_difference <-
full_dataframe$SVC_density - full_dataframe$prey_density
# SVC - roving density difference calculation
full_dataframe$SVC_pred_difference <-
full_dataframe$SVC_density - full_dataframe$pred_density
# transect - roving density difference calculation
full_dataframe$prey_pred_difference <-
full_dataframe$prey_density - full_dataframe$pred_density
# Dataframe Edits ==============================================================
# The following provides some additional edits to the full dataframe to produce
# a final version.
# re-order columns
full_dataframe <- full_dataframe[,c(2,1,21:23,3,4,8,10,13,14,21,25:33,9,11,12,
34,15:17,35,18:20,36:39)]
# re-name columns
full_dataframe <- full_dataframe %>% rename(habitat = SVC_habitat)
full_dataframe <- full_dataframe %>% rename(species_order = order)
# SVC vs. Transect Survey Dataframe ============================================
# The following creates a dataframe specific to observations within SVC and
# transect surveys.
# select SVC and transect columns
SVCprey <- full_dataframe[,c(1:29,34)]
# calculate total density for SVC and transect surveys
SVCprey$total_density <- SVCprey$SVC_density+SVCprey$prey_density
# remove rows where total density = 0
SVCprey_data <- SVCprey[SVCprey$total_density !=0,]
# remove trumpetfish (only species in order)
SVCprey_data <- SVCprey_data[SVCprey_data$species_order !="Syngnathiformes",]
# remove silversides (only species in order)
SVCprey_data <- SVCprey_data[SVCprey_data$species_order !="Atheriniformes",]
# remove eyed flounder (only depressiform species)
SVCprey_data <- SVCprey_data[SVCprey_data$species_order !="Pleuronectiformes",]
# remove mackerel scad (only pelagic species)
SVCprey_data <- SVCprey_data[SVCprey_data$species !="mackerel scad",]
# remove eels
SVCprey_data <- SVCprey_data[SVCprey_data$species_order !="Anguilliformes",]
# remove sessions with un-matched dates between surveys
SVCprey_data <- SVCprey_data[SVCprey_data$session !=178,]
SVCprey_data <- SVCprey_data[SVCprey_data$session !=179,]
SVCprey_data <- SVCprey_data[SVCprey_data$session !=180,]
SVCprey_data <- SVCprey_data[SVCprey_data$session !=268,]
# SVC vs. Roving Survey Dataframe ==============================================
# The following creates a dataframe specific to observations within SVC and
# roving surveys.
# select SVC and roving columns
SVCpred <- full_dataframe[,c(1:25,30:33,35)]
# filter for species recorded on predator surveys
SVCpred <- filter(SVCpred, predator_presence == 1)
# calculate total density for SVC and roving surveys
SVCpred$total_density <- SVCpred$SVC_density+SVCpred$pred_density
# remove rows where total density = 0
SVCpred_data <- SVCpred[SVCpred$total_density !=0,]
# remove trumpetfish (only species in order)
SVCpred_data <- SVCpred_data[SVCpred_data$species_order !="Syngnathiformes",]
# remove gray snapper (inconsistently reported)
SVCpred_data <- SVCpred_data[SVCpred_data$species !="gray snapper",]
# remove amberjack (only schooling species)
SVCpred_data <- SVCpred_data[SVCpred_data$species !="amberjack",]
# remove black margate (only compressiform species)
# SVCpred_data <- SVCpred_data[SVCpred_data$species !="black margate",]
# remove sessions with un-matched dates between surveys
SVCpred_data <- SVCpred_data[SVCpred_data$session !=178,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=179,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=180,]
# remove sessions with no roving data
SVCpred_data <- SVCpred_data[SVCpred_data$session !=264,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=265,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=266,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=267,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=268,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=269,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=270,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=271,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=272,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=273,]
SVCpred_data <- SVCpred_data[SVCpred_data$session !=274,]
# remove NA values
SVCpred_data <- na.omit(SVCpred_data)
# Density Log Transformation ===================================================
# Density differences between the survey types did not meet normality
# assumptions required for analyses, so the following conducts a log
# transformation of raw densities before taking the difference to improved
# normality.
# calculate log SVC density in SVC vs. transect dataframe
log_SVCdensity <- log(SVCprey_data$SVC_density + 0.001)
# calculate log transect density
log_preydensity <- log(SVCprey_data$prey_density + 0.001)
# histogram of SVC vs. transect log density differences
hist(log_SVCdensity-log_preydensity)
# calculate SVC vs. transect log density difference
SVCprey_data$log_difference <- log_SVCdensity-log_preydensity
# calculate log SVC density in SVC vs. roving dataframe
log_SVCdensity2 <- log(SVCpred_data$SVC_density + 0.001)
# calculate log roving density
log_preddensity <- log(SVCpred_data$pred_density + 0.001)
# histogram of SVC vs. roving log density differences
hist(log_SVCdensity2-log_preddensity)
# calculate SVC vs. roving log density difference
SVCpred_data$log_difference <- log_SVCdensity2-log_preddensity
# Average Depth Calculation ====================================================
# The following calculates average depth values for each session between the two
# survey types in each dataframe (SVC vs. transect and SVC vs. roving).
# SVC vs. transect dataframe average depth calculation
SVCprey_data$average_depth <- (SVCprey_data$SVC_max_depth +
SVCprey_data$prey_depth)/2
# SVC vs. roving dataframe average depth calculation
SVCpred_data$average_depth <- (SVCpred_data$SVC_max_depth +
SVCpred_data$pred_depth)/2
# export SVC vs. transect survey dataframe
write_csv(SVCprey_data, here("./dataframes/SVCprey_dataframe.csv"))
# export SVC vs. roving survey dataframe
write_csv(SVCpred_data, here("./dataframes/SVCpred_dataframe.csv"))
|
# Run sudo R, add packages to install
packages <- c("mlr", "devtools", "tableone", "jtools", "ggstance", "kernlab", "randomForest", "reshape2", "GGally", "FactoMineR")
install.packages(packages, lib="/usr/local/lib/R/site-library")
devtools::install_github("vqv/ggbiplot")
devtools::install_github("factoextra")
|
/pkg_install.R
|
no_license
|
mazzottidr/cursoML2018
|
R
| false | false | 316 |
r
|
# Run sudo R, add packages to install
packages <- c("mlr", "devtools", "tableone", "jtools", "ggstance", "kernlab", "randomForest", "reshape2", "GGally", "FactoMineR")
install.packages(packages, lib="/usr/local/lib/R/site-library")
devtools::install_github("vqv/ggbiplot")
devtools::install_github("factoextra")
|
LognormalLikelihood <- function(x, mu, sigma){
z <- (log(x) - mu) / sigma
lnL <- -z^2/2 - log(x) - log(sigma)
sum(lnL)
}
#' @title CalcLognormalLikelihood
#'
#' @param data A vector of sample observations
#' @param mean_range A vector with values of the mean
#' @param CV_range A vector with CV values to be tested
#' @param length.out If the parameter ranges
#'
#' @details
#'
#' The ranges for mean and CV may be of any arbitrary length. If they are a given as a vector of length 2, the function will
#' assume that this represents the bounding range of a sequence of values. In this instance, a sequence of length equal to the
#' length.out parameter will be generated.
#'
#' @include Params.R
#'
#' @examples
#' my_likelihood <- CalcLognormalLikelihood(somedata, mean_range = c(50e3, 150e3), CV_range = c(0.3, 1.8))
#'
#' @export
#'
#' @importFrom purrrlyr by_row
#'
CalcLognormalLikelihood <- function(data, mean_range, CV_range, length.out = 100){
if (!is.numeric(data)) stop("data input must be numeric")
if (any(data <= 0)) stop("data input must be greater than zero")
if (any(mean_range <= 0 )) stop("mean_range must be greater than zero")
if (any(CV_range <= 0 )) stop("CV_range must be greater than zero")
if (length(mean_range) == 2) {
mean_range <- seq(from = mean_range[1], to = mean_range[2], length.out = length.out)
}
if (length(CV_range) == 2) {
CV_range <- seq(from = CV_range[1], to = CV_range[2], length.out = length.out)
}
dfL <- expand.grid(mean_range, CV_range)
rownames(dfL) <- NULL
names(dfL) <- c("Mean", "CV")
dfL <- purrrlyr::by_row(
dfL
, .to = "Likelihood"
, .collate = "rows"
, function(x){
lstParams <- LognormalParams(x$Mean, x$CV)
LognormalLikelihood(data, lstParams$mu, lstParams$sigma)
})
dfL
}
|
/R/Likelihood.R
|
no_license
|
PirateGrunt/momentus
|
R
| false | false | 1,812 |
r
|
LognormalLikelihood <- function(x, mu, sigma){
z <- (log(x) - mu) / sigma
lnL <- -z^2/2 - log(x) - log(sigma)
sum(lnL)
}
#' @title CalcLognormalLikelihood
#'
#' @param data A vector of sample observations
#' @param mean_range A vector with values of the mean
#' @param CV_range A vector with CV values to be tested
#' @param length.out If the parameter ranges
#'
#' @details
#'
#' The ranges for mean and CV may be of any arbitrary length. If they are a given as a vector of length 2, the function will
#' assume that this represents the bounding range of a sequence of values. In this instance, a sequence of length equal to the
#' length.out parameter will be generated.
#'
#' @include Params.R
#'
#' @examples
#' my_likelihood <- CalcLognormalLikelihood(somedata, mean_range = c(50e3, 150e3), CV_range = c(0.3, 1.8))
#'
#' @export
#'
#' @importFrom purrrlyr by_row
#'
CalcLognormalLikelihood <- function(data, mean_range, CV_range, length.out = 100){
if (!is.numeric(data)) stop("data input must be numeric")
if (any(data <= 0)) stop("data input must be greater than zero")
if (any(mean_range <= 0 )) stop("mean_range must be greater than zero")
if (any(CV_range <= 0 )) stop("CV_range must be greater than zero")
if (length(mean_range) == 2) {
mean_range <- seq(from = mean_range[1], to = mean_range[2], length.out = length.out)
}
if (length(CV_range) == 2) {
CV_range <- seq(from = CV_range[1], to = CV_range[2], length.out = length.out)
}
dfL <- expand.grid(mean_range, CV_range)
rownames(dfL) <- NULL
names(dfL) <- c("Mean", "CV")
dfL <- purrrlyr::by_row(
dfL
, .to = "Likelihood"
, .collate = "rows"
, function(x){
lstParams <- LognormalParams(x$Mean, x$CV)
LognormalLikelihood(data, lstParams$mu, lstParams$sigma)
})
dfL
}
|
#' @title Print Loess bootstrap model
#' @description print method for class "loess.boot"
#' @param x Object of class loess.boot
#' @param ... Ignored
#'
#' @method print loess.boot
#' @export
print.loess.boot <- function(x, ...) {
cat("Number of permutations: ", x$nreps, "\n", sep="")
cat("Loess alpha (span) parameter: ", x$span, "\n", sep="")
cat("Polynomial degree: ", x$degree, "\n", sep="")
cat("Data normalized: ", x$normalize, "\n", sep="")
cat("Model distribution: ", x$family, "\n", sep="")
cat("parametric model: ", x$parametric, "\n", sep="")
}
|
/spatialEco/R/print.loess.boot.R
|
no_license
|
albrizre/spatstat.revdep
|
R
| false | false | 574 |
r
|
#' @title Print Loess bootstrap model
#' @description print method for class "loess.boot"
#' @param x Object of class loess.boot
#' @param ... Ignored
#'
#' @method print loess.boot
#' @export
print.loess.boot <- function(x, ...) {
cat("Number of permutations: ", x$nreps, "\n", sep="")
cat("Loess alpha (span) parameter: ", x$span, "\n", sep="")
cat("Polynomial degree: ", x$degree, "\n", sep="")
cat("Data normalized: ", x$normalize, "\n", sep="")
cat("Model distribution: ", x$family, "\n", sep="")
cat("parametric model: ", x$parametric, "\n", sep="")
}
|
testlist <- list(id = NULL, score = NULL, id = NULL, booklet_id = integer(0), item_score = c(NA, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), person_id = integer(0))
result <- do.call(dexterMST:::mutate_booklet_score,testlist)
str(result)
|
/dexterMST/inst/testfiles/mutate_booklet_score/libFuzzer_mutate_booklet_score/mutate_booklet_score_valgrind_files/1612727633-test.R
|
no_license
|
akhikolla/updatedatatype-list1
|
R
| false | false | 540 |
r
|
testlist <- list(id = NULL, score = NULL, id = NULL, booklet_id = integer(0), item_score = c(NA, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), person_id = integer(0))
result <- do.call(dexterMST:::mutate_booklet_score,testlist)
str(result)
|
## Calculate square error projection from sscden objects
project.sscden1 <- function(object,include,...)
{
include <- unique(include)
term <- object$terms
mf <- object$mf
nobs <- dim(mf)[1]
xnames <- object$xnames
ynames <- object$ynames
xx.wt <- object$xx.wt
nx <- length(xx.wt)
xx <- mf[!object$x.dup.ind,xnames,drop=FALSE]
qd.pt <- object$rho$env$qd.pt
qd.wt <- object$rho$env$qd.wt
rho.d <- t(object$rho$fun(xx,qd.pt,object$rho$env,outer=TRUE))
rho.wt <- rho.d*qd.wt
rho.d <- t(t(log(rho.d))-apply(log(rho.d)*rho.wt,2,sum))
nmesh <- length(qd.wt)
ns <- length(object$id.s)
nr <- length(object$id.r)
nbasis <- length(object$id.basis)
s.rho <- ss <- 0
r.rho <- matrix(0,nbasis,nr)
sr <- array(0,c(ns,nbasis,nr))
rr <- array(0,c(nbasis,nbasis,nr,nr))
rho2 <- sum(xx.wt*apply(rho.d^2*rho.wt,2,sum))
for (k in 1:nx) {
id.x <- (1:nobs)[!object$x.dup.ind][k]
qd.s <- NULL
qd.r <- list(NULL)
iq <- 0
for (label in term$labels) {
vlist <- term[[label]]$vlist
x.list <- xnames[xnames%in%vlist]
y.list <- ynames[ynames%in%vlist]
xy.basis <- mf[object$id.basis,vlist]
qd.xy <- data.frame(matrix(0,nmesh,length(vlist)))
names(qd.xy) <- vlist
qd.xy[,y.list] <- qd.pt[,y.list]
if (length(x.list)) xx <- mf[rep(id.x,nmesh),x.list,drop=FALSE]
else xx <- NULL
nphi <- term[[label]]$nphi
nrk <- term[[label]]$nrk
if (nphi) {
phi <- term[[label]]$phi
for (i in 1:nphi) {
if (is.null(xx)) {
qd.s.wk <- phi$fun(qd.xy[,,drop=TRUE],nu=i,env=phi$env)
qd.s <- cbind(qd.s,qd.s.wk)
}
else {
if (length(y.list)>0) {
qd.xy[,x.list] <- xx
qd.s <- cbind(qd.s,phi$fun(qd.xy,i,phi$env))
}
}
}
}
if (nrk) {
rk <- term[[label]]$rk
for (i in 1:nrk) {
if (is.null(xx)) {
qd.r.wk <- rk$fun(qd.xy[,,drop=TRUE],xy.basis,nu=i,env=rk$env,TRUE)
iq <- iq+1
qd.r[[iq]] <- qd.r.wk
}
else {
if (length(y.list)>0) {
qd.xy[,x.list] <- xx
iq <- iq+1
qd.r[[iq]] <- rk$fun(qd.xy,xy.basis,i,rk$env,TRUE)
}
}
}
}
}
if (ns) {
qd.s <- sweep(qd.s,2,apply(qd.s*rho.wt[,k],2,sum))
s.rho <- s.rho + xx.wt[k]*apply(qd.s*rho.d[,k]*rho.wt[,k],2,sum)
ss <- ss + xx.wt[k]*t(rho.wt[,k]*qd.s)%*%qd.s
}
for (i in 1:iq) {
qd.r[[i]] <- sweep(qd.r[[i]],2,apply(qd.r[[i]]*rho.wt[,k],2,sum))
r.rho[,i] <- r.rho[,i] + xx.wt[k]*apply(qd.r[[i]]*rho.d[,k]*rho.wt[,k],2,sum)
if (ns) sr[,,i] <- sr[,,i] + xx.wt[k]*t(rho.wt[,k]*qd.s)%*%qd.r[[i]]
for (j in 1:i) {
rr.wk <- xx.wt[k]*t(rho.wt[,k]*qd.r[[i]])%*%qd.r[[j]]
rr[,,i,j] <- rr[,,i,j] + rr.wk
if (i-j) rr[,,j,i] <- rr[,,j,i] + t(rr.wk)
}
}
}
## evaluate full model
if (ns) d <- object$d[object$id.s]
c <- object$c
theta <- object$theta[object$id.r]
nq <- length(theta)
if (ns) s.eta <- ss%*%d
r.eta <- tmp <- NULL
r.rho.wk <- sr.wk <- rr.wk <- 0
for (i in 1:nq) {
tmp <- c(tmp,10^(2*theta[i])*sum(diag(rr[,,i,i])))
if (ns) {
s.eta <- s.eta + 10^theta[i]*sr[,,i]%*%c
if (length(d)==1) r.eta.wk <- sr[,,i]*d
else r.eta.wk <- t(sr[,,i])%*%d
sr.wk <- sr.wk + 10^theta[i]*sr[,,i]
}
else r.eta.wk <- 0
r.rho.wk <- r.rho.wk + 10^theta[i]*r.rho[,i]
for (j in 1:nq) {
r.eta.wk <- r.eta.wk + 10^theta[j]*rr[,,i,j]%*%c
rr.wk <- rr.wk + 10^(theta[i]+theta[j])*rr[,,i,j]
}
r.eta <- cbind(r.eta,r.eta.wk)
}
rho.eta <- sum(r.rho.wk*c)
if (ns) rho.eta <- rho.eta + sum(r.rho.wk*c)
eta2 <- sum(c*(rr.wk%*%c))
if (ns) eta2 <- eta2 + sum(d*(ss%*%d)) + 2*sum(d*(sr.wk%*%c))
mse <- eta2 + rho2 + 2*rho.eta
## extract terms in subspace
id.s <- id.r <- NULL
for (label in include) {
id.s <- c(id.s,object$id.s.list[[label]])
id.r <- c(id.r,object$id.r.list[[label]])
}
if (is.null(id.s)&is.null(id.r))
stop("gss error in project.sscden1: include some terms")
if (!all(id.s%in%object$id.s)|!all(id.r%in%object$id.r))
stop("gss error in project.sscden1: included terms are not in the model")
## calculate projection
rkl <- function(theta1=NULL) {
theta.wk <- 1:nq0
theta.wk[fix] <- theta[fix]
if (nq0-1) theta.wk[-fix] <- theta1
##
id.s0 <- (1:length(object$id.s))[object$id.s%in%id.s]
id.r0 <- (1:length(object$id.r))[object$id.r%in%id.r]
if (length(id.s0)) ss.wk <- ss[id.s0,id.s0,drop=FALSE]
if (length(id.r0)) {
r.eta.wk <- rr.wk <- 0
sr.wk <- matrix(0,length(id.s),nbasis)
for (i in 1:length(id.r0)) {
r.eta.wk <- r.eta.wk + 10^theta.wk[i]*r.eta[,id.r0[i]]
if (length(id.s0)) sr.wk <- sr.wk + 10^theta.wk[i]*sr[id.s0,,id.r0[i]]
for (j in 1:length(id.r0)) {
rr.wk <- rr.wk + 10^(theta.wk[i]+theta.wk[j])*rr[,,id.r0[i],id.r0[j]]
}
}
if (length(id.s0)) {
v <- cbind(rbind(ss.wk,t(sr.wk)),rbind(sr.wk,rr.wk))
mu <- c(s.eta[id.s0],r.eta.wk)
}
else {
v <- rbind(sr.wk,rr.wk)
mu <- r.eta.wk
}
}
else {
v <- ss.wk
mu <- s.eta[id.s0]
}
nn <- length(mu)
z <- chol(v,pivot=TRUE)
v <- z
rkv <- attr(z,"rank")
m.eps <- .Machine$double.eps
while (v[rkv,rkv]<2*sqrt(m.eps)*v[1,1]) rkv <- rkv - 1
if (rkv<nn) v[(1:nn)>rkv,(1:nn)>rkv] <- diag(v[1,1],nn-rkv)
mu <- backsolve(v,mu[attr(z,"pivot")],transpose=TRUE)
eta2 - sum(mu[1:rkv]^2)
}
cv.wk <- function(theta) cv.scale*rkl(theta)+cv.shift
## initialization
fix <- rev(order(tmp[id.r]))[1]
theta <- object$theta[id.r]
## projection
nq0 <- length(id.r)
if (nq0>1) {
if (object$skip.iter) se <- rkl(theta[-fix])
else {
if (nq0-2) {
## scale and shift cv
tmp <- abs(rkl(theta[-fix]))
cv.scale <- 1
cv.shift <- 0
if (tmp<1&tmp>10^(-4)) {
cv.scale <- 10/tmp
cv.shift <- 0
}
if (tmp<10^(-4)) {
cv.scale <- 10^2
cv.shift <- 10
}
zz <- nlm(cv.wk,theta[-fix],stepmax=.5,ndigit=7)
}
else {
the.wk <- theta[-fix]
repeat {
mn <- the.wk-1
mx <- the.wk+1
zz <- nlm0(rkl,c(mn,mx))
if (min(zz$est-mn,mx-zz$est)>=1e-3) break
else the.wk <- zz$est
}
}
se <- rkl(zz$est)
}
}
else se <- rkl()
list(ratio=se/mse,se=se)
}
|
/R/project.sscden1.R
|
no_license
|
weirichd/gss
|
R
| false | false | 7,797 |
r
|
## Calculate square error projection from sscden objects
project.sscden1 <- function(object,include,...)
{
include <- unique(include)
term <- object$terms
mf <- object$mf
nobs <- dim(mf)[1]
xnames <- object$xnames
ynames <- object$ynames
xx.wt <- object$xx.wt
nx <- length(xx.wt)
xx <- mf[!object$x.dup.ind,xnames,drop=FALSE]
qd.pt <- object$rho$env$qd.pt
qd.wt <- object$rho$env$qd.wt
rho.d <- t(object$rho$fun(xx,qd.pt,object$rho$env,outer=TRUE))
rho.wt <- rho.d*qd.wt
rho.d <- t(t(log(rho.d))-apply(log(rho.d)*rho.wt,2,sum))
nmesh <- length(qd.wt)
ns <- length(object$id.s)
nr <- length(object$id.r)
nbasis <- length(object$id.basis)
s.rho <- ss <- 0
r.rho <- matrix(0,nbasis,nr)
sr <- array(0,c(ns,nbasis,nr))
rr <- array(0,c(nbasis,nbasis,nr,nr))
rho2 <- sum(xx.wt*apply(rho.d^2*rho.wt,2,sum))
for (k in 1:nx) {
id.x <- (1:nobs)[!object$x.dup.ind][k]
qd.s <- NULL
qd.r <- list(NULL)
iq <- 0
for (label in term$labels) {
vlist <- term[[label]]$vlist
x.list <- xnames[xnames%in%vlist]
y.list <- ynames[ynames%in%vlist]
xy.basis <- mf[object$id.basis,vlist]
qd.xy <- data.frame(matrix(0,nmesh,length(vlist)))
names(qd.xy) <- vlist
qd.xy[,y.list] <- qd.pt[,y.list]
if (length(x.list)) xx <- mf[rep(id.x,nmesh),x.list,drop=FALSE]
else xx <- NULL
nphi <- term[[label]]$nphi
nrk <- term[[label]]$nrk
if (nphi) {
phi <- term[[label]]$phi
for (i in 1:nphi) {
if (is.null(xx)) {
qd.s.wk <- phi$fun(qd.xy[,,drop=TRUE],nu=i,env=phi$env)
qd.s <- cbind(qd.s,qd.s.wk)
}
else {
if (length(y.list)>0) {
qd.xy[,x.list] <- xx
qd.s <- cbind(qd.s,phi$fun(qd.xy,i,phi$env))
}
}
}
}
if (nrk) {
rk <- term[[label]]$rk
for (i in 1:nrk) {
if (is.null(xx)) {
qd.r.wk <- rk$fun(qd.xy[,,drop=TRUE],xy.basis,nu=i,env=rk$env,TRUE)
iq <- iq+1
qd.r[[iq]] <- qd.r.wk
}
else {
if (length(y.list)>0) {
qd.xy[,x.list] <- xx
iq <- iq+1
qd.r[[iq]] <- rk$fun(qd.xy,xy.basis,i,rk$env,TRUE)
}
}
}
}
}
if (ns) {
qd.s <- sweep(qd.s,2,apply(qd.s*rho.wt[,k],2,sum))
s.rho <- s.rho + xx.wt[k]*apply(qd.s*rho.d[,k]*rho.wt[,k],2,sum)
ss <- ss + xx.wt[k]*t(rho.wt[,k]*qd.s)%*%qd.s
}
for (i in 1:iq) {
qd.r[[i]] <- sweep(qd.r[[i]],2,apply(qd.r[[i]]*rho.wt[,k],2,sum))
r.rho[,i] <- r.rho[,i] + xx.wt[k]*apply(qd.r[[i]]*rho.d[,k]*rho.wt[,k],2,sum)
if (ns) sr[,,i] <- sr[,,i] + xx.wt[k]*t(rho.wt[,k]*qd.s)%*%qd.r[[i]]
for (j in 1:i) {
rr.wk <- xx.wt[k]*t(rho.wt[,k]*qd.r[[i]])%*%qd.r[[j]]
rr[,,i,j] <- rr[,,i,j] + rr.wk
if (i-j) rr[,,j,i] <- rr[,,j,i] + t(rr.wk)
}
}
}
## evaluate full model
if (ns) d <- object$d[object$id.s]
c <- object$c
theta <- object$theta[object$id.r]
nq <- length(theta)
if (ns) s.eta <- ss%*%d
r.eta <- tmp <- NULL
r.rho.wk <- sr.wk <- rr.wk <- 0
for (i in 1:nq) {
tmp <- c(tmp,10^(2*theta[i])*sum(diag(rr[,,i,i])))
if (ns) {
s.eta <- s.eta + 10^theta[i]*sr[,,i]%*%c
if (length(d)==1) r.eta.wk <- sr[,,i]*d
else r.eta.wk <- t(sr[,,i])%*%d
sr.wk <- sr.wk + 10^theta[i]*sr[,,i]
}
else r.eta.wk <- 0
r.rho.wk <- r.rho.wk + 10^theta[i]*r.rho[,i]
for (j in 1:nq) {
r.eta.wk <- r.eta.wk + 10^theta[j]*rr[,,i,j]%*%c
rr.wk <- rr.wk + 10^(theta[i]+theta[j])*rr[,,i,j]
}
r.eta <- cbind(r.eta,r.eta.wk)
}
rho.eta <- sum(r.rho.wk*c)
if (ns) rho.eta <- rho.eta + sum(r.rho.wk*c)
eta2 <- sum(c*(rr.wk%*%c))
if (ns) eta2 <- eta2 + sum(d*(ss%*%d)) + 2*sum(d*(sr.wk%*%c))
mse <- eta2 + rho2 + 2*rho.eta
## extract terms in subspace
id.s <- id.r <- NULL
for (label in include) {
id.s <- c(id.s,object$id.s.list[[label]])
id.r <- c(id.r,object$id.r.list[[label]])
}
if (is.null(id.s)&is.null(id.r))
stop("gss error in project.sscden1: include some terms")
if (!all(id.s%in%object$id.s)|!all(id.r%in%object$id.r))
stop("gss error in project.sscden1: included terms are not in the model")
## calculate projection
rkl <- function(theta1=NULL) {
theta.wk <- 1:nq0
theta.wk[fix] <- theta[fix]
if (nq0-1) theta.wk[-fix] <- theta1
##
id.s0 <- (1:length(object$id.s))[object$id.s%in%id.s]
id.r0 <- (1:length(object$id.r))[object$id.r%in%id.r]
if (length(id.s0)) ss.wk <- ss[id.s0,id.s0,drop=FALSE]
if (length(id.r0)) {
r.eta.wk <- rr.wk <- 0
sr.wk <- matrix(0,length(id.s),nbasis)
for (i in 1:length(id.r0)) {
r.eta.wk <- r.eta.wk + 10^theta.wk[i]*r.eta[,id.r0[i]]
if (length(id.s0)) sr.wk <- sr.wk + 10^theta.wk[i]*sr[id.s0,,id.r0[i]]
for (j in 1:length(id.r0)) {
rr.wk <- rr.wk + 10^(theta.wk[i]+theta.wk[j])*rr[,,id.r0[i],id.r0[j]]
}
}
if (length(id.s0)) {
v <- cbind(rbind(ss.wk,t(sr.wk)),rbind(sr.wk,rr.wk))
mu <- c(s.eta[id.s0],r.eta.wk)
}
else {
v <- rbind(sr.wk,rr.wk)
mu <- r.eta.wk
}
}
else {
v <- ss.wk
mu <- s.eta[id.s0]
}
nn <- length(mu)
z <- chol(v,pivot=TRUE)
v <- z
rkv <- attr(z,"rank")
m.eps <- .Machine$double.eps
while (v[rkv,rkv]<2*sqrt(m.eps)*v[1,1]) rkv <- rkv - 1
if (rkv<nn) v[(1:nn)>rkv,(1:nn)>rkv] <- diag(v[1,1],nn-rkv)
mu <- backsolve(v,mu[attr(z,"pivot")],transpose=TRUE)
eta2 - sum(mu[1:rkv]^2)
}
cv.wk <- function(theta) cv.scale*rkl(theta)+cv.shift
## initialization
fix <- rev(order(tmp[id.r]))[1]
theta <- object$theta[id.r]
## projection
nq0 <- length(id.r)
if (nq0>1) {
if (object$skip.iter) se <- rkl(theta[-fix])
else {
if (nq0-2) {
## scale and shift cv
tmp <- abs(rkl(theta[-fix]))
cv.scale <- 1
cv.shift <- 0
if (tmp<1&tmp>10^(-4)) {
cv.scale <- 10/tmp
cv.shift <- 0
}
if (tmp<10^(-4)) {
cv.scale <- 10^2
cv.shift <- 10
}
zz <- nlm(cv.wk,theta[-fix],stepmax=.5,ndigit=7)
}
else {
the.wk <- theta[-fix]
repeat {
mn <- the.wk-1
mx <- the.wk+1
zz <- nlm0(rkl,c(mn,mx))
if (min(zz$est-mn,mx-zz$est)>=1e-3) break
else the.wk <- zz$est
}
}
se <- rkl(zz$est)
}
}
else se <- rkl()
list(ratio=se/mse,se=se)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_functions.R
\name{omicsContrib}
\alias{omicsContrib}
\title{Plot the omics contribution per MFA axis and the overall weighted
contribution}
\usage{
omicsContrib(
padma_obj,
max_dim = min(10, nrow(MFA_results(padma_obj)$eig)),
ggplot = TRUE
)
}
\arguments{
\item{padma_obj}{Output from running the \code{padma} function
(with \code{'full_results = TRUE'})}
\item{max_dim}{Maximum dimension number of the MFA to be plotted}
\item{ggplot}{If \code{TRUE}, use \code{ggplot2} for plotting (and
\code{cowplot} for combining ggplots)}
}
\value{
Barplots of percent variance contribution, optionally of class
\code{ggplot}.
}
\description{
Plot barplots indicating the percent contribution of each omics to each MFA
dimension, as well as the overall weighted (by eigenvalue) percent
contribution to the full analysis.
}
\examples{
LUAD_subset <- padma::LUAD_subset
## Create MultiAssayExperiment object with LUAD data
omics_data <-
list(rnaseq = LUAD_subset$rnaseq,
methyl = LUAD_subset$methyl,
mirna = LUAD_subset$mirna,
cna = LUAD_subset$cna)
pheno_data <-
data.frame(LUAD_subset$clinical,
row.names = LUAD_subset$clinical$bcr_patient_barcode)
mae <-
suppressMessages(
MultiAssayExperiment::MultiAssayExperiment(
experiments = omics_data, colData = pheno_data))
## Run padma
run_padma <-
padma(mae, gene_map = padma::mirtarbase,
pathway_name = "c2_cp_BIOCARTA_D4GDI_PATHWAY", verbose = FALSE)
summary(run_padma)
## padma plots
factorMap(run_padma, dim_x = 1, dim_y = 2)
factorMap(run_padma, dim_x = 1, dim_y = 2,
partial_id = "TCGA-78-7536")
omicsContrib(run_padma, max_dim = 10)
}
|
/man/omicsContrib.Rd
|
no_license
|
lwaldron/padma
|
R
| false | true | 1,750 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_functions.R
\name{omicsContrib}
\alias{omicsContrib}
\title{Plot the omics contribution per MFA axis and the overall weighted
contribution}
\usage{
omicsContrib(
padma_obj,
max_dim = min(10, nrow(MFA_results(padma_obj)$eig)),
ggplot = TRUE
)
}
\arguments{
\item{padma_obj}{Output from running the \code{padma} function
(with \code{'full_results = TRUE'})}
\item{max_dim}{Maximum dimension number of the MFA to be plotted}
\item{ggplot}{If \code{TRUE}, use \code{ggplot2} for plotting (and
\code{cowplot} for combining ggplots)}
}
\value{
Barplots of percent variance contribution, optionally of class
\code{ggplot}.
}
\description{
Plot barplots indicating the percent contribution of each omics to each MFA
dimension, as well as the overall weighted (by eigenvalue) percent
contribution to the full analysis.
}
\examples{
LUAD_subset <- padma::LUAD_subset
## Create MultiAssayExperiment object with LUAD data
omics_data <-
list(rnaseq = LUAD_subset$rnaseq,
methyl = LUAD_subset$methyl,
mirna = LUAD_subset$mirna,
cna = LUAD_subset$cna)
pheno_data <-
data.frame(LUAD_subset$clinical,
row.names = LUAD_subset$clinical$bcr_patient_barcode)
mae <-
suppressMessages(
MultiAssayExperiment::MultiAssayExperiment(
experiments = omics_data, colData = pheno_data))
## Run padma
run_padma <-
padma(mae, gene_map = padma::mirtarbase,
pathway_name = "c2_cp_BIOCARTA_D4GDI_PATHWAY", verbose = FALSE)
summary(run_padma)
## padma plots
factorMap(run_padma, dim_x = 1, dim_y = 2)
factorMap(run_padma, dim_x = 1, dim_y = 2,
partial_id = "TCGA-78-7536")
omicsContrib(run_padma, max_dim = 10)
}
|
# ----------------------------------------------
# Caitlin O'Brien-Carelli
# Prep incidence and mortality data from WHO/UNAIDS
# Set function arguments to determine which data sources
# ----------------------------------------------
# --------------------
# Set up R
rm(list=ls())
library(data.table)
library(ggplot2)
library(RColorBrewer)
library(stringr)
options(scipen=999)
# --------------------
# detect the user
user = Sys.info()[['user']]
#--------------------------------
# set directories
# detect if operating on windows or on the cluster
j = ifelse(Sys.info()[1]=='Windows', 'J:', '/home/j')
# set the directory for input and output
dir = paste0(j, '/Project/Evaluation/GF/impact_evaluation/synthesis_epidemiology/')
code_dir = paste0('C:/Users/', user, '/local/gf/impact_evaluation/gbd_epidemiology/')
outDir = paste0(j, '/Project/Evaluation/GF/impact_evaluation/synthesis_epidemiology/')
#-----------------------------------------------------------------
# read in who tb
tb = fread(paste0(dir, 'raw_data/who_tb/TB_burden_countries_2020-01-21.csv'))
#---------------------------------
# before subsetting, get a global population estimate
global_pop = tb[ ,.(population = sum(e_pop_num)), by=year]
global_pop[ ,location:='Global'] # use for hiv estimates
#---------------------------------
# calculate global incidence and mortality for tb
# merge in later
# sum everything to the global level, shape long and subset
glob_tb = tb[ ,lapply(.SD, sum, na.rm=T), .SDcols=7:50, by=year]
glob_tb = melt(glob_tb, id.vars='year')
keep_glob = c('year', 'e_pop_num', 'e_inc_num', 'e_inc_num_hi', 'e_inc_num_lo',
'e_mort_num', 'e_mort_num_hi', 'e_mort_num_lo')
glob_tb = glob_tb[variable %in% keep_glob]
# shape wide and calculate
glob_tb = dcast(glob_tb, year~variable)
glob_tb[ ,pop_factor:=e_pop_num/100000]
glob_tb_rates = glob_tb[ ,lapply(.SD, function(x) x = x/pop_factor), .SDcols=3:8, by=.(year, pop_factor)]
glob_tb_rates[ ,pop_factor:=NULL]
glob_tb[ ,c('pop_factor', 'e_pop_num'):=NULL]
# keep mortality and incidence rates and counts
setnames(glob_tb_rates, c("year", "e_inc_num", "e_inc_num_lo",
"e_inc_num_hi", "e_mort_num", "e_mort_num_lo", "e_mort_num_hi"),
c('year', 'e_inc_100k', 'e_inc_100k_lo', 'e_inc_100k_hi',
'e_mort_100k', 'e_mort_100k_lo', 'e_mort_100k_hi'))
glob_tb = merge(glob_tb, glob_tb_rates)
glob_tb = melt(glob_tb, id.vars = 'year')
glob_tb[ ,location:='Global']
#--------------------------------
# subset and rename variables
countries = c("Cambodia", "Democratic Republic of the Congo",
"Guatemala", "Mozambique", "Myanmar", "Senegal", "Sudan", "Uganda", "Global")
# subset to relevant countries, does not include global estimates
tb = tb[country %in% countries]
# --------------------------------------
# extract population data and use later to calculate hiv mortality rates
pop = tb[ ,.(population = e_pop_num), by = .(country, year)]
setnames(pop, 'country', 'location')
pop = rbind(pop, global_pop)
# --------------------------------------
# PREP TB DATA
# drop unecessary variables and shape long
tb[ ,c('iso2', 'iso3', 'iso_numeric', 'g_whoregion'):=NULL]
tb = melt(tb, id.vars = c('country', 'year')) # some are numerics and some integers
# keep mortality and incidence rates and counts
keep = c('country', 'year', 'e_inc_100k', 'e_inc_100k_hi', 'e_inc_100k_lo',
'e_mort_100k', 'e_mort_100k_hi', 'e_mort_100k_lo',
'e_inc_num', 'e_inc_num_hi', 'e_inc_num_lo',
'e_mort_num', 'e_mort_num_hi', 'e_mort_num_lo')
tb = tb[variable %in% keep]
setnames(tb, 'country', 'location')
# add in global data
tb = rbind(tb, glob_tb)
# format to match
tb[grepl('hi', variable), bound:='upper']
tb[grepl('lo', variable), bound:='lower']
tb[!grepl('lo', variable) & !grepl('hi', variable), bound:='val']
# designate mortality and incidence, rates and counts
tb[grepl('mort', variable), measure:='Deaths']
tb[grepl('inc', variable), measure:='Incidence']
tb[grepl('100k', variable), metric:='Rate']
tb[!grepl('100k', variable), metric:='Number']
# add age
tb[metric=='Number', age:='All Ages']
tb[metric=='Rate', age:='Age-standardized']
# add variables not included in the data
tb[ ,sex:='Both']
tb[ ,cause:='Tuberculosis']
tb[ ,variable:=NULL]
tb = dcast(tb, measure+location+sex+age+cause+metric+year~bound)
#---------------------
# output prepped tb data
saveRDS(tb, paste0(outDir, 'prepped_data/who_tb_2000_2018_prepped.rds'))
#---------------------
#-------------------------------------------------------
# PREP HIV DATA
#------------------------
# upload and prep unaids data
hiv = fread(paste0(dir, 'raw_data/unaids/New HIV infections_HIV incidence per 1000 population_Population_ All ages.csv'))
hiv2 = fread(paste0(dir, 'raw_data/unaids/AIDS-related deaths_Number of AIDS-related deaths_Population_ All ages.csv'))
hiv3 = fread(paste0(dir, 'raw_data/unaids/new_hiv_infections_counts.csv'))
#----------------
# write a function to format the unaids data
format = function(x) {
# rename the columns and drop the first row as it imports weird
name_list = as.character(x[1])
setnames(x, name_list)
x = x[-1]
# subset and shape long
x = x[Country %in% countries]
setnames(x, 'Country', 'location')
x = melt(x, id.vars='location')
return(x)}
#----------------
#------------------------
# format the data and bind them together
hiv = format(hiv)
hiv2 = format(hiv2)
hiv3 = format(hiv3)
hiv[ ,measure:='Incidence']
hiv[ , metric:='Rate']
hiv2[ , measure:='Deaths']
hiv2[ , metric:='Number']
#--------------------------
hiv = rbind(hiv, hiv2)
hiv[metric=='Number', age:='All Ages']
hiv[metric=='Rate', age:='Age-standardized']# check if age standardized
# shape wide
hiv[grepl('upper', variable), bound:='upper']
hiv[grepl('lower', variable), bound:='lower']
hiv[!grepl('lower', variable) & !grepl('upper', variable), bound:='val']
# split and add year
hiv$year = unlist(lapply(str_split(hiv$variable, '_'), '[', 1))
hiv[ ,variable:=NULL]
hiv = dcast(hiv, location+measure+metric+age+year~bound)
#------------------------
# new infection counts need special wrangling
# reset names and develop bounds
setnames(hiv3, 'variable', 'year')
hiv3$val = trimws(unlist(lapply(str_split(hiv3$value, '\\['), '[', 1)))
hiv3$lower = trimws(unlist(lapply(str_split(hiv3$value, '\\['), '[', 2)))
hiv3$lower = trimws(unlist(lapply(str_split(hiv3$lower, '\\-'), '[', 1))) #split twice for lower bound
hiv3$upper = trimws(unlist(lapply(str_split(hiv3$value, '\\['), '[', 2)))
hiv3$upper = trimws(unlist(lapply(str_split(hiv3$upper, '\\-'), '[', 2)))
hiv3[ ,upper:=gsub('\\]', '', upper)]
hiv3[ ,value:=NULL]
# add in identifiers
hiv3[ ,age:='All Ages']
hiv3[ ,measure:='Incidence']
hiv3[ , metric:='Number']
hiv = rbind(hiv, hiv3)
#--------------------------------------------------
# final formatting of numbers for the merge
# if the estimate is listed as "less than," delete from the data
hiv[grep('<', val)][order(location, year)]
hiv = hiv[!grepl('<', val)]
# only one estimate for cambodia has a less than, in 1990
hiv = hiv[!grepl('<', upper)]
# if the lower bound is listed as less than 1,000, estimate as 0
hiv[grepl('<', lower), lower:=0]
# trim the spaces from the values
hiv[ ,val:=as.numeric(gsub(' ', '', val))]
hiv[ ,lower:=as.numeric(gsub(' ', '', lower))]
hiv[ ,upper:=as.numeric(gsub(' ', '', upper))]
# add the cause
hiv[ ,cause:='HIV/AIDS']
hiv[ ,sex:='Both']
# confirm year is a numeric
hiv[, year:=as.numeric(as.character(year))]
#--------------------------
# hiv rates are per 1,000 - convert to rate per 100k
hiv[metric == 'Rate' , val:=100*val]
hiv[metric == 'Rate' , lower:=100*lower]
hiv[metric == 'Rate' , upper:=100*upper]
#--------------------------------------------------
# calculate mortality rates
death_rates = hiv[measure=='Deaths' & 2000 <= year]
death_rates = merge(death_rates, pop, by=c('location','year'))
# create rates
death_rates[ ,population:=(population/100000)]
death_rates[ ,val:=val/population]
death_rates[ ,lower:=lower/population]
death_rates[ ,upper:=upper/population]
death_rates[ ,metric:='Rate']
death_rates[ ,age:='Age-standardized']
death_rates[ ,population:=NULL]
# bind death rates into the data
hiv = rbind(hiv, death_rates)
#--------------------------------------------------
# save the data set and bind tb and hiv together
# output prepped hiv data
saveRDS(hiv, paste0(outDir, 'prepped_data/unaids_hiv_2000_2018_prepped.rds'))
# bind the data sets together
tb_hiv = rbind(tb, hiv)
#-------------------------------------------------------
# prep malaria data
mal = readRDS(paste0(dir, 'raw_data/who_malaria/estimated_malaria_cases_and_deaths_2010_2018.rds'))
mal = mal[country!='South Sudan']
# format the data and shape long
setnames(mal, 'country', 'location')
mal[ ,sex:='Both']
mal[ ,population_at_risk:=NULL]
mal = melt(mal, id.vars=c('location', 'year', 'sex'))
# format to match
mal[grepl('upper', variable), bound:='upper']
mal[grepl('lower', variable), bound:='lower']
mal[grepl('point', variable), bound:='val']
# designate mortality and incidence, rates and counts
mal[grepl('deaths', variable) | grepl('mortality', variable) , measure:='Deaths']
mal[grepl('inc', variable) | grepl('cases', variable), measure:='Incidence']
mal[grepl('inc', variable) | grepl('mortality', variable), metric:='Rate']
mal[grepl('deaths', variable) | grepl('cases', variable), metric:='Number']
# add age
mal[metric=='Number', age:='All Ages']
mal[metric=='Rate', age:='Age-standardized']
# add variables not included in the data
mal[ ,sex:='Both']
mal[ ,cause:='Malaria']
mal[ ,variable:=NULL]
# shape wide
mal = dcast(mal, measure+location+sex+age+cause+metric+year~bound)
# match locations in other data
mal[location=='DRC', location:='Democratic Republic of the Congo']
#--------------------------------------------------
# save the data set and bind tb and hiv together
# output prepped hiv data
saveRDS(mal, paste0(outDir, 'prepped_data/who_malaria_2010_2018_prepped.rds'))
# bind the data sets together
dt = rbind(tb_hiv, mal)
# ensure year is not a factor
dt[ ,year:=as.numeric(as.character(year))]
#------------------------------------------------
# save the final product
saveRDS(dt, paste0(outDir, 'prepped_data/who_unaids_prepped.rds'))
#------------------------------------------------
|
/impact_evaluation/synthesis/1_prep_who_unaids_data.R
|
no_license
|
ihmeuw/gf
|
R
| false | false | 10,343 |
r
|
# ----------------------------------------------
# Caitlin O'Brien-Carelli
# Prep incidence and mortality data from WHO/UNAIDS
# Set function arguments to determine which data sources
# ----------------------------------------------
# --------------------
# Set up R
rm(list=ls())
library(data.table)
library(ggplot2)
library(RColorBrewer)
library(stringr)
options(scipen=999)
# --------------------
# detect the user
user = Sys.info()[['user']]
#--------------------------------
# set directories
# detect if operating on windows or on the cluster
j = ifelse(Sys.info()[1]=='Windows', 'J:', '/home/j')
# set the directory for input and output
dir = paste0(j, '/Project/Evaluation/GF/impact_evaluation/synthesis_epidemiology/')
code_dir = paste0('C:/Users/', user, '/local/gf/impact_evaluation/gbd_epidemiology/')
outDir = paste0(j, '/Project/Evaluation/GF/impact_evaluation/synthesis_epidemiology/')
#-----------------------------------------------------------------
# read in who tb
tb = fread(paste0(dir, 'raw_data/who_tb/TB_burden_countries_2020-01-21.csv'))
#---------------------------------
# before subsetting, get a global population estimate
global_pop = tb[ ,.(population = sum(e_pop_num)), by=year]
global_pop[ ,location:='Global'] # use for hiv estimates
#---------------------------------
# calculate global incidence and mortality for tb
# merge in later
# sum everything to the global level, shape long and subset
glob_tb = tb[ ,lapply(.SD, sum, na.rm=T), .SDcols=7:50, by=year]
glob_tb = melt(glob_tb, id.vars='year')
keep_glob = c('year', 'e_pop_num', 'e_inc_num', 'e_inc_num_hi', 'e_inc_num_lo',
'e_mort_num', 'e_mort_num_hi', 'e_mort_num_lo')
glob_tb = glob_tb[variable %in% keep_glob]
# shape wide and calculate
glob_tb = dcast(glob_tb, year~variable)
glob_tb[ ,pop_factor:=e_pop_num/100000]
glob_tb_rates = glob_tb[ ,lapply(.SD, function(x) x = x/pop_factor), .SDcols=3:8, by=.(year, pop_factor)]
glob_tb_rates[ ,pop_factor:=NULL]
glob_tb[ ,c('pop_factor', 'e_pop_num'):=NULL]
# keep mortality and incidence rates and counts
setnames(glob_tb_rates, c("year", "e_inc_num", "e_inc_num_lo",
"e_inc_num_hi", "e_mort_num", "e_mort_num_lo", "e_mort_num_hi"),
c('year', 'e_inc_100k', 'e_inc_100k_lo', 'e_inc_100k_hi',
'e_mort_100k', 'e_mort_100k_lo', 'e_mort_100k_hi'))
glob_tb = merge(glob_tb, glob_tb_rates)
glob_tb = melt(glob_tb, id.vars = 'year')
glob_tb[ ,location:='Global']
#--------------------------------
# subset and rename variables
countries = c("Cambodia", "Democratic Republic of the Congo",
"Guatemala", "Mozambique", "Myanmar", "Senegal", "Sudan", "Uganda", "Global")
# subset to relevant countries, does not include global estimates
tb = tb[country %in% countries]
# --------------------------------------
# extract population data and use later to calculate hiv mortality rates
pop = tb[ ,.(population = e_pop_num), by = .(country, year)]
setnames(pop, 'country', 'location')
pop = rbind(pop, global_pop)
# --------------------------------------
# PREP TB DATA
# drop unecessary variables and shape long
tb[ ,c('iso2', 'iso3', 'iso_numeric', 'g_whoregion'):=NULL]
tb = melt(tb, id.vars = c('country', 'year')) # some are numerics and some integers
# keep mortality and incidence rates and counts
keep = c('country', 'year', 'e_inc_100k', 'e_inc_100k_hi', 'e_inc_100k_lo',
'e_mort_100k', 'e_mort_100k_hi', 'e_mort_100k_lo',
'e_inc_num', 'e_inc_num_hi', 'e_inc_num_lo',
'e_mort_num', 'e_mort_num_hi', 'e_mort_num_lo')
tb = tb[variable %in% keep]
setnames(tb, 'country', 'location')
# add in global data
tb = rbind(tb, glob_tb)
# format to match
tb[grepl('hi', variable), bound:='upper']
tb[grepl('lo', variable), bound:='lower']
tb[!grepl('lo', variable) & !grepl('hi', variable), bound:='val']
# designate mortality and incidence, rates and counts
tb[grepl('mort', variable), measure:='Deaths']
tb[grepl('inc', variable), measure:='Incidence']
tb[grepl('100k', variable), metric:='Rate']
tb[!grepl('100k', variable), metric:='Number']
# add age
tb[metric=='Number', age:='All Ages']
tb[metric=='Rate', age:='Age-standardized']
# add variables not included in the data
tb[ ,sex:='Both']
tb[ ,cause:='Tuberculosis']
tb[ ,variable:=NULL]
tb = dcast(tb, measure+location+sex+age+cause+metric+year~bound)
#---------------------
# output prepped tb data
saveRDS(tb, paste0(outDir, 'prepped_data/who_tb_2000_2018_prepped.rds'))
#---------------------
#-------------------------------------------------------
# PREP HIV DATA
#------------------------
# upload and prep unaids data
hiv = fread(paste0(dir, 'raw_data/unaids/New HIV infections_HIV incidence per 1000 population_Population_ All ages.csv'))
hiv2 = fread(paste0(dir, 'raw_data/unaids/AIDS-related deaths_Number of AIDS-related deaths_Population_ All ages.csv'))
hiv3 = fread(paste0(dir, 'raw_data/unaids/new_hiv_infections_counts.csv'))
#----------------
# write a function to format the unaids data
format = function(x) {
# rename the columns and drop the first row as it imports weird
name_list = as.character(x[1])
setnames(x, name_list)
x = x[-1]
# subset and shape long
x = x[Country %in% countries]
setnames(x, 'Country', 'location')
x = melt(x, id.vars='location')
return(x)}
#----------------
#------------------------
# format the data and bind them together
hiv = format(hiv)
hiv2 = format(hiv2)
hiv3 = format(hiv3)
hiv[ ,measure:='Incidence']
hiv[ , metric:='Rate']
hiv2[ , measure:='Deaths']
hiv2[ , metric:='Number']
#--------------------------
hiv = rbind(hiv, hiv2)
hiv[metric=='Number', age:='All Ages']
hiv[metric=='Rate', age:='Age-standardized']# check if age standardized
# shape wide
hiv[grepl('upper', variable), bound:='upper']
hiv[grepl('lower', variable), bound:='lower']
hiv[!grepl('lower', variable) & !grepl('upper', variable), bound:='val']
# split and add year
hiv$year = unlist(lapply(str_split(hiv$variable, '_'), '[', 1))
hiv[ ,variable:=NULL]
hiv = dcast(hiv, location+measure+metric+age+year~bound)
#------------------------
# new infection counts need special wrangling
# reset names and develop bounds
setnames(hiv3, 'variable', 'year')
hiv3$val = trimws(unlist(lapply(str_split(hiv3$value, '\\['), '[', 1)))
hiv3$lower = trimws(unlist(lapply(str_split(hiv3$value, '\\['), '[', 2)))
hiv3$lower = trimws(unlist(lapply(str_split(hiv3$lower, '\\-'), '[', 1))) #split twice for lower bound
hiv3$upper = trimws(unlist(lapply(str_split(hiv3$value, '\\['), '[', 2)))
hiv3$upper = trimws(unlist(lapply(str_split(hiv3$upper, '\\-'), '[', 2)))
hiv3[ ,upper:=gsub('\\]', '', upper)]
hiv3[ ,value:=NULL]
# add in identifiers
hiv3[ ,age:='All Ages']
hiv3[ ,measure:='Incidence']
hiv3[ , metric:='Number']
hiv = rbind(hiv, hiv3)
#--------------------------------------------------
# final formatting of numbers for the merge
# if the estimate is listed as "less than," delete from the data
hiv[grep('<', val)][order(location, year)]
hiv = hiv[!grepl('<', val)]
# only one estimate for cambodia has a less than, in 1990
hiv = hiv[!grepl('<', upper)]
# if the lower bound is listed as less than 1,000, estimate as 0
hiv[grepl('<', lower), lower:=0]
# trim the spaces from the values
hiv[ ,val:=as.numeric(gsub(' ', '', val))]
hiv[ ,lower:=as.numeric(gsub(' ', '', lower))]
hiv[ ,upper:=as.numeric(gsub(' ', '', upper))]
# add the cause
hiv[ ,cause:='HIV/AIDS']
hiv[ ,sex:='Both']
# confirm year is a numeric
hiv[, year:=as.numeric(as.character(year))]
#--------------------------
# hiv rates are per 1,000 - convert to rate per 100k
hiv[metric == 'Rate' , val:=100*val]
hiv[metric == 'Rate' , lower:=100*lower]
hiv[metric == 'Rate' , upper:=100*upper]
#--------------------------------------------------
# calculate mortality rates
death_rates = hiv[measure=='Deaths' & 2000 <= year]
death_rates = merge(death_rates, pop, by=c('location','year'))
# create rates
death_rates[ ,population:=(population/100000)]
death_rates[ ,val:=val/population]
death_rates[ ,lower:=lower/population]
death_rates[ ,upper:=upper/population]
death_rates[ ,metric:='Rate']
death_rates[ ,age:='Age-standardized']
death_rates[ ,population:=NULL]
# bind death rates into the data
hiv = rbind(hiv, death_rates)
#--------------------------------------------------
# save the data set and bind tb and hiv together
# output prepped hiv data
saveRDS(hiv, paste0(outDir, 'prepped_data/unaids_hiv_2000_2018_prepped.rds'))
# bind the data sets together
tb_hiv = rbind(tb, hiv)
#-------------------------------------------------------
# prep malaria data
mal = readRDS(paste0(dir, 'raw_data/who_malaria/estimated_malaria_cases_and_deaths_2010_2018.rds'))
mal = mal[country!='South Sudan']
# format the data and shape long
setnames(mal, 'country', 'location')
mal[ ,sex:='Both']
mal[ ,population_at_risk:=NULL]
mal = melt(mal, id.vars=c('location', 'year', 'sex'))
# format to match
mal[grepl('upper', variable), bound:='upper']
mal[grepl('lower', variable), bound:='lower']
mal[grepl('point', variable), bound:='val']
# designate mortality and incidence, rates and counts
mal[grepl('deaths', variable) | grepl('mortality', variable) , measure:='Deaths']
mal[grepl('inc', variable) | grepl('cases', variable), measure:='Incidence']
mal[grepl('inc', variable) | grepl('mortality', variable), metric:='Rate']
mal[grepl('deaths', variable) | grepl('cases', variable), metric:='Number']
# add age
mal[metric=='Number', age:='All Ages']
mal[metric=='Rate', age:='Age-standardized']
# add variables not included in the data
mal[ ,sex:='Both']
mal[ ,cause:='Malaria']
mal[ ,variable:=NULL]
# shape wide
mal = dcast(mal, measure+location+sex+age+cause+metric+year~bound)
# match locations in other data
mal[location=='DRC', location:='Democratic Republic of the Congo']
#--------------------------------------------------
# save the data set and bind tb and hiv together
# output prepped hiv data
saveRDS(mal, paste0(outDir, 'prepped_data/who_malaria_2010_2018_prepped.rds'))
# bind the data sets together
dt = rbind(tb_hiv, mal)
# ensure year is not a factor
dt[ ,year:=as.numeric(as.character(year))]
#------------------------------------------------
# save the final product
saveRDS(dt, paste0(outDir, 'prepped_data/who_unaids_prepped.rds'))
#------------------------------------------------
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/convert_MMU_Block.R
\name{convert_MMU_Block}
\alias{convert_MMU_Block}
\title{Convert Measurement, methods and units block}
\usage{
convert_MMU_Block(methodColStrs = NULL, unitName = NULL, org.df,
unit.df = NULL, verbose = FALSE)
}
\arguments{
\item{methodColStrs}{an array of strings idenfiying the methods}
\item{verbose}{}
}
\description{
This function will convert a long format with columns declared as methods, measurements, or units.
}
|
/man/convert_MMU_Block.Rd
|
permissive
|
Em17y/soilDataR
|
R
| false | true | 524 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/convert_MMU_Block.R
\name{convert_MMU_Block}
\alias{convert_MMU_Block}
\title{Convert Measurement, methods and units block}
\usage{
convert_MMU_Block(methodColStrs = NULL, unitName = NULL, org.df,
unit.df = NULL, verbose = FALSE)
}
\arguments{
\item{methodColStrs}{an array of strings idenfiying the methods}
\item{verbose}{}
}
\description{
This function will convert a long format with columns declared as methods, measurements, or units.
}
|
\name{mosaic.generateMetricTablePlot}
\alias{mosaic.generateMetricTablePlot}
\title{
generateMetricTablePlot
}
\description{
Generate missing-ratio table for metric data (data, num of columns, column index, varname)
}
\usage{
mosaic.generateMetricTablePlot(data, num_of_columns, index, varname)
}
\arguments{
\item{data}{
preprocessed data frame including 'valid value markers'
}
\item{num_of_columns}{
absolute number of to be processed data columns
}
\item{index}{
current column to be processed
}
\item{varname}{
current name of variable to be used in table heading
}
}
\author{
The MOSAIC Project, Martin Bialke
}
\note{
Function call type: internal
}
|
/man/mosaic.generateMetricTablePlot.Rd
|
no_license
|
cran/MOQA
|
R
| false | false | 705 |
rd
|
\name{mosaic.generateMetricTablePlot}
\alias{mosaic.generateMetricTablePlot}
\title{
generateMetricTablePlot
}
\description{
Generate missing-ratio table for metric data (data, num of columns, column index, varname)
}
\usage{
mosaic.generateMetricTablePlot(data, num_of_columns, index, varname)
}
\arguments{
\item{data}{
preprocessed data frame including 'valid value markers'
}
\item{num_of_columns}{
absolute number of to be processed data columns
}
\item{index}{
current column to be processed
}
\item{varname}{
current name of variable to be used in table heading
}
}
\author{
The MOSAIC Project, Martin Bialke
}
\note{
Function call type: internal
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/exportFromSCE.R
\name{exportFromSCE}
\alias{exportFromSCE}
\title{Export SingleCellExperiment (SCE) object to Cerebro.}
\usage{
exportFromSCE(
object,
assay = "logcounts",
file,
experiment_name,
organism,
groups,
cell_cycle = NULL,
nUMI = "nUMI",
nGene = "nGene",
add_all_meta_data = TRUE,
use_delayed_array = FALSE,
verbose = FALSE
)
}
\arguments{
\item{object}{\code{SingleCellExperiment} (\code{SCE}) object.}
\item{assay}{Assay to pull expression values from; defaults to
\code{logcounts}. It is recommended to use sparse data (such as
log-transformed or raw counts) instead of dense data (such as the 'scaled'
slot) to avoid performance bottlenecks in the Cerebro interface.}
\item{file}{Where to save the output.}
\item{experiment_name}{Experiment name.}
\item{organism}{Organism, e.g. \code{hg} (human), \code{mm} (mouse), etc.}
\item{groups}{Names of grouping variables in meta data
(\code{colData(object)}), e.g. \code{c("sample","cluster")}; at least one
must be provided; defaults to \code{NULL}.}
\item{cell_cycle}{Names of columns in meta data (\code{colData(object)}) that#
contain cell cycle information, e.g. \code{c("Phase")}; defaults to
\code{NULL}.}
\item{nUMI}{Column in \code{colData(object)} that contains information about
number of transcripts per cell; defaults to \code{nUMI}.}
\item{nGene}{Column in \code{colData(object)} that contains information about
number of expressed genes per cell; defaults to \code{nGene}.}
\item{add_all_meta_data}{If set to \code{TRUE}, all further meta data columns
will be extracted as well.}
\item{use_delayed_array}{When set to \code{TRUE}, the expression matrix will
be stored as an \code{RleMatrix} (see \code{DelayedArray} package). This can
be useful for very large data sets, as the matrix won't be loaded into memory
and instead values will be read from the disk directly, at the cost of
performance. Note that it is necessary to install the \code{DelayedArray}
package. If set to \code{FALSE} (default), the expression matrix will be
copied from the input object as is. It is recommended to use a sparse format,
such as \code{dgCMatrix} from the \code{Matrix} package.}
\item{verbose}{Set this to \code{TRUE} if you want additional log messages;
defaults to \code{FALSE}.}
}
\value{
No data returned.
}
\description{
This function allows to export a \code{SingleCellExperiment} (\code{SCE})
object to visualize in Cerebro.
}
\examples{
pbmc <- readRDS(system.file("extdata/v1.3/pbmc_SCE.rds",
package = "cerebroApp"))
exportFromSCE(
object = pbmc,
file = 'pbmc_SCE.crb',
experiment_name = 'PBMC',
organism = 'hg',
groups = c('sample','cluster'),
nUMI = 'nUMI',
nGene = 'nGene',
use_delayed_array = FALSE,
verbose = TRUE
)
}
|
/man/exportFromSCE.Rd
|
permissive
|
romanhaa/cerebroApp
|
R
| false | true | 2,826 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/exportFromSCE.R
\name{exportFromSCE}
\alias{exportFromSCE}
\title{Export SingleCellExperiment (SCE) object to Cerebro.}
\usage{
exportFromSCE(
object,
assay = "logcounts",
file,
experiment_name,
organism,
groups,
cell_cycle = NULL,
nUMI = "nUMI",
nGene = "nGene",
add_all_meta_data = TRUE,
use_delayed_array = FALSE,
verbose = FALSE
)
}
\arguments{
\item{object}{\code{SingleCellExperiment} (\code{SCE}) object.}
\item{assay}{Assay to pull expression values from; defaults to
\code{logcounts}. It is recommended to use sparse data (such as
log-transformed or raw counts) instead of dense data (such as the 'scaled'
slot) to avoid performance bottlenecks in the Cerebro interface.}
\item{file}{Where to save the output.}
\item{experiment_name}{Experiment name.}
\item{organism}{Organism, e.g. \code{hg} (human), \code{mm} (mouse), etc.}
\item{groups}{Names of grouping variables in meta data
(\code{colData(object)}), e.g. \code{c("sample","cluster")}; at least one
must be provided; defaults to \code{NULL}.}
\item{cell_cycle}{Names of columns in meta data (\code{colData(object)}) that#
contain cell cycle information, e.g. \code{c("Phase")}; defaults to
\code{NULL}.}
\item{nUMI}{Column in \code{colData(object)} that contains information about
number of transcripts per cell; defaults to \code{nUMI}.}
\item{nGene}{Column in \code{colData(object)} that contains information about
number of expressed genes per cell; defaults to \code{nGene}.}
\item{add_all_meta_data}{If set to \code{TRUE}, all further meta data columns
will be extracted as well.}
\item{use_delayed_array}{When set to \code{TRUE}, the expression matrix will
be stored as an \code{RleMatrix} (see \code{DelayedArray} package). This can
be useful for very large data sets, as the matrix won't be loaded into memory
and instead values will be read from the disk directly, at the cost of
performance. Note that it is necessary to install the \code{DelayedArray}
package. If set to \code{FALSE} (default), the expression matrix will be
copied from the input object as is. It is recommended to use a sparse format,
such as \code{dgCMatrix} from the \code{Matrix} package.}
\item{verbose}{Set this to \code{TRUE} if you want additional log messages;
defaults to \code{FALSE}.}
}
\value{
No data returned.
}
\description{
This function allows to export a \code{SingleCellExperiment} (\code{SCE})
object to visualize in Cerebro.
}
\examples{
pbmc <- readRDS(system.file("extdata/v1.3/pbmc_SCE.rds",
package = "cerebroApp"))
exportFromSCE(
object = pbmc,
file = 'pbmc_SCE.crb',
experiment_name = 'PBMC',
organism = 'hg',
groups = c('sample','cluster'),
nUMI = 'nUMI',
nGene = 'nGene',
use_delayed_array = FALSE,
verbose = TRUE
)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/themes.R
\name{theme_scientific}
\alias{theme_scientific}
\title{Theme 'scientific'}
\usage{
theme_scientific()
}
\value{
theme
}
\description{
A theme that was strongly based on http://minimaxir.com/2015/02/ggplot-tutorial/
}
|
/man/theme_scientific.Rd
|
no_license
|
DPCscience/artyfarty
|
R
| false | true | 306 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/themes.R
\name{theme_scientific}
\alias{theme_scientific}
\title{Theme 'scientific'}
\usage{
theme_scientific()
}
\value{
theme
}
\description{
A theme that was strongly based on http://minimaxir.com/2015/02/ggplot-tutorial/
}
|
install.packages("openxlsx")
library(openxlsx)
input<- EastWestAirlines
normal<-scale(input[,2:12])
dis<-dist(normal,method ="euclidean")
fit<-hclust(dis,method = "ward.D2")
plot(fit)
plot(fit,hang=-1)
group<-cutree(fit,k=6)
rect.hclust(fit,k=6,border="red")
matrix<-as.matrix(group,EastWestAirlines)
matrix
final<-data.frame(EastWestAirlines,group)
last<-final[,c(ncol(final),1:(ncol(final)-1))]
write.csv(last,file="dendgoram")
##k mean
input<- EastWestAirlines
normal<-scale(input[,2:12])
plot(normal)
plot(normal,type = "n")
text(normal,rownames(normal))
km<-kmeans(normal,6)
str(km)
km$cluster
install.packages("animation")
library(animation)
km1<-kmeans.ani(normal,6)
wss = (nrow(normal)-1)*sum(apply(normal,2,var))
for(i in 2:12) wss[i] = sum(kmeans(normal,centers=i)$withinss)
plot(1:12,wss,type="b",xlab = "Number of clusters",ylab="within groups sum of squares")
title(sub="k-means clustering screen-plot")
fit<-kmeans(normal,6)
str(fit$cluster)
final2<-data.frame(input,fit$cluster)
final2
final3<-final2[,c(ncol(final2),1:(ncol(final2)-1))]
aggregate(input[,2:12],by=list(fit$cluster),FUN=mean)
write.csv(final3,file="kmean")
getwd()
|
/cluster k means.R
|
no_license
|
vageesha1994/cluster
|
R
| false | false | 1,186 |
r
|
install.packages("openxlsx")
library(openxlsx)
input<- EastWestAirlines
normal<-scale(input[,2:12])
dis<-dist(normal,method ="euclidean")
fit<-hclust(dis,method = "ward.D2")
plot(fit)
plot(fit,hang=-1)
group<-cutree(fit,k=6)
rect.hclust(fit,k=6,border="red")
matrix<-as.matrix(group,EastWestAirlines)
matrix
final<-data.frame(EastWestAirlines,group)
last<-final[,c(ncol(final),1:(ncol(final)-1))]
write.csv(last,file="dendgoram")
##k mean
input<- EastWestAirlines
normal<-scale(input[,2:12])
plot(normal)
plot(normal,type = "n")
text(normal,rownames(normal))
km<-kmeans(normal,6)
str(km)
km$cluster
install.packages("animation")
library(animation)
km1<-kmeans.ani(normal,6)
wss = (nrow(normal)-1)*sum(apply(normal,2,var))
for(i in 2:12) wss[i] = sum(kmeans(normal,centers=i)$withinss)
plot(1:12,wss,type="b",xlab = "Number of clusters",ylab="within groups sum of squares")
title(sub="k-means clustering screen-plot")
fit<-kmeans(normal,6)
str(fit$cluster)
final2<-data.frame(input,fit$cluster)
final2
final3<-final2[,c(ncol(final2),1:(ncol(final2)-1))]
aggregate(input[,2:12],by=list(fit$cluster),FUN=mean)
write.csv(final3,file="kmean")
getwd()
|
library(glmnet)
mydata = read.table("./TrainingSet/RF/cervix.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.01,family="gaussian",standardize=FALSE)
sink('./Model/EN/Classifier/cervix/cervix_006.txt',append=TRUE)
print(glm$glmnet.fit)
sink()
|
/Model/EN/Classifier/cervix/cervix_006.R
|
no_license
|
leon1003/QSMART
|
R
| false | false | 351 |
r
|
library(glmnet)
mydata = read.table("./TrainingSet/RF/cervix.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.01,family="gaussian",standardize=FALSE)
sink('./Model/EN/Classifier/cervix/cervix_006.txt',append=TRUE)
print(glm$glmnet.fit)
sink()
|
# 2-level bootstrapping example function
#' Multi-level Bootstrapping
#'
#' Bootstrapping for 2-level multi-level data
#' @usage bootmlm2(id, group1, data)
#' @param id character of level 1 grouping variable/identifier
#' @param group1 character of level 2 grouping variable (ex: classrooms)
#' @param data dataset from which to bootstrap
#' @examples bootmlm2(id = "ID", group1 = "schoolid", data = dat)
bootmlm2 <- function(id, group1, data){
# Find the unique groups
num.groups <- unique(data[,group1])
# Find the unique IDs
num.ids <- unique(data[,id])
# Initiate bootstrap sample storage
boot.samp <- NULL
# Get column call ready since can't pass as before
group1 <- data[,group1]
# Resample within each group
for(i in num.groups){
# Get observations that are in current group
in.group <- data[group1==i,]
# sample from within that group
group.samp <- in.group[sample(1:nrow(in.group), nrow(in.group), replace = T),]
# Save into a data frame
boot.samp <- rbind(boot.samp,group.samp)
}
return(boot.samp)
}
|
/R/bootmlm_example_2levelonly.R
|
no_license
|
CClingain/powermlm
|
R
| false | false | 1,062 |
r
|
# 2-level bootstrapping example function
#' Multi-level Bootstrapping
#'
#' Bootstrapping for 2-level multi-level data
#' @usage bootmlm2(id, group1, data)
#' @param id character of level 1 grouping variable/identifier
#' @param group1 character of level 2 grouping variable (ex: classrooms)
#' @param data dataset from which to bootstrap
#' @examples bootmlm2(id = "ID", group1 = "schoolid", data = dat)
bootmlm2 <- function(id, group1, data){
# Find the unique groups
num.groups <- unique(data[,group1])
# Find the unique IDs
num.ids <- unique(data[,id])
# Initiate bootstrap sample storage
boot.samp <- NULL
# Get column call ready since can't pass as before
group1 <- data[,group1]
# Resample within each group
for(i in num.groups){
# Get observations that are in current group
in.group <- data[group1==i,]
# sample from within that group
group.samp <- in.group[sample(1:nrow(in.group), nrow(in.group), replace = T),]
# Save into a data frame
boot.samp <- rbind(boot.samp,group.samp)
}
return(boot.samp)
}
|
\name{utf8_width}
\title{Measure the Character String Width}
\alias{utf8_width}
\description{
Compute the display widths of the elements of a character object.
}
\usage{
utf8_width(x, encode = TRUE, quote = FALSE, utf8 = NULL)
}
\arguments{
\item{x}{character object.}
\item{encode}{whether to encode the object before measuring its
width.}
\item{quote}{whether to quote the object before measuring its
width.}
\item{utf8}{logical scalar indicating whether to determine widths
assuming a UTF-8 capable display (ASCII-only otherwise), or
\code{NULL} to format for output capabilities as determined
by \code{output_utf8()}.}
}
\details{
\code{utf8_width} returns the printed widths of the elements of
a character object on a UTF-8 device (or on an ASCII device
when \code{output_utf8()} is \code{FALSE}), when printed with
\code{utf8_print}. If the string is not printable on the device,
for example if it contains a control code like \code{"\n"}, then
the result is \code{NA}. If \code{encode = TRUE}, the default,
then the function returns the widths of the encoded elements
via \code{utf8_encode}); otherwise, the function returns the
widths of the original elements.
}
\value{
An integer object, with the same \code{names}, \code{dim}, and
\code{dimnames} as \code{x}.
}
\seealso{
\code{\link{utf8_print}}.
}
\examples{
# the second element is encoded in latin-1, but declared as UTF-8
x <- c("fa\u00E7ile", "fa\xE7ile", "fa\xC3\xA7ile")
Encoding(x) <- c("UTF-8", "UTF-8", "bytes")
# get widths
utf8_width(x)
utf8_width(x, encode = FALSE)
utf8_width('"')
utf8_width('"', quote = TRUE)
}
|
/man/utf8_width.Rd
|
permissive
|
bedatadriven/r-utf8
|
R
| false | false | 1,698 |
rd
|
\name{utf8_width}
\title{Measure the Character String Width}
\alias{utf8_width}
\description{
Compute the display widths of the elements of a character object.
}
\usage{
utf8_width(x, encode = TRUE, quote = FALSE, utf8 = NULL)
}
\arguments{
\item{x}{character object.}
\item{encode}{whether to encode the object before measuring its
width.}
\item{quote}{whether to quote the object before measuring its
width.}
\item{utf8}{logical scalar indicating whether to determine widths
assuming a UTF-8 capable display (ASCII-only otherwise), or
\code{NULL} to format for output capabilities as determined
by \code{output_utf8()}.}
}
\details{
\code{utf8_width} returns the printed widths of the elements of
a character object on a UTF-8 device (or on an ASCII device
when \code{output_utf8()} is \code{FALSE}), when printed with
\code{utf8_print}. If the string is not printable on the device,
for example if it contains a control code like \code{"\n"}, then
the result is \code{NA}. If \code{encode = TRUE}, the default,
then the function returns the widths of the encoded elements
via \code{utf8_encode}); otherwise, the function returns the
widths of the original elements.
}
\value{
An integer object, with the same \code{names}, \code{dim}, and
\code{dimnames} as \code{x}.
}
\seealso{
\code{\link{utf8_print}}.
}
\examples{
# the second element is encoded in latin-1, but declared as UTF-8
x <- c("fa\u00E7ile", "fa\xE7ile", "fa\xC3\xA7ile")
Encoding(x) <- c("UTF-8", "UTF-8", "bytes")
# get widths
utf8_width(x)
utf8_width(x, encode = FALSE)
utf8_width('"')
utf8_width('"', quote = TRUE)
}
|
# use quadratic exponential scheme from Andersen (2008) to discretize the CIR process
QE = function(v0, kappa, theta, sigma, T, n, M){
Delta = T / n
U = matrix(runif(M * n), M, n)
psic = 1.5
emkt = exp(- kappa * Delta)
c1 = sigma * sigma * emkt * (1 - emkt) / kappa
c2 = theta * sigma * sigma * ((1 - emkt)^2) / (2 * kappa)
v = matrix(, M, n + 1)
v[, 1]= v0
for(i in 1:n){
s2 = v[, i] * c1 + c2
m = v[, i] * emkt + theta * (1 - emkt)
psi = s2 / (m * m)
v[,i + 1] =
ifelse(
psi <= psic,
# a * (b + Z_v)^2, where a = m / (1 + b^2)
m / (1 + 2 * psi^-1 - 1 + sqrt(2 * psi^-1) * sqrt(2 * psi^-1 - 1)) * (sqrt(2 * psi^-1 - 1 + sqrt(2 * psi^-1) * sqrt(2 * psi^-1 - 1)) + qnorm(U[, i]))^2,
# p = (psi - 1) / (psi + 1)
ifelse(U[, i] < (psi - 1) / (psi + 1), 0, ((1 - (psi - 1) / (psi + 1)) / m)^-1 * log( (1 - (psi - 1) / (psi + 1)) / (1 - U[,i]) ))
)
}
# Old, not vectorized code:
# if(psi < psic){
#
# psii = 1 / psi
# b2 = 2 * psii - 1 + sqrt(2 * psii * (2 * psii - 1))
# a = m / (1 + b2)
# v[i] = a * (sqrt(b2) + qnorm(U[i - 1]))^2
#
# }else{
#
# p = (psi - 1) / (psi + 1)
# beta = (1 - p) / m
#
# if(U[i - 1] < p){
#
# v[i] = 0
#
# }else{
#
# v[i] = (1 / beta) * log( (1 - p) / (1 - U[i - 1]) )
#
# }
# }
# }
return(v)
}
# test = QE(0.04, 0.5, 0.04, 1, 1, 1, 1000000)
# > MomentsCIR(p = 1:4, kappa = 0.5, theta = 0.04, sigma = 1, v0 = 0.04, t = 1)
# Evt^1|v0 Evt^2|v0 Evt^3|v0 Evt^4|v0
# 0.04000000 0.02688482 0.03050794 0.04777093
|
/R/CIR/QE.r
|
no_license
|
mrockinger/CIR-Heston-Simulation
|
R
| false | false | 1,995 |
r
|
# use quadratic exponential scheme from Andersen (2008) to discretize the CIR process
QE = function(v0, kappa, theta, sigma, T, n, M){
Delta = T / n
U = matrix(runif(M * n), M, n)
psic = 1.5
emkt = exp(- kappa * Delta)
c1 = sigma * sigma * emkt * (1 - emkt) / kappa
c2 = theta * sigma * sigma * ((1 - emkt)^2) / (2 * kappa)
v = matrix(, M, n + 1)
v[, 1]= v0
for(i in 1:n){
s2 = v[, i] * c1 + c2
m = v[, i] * emkt + theta * (1 - emkt)
psi = s2 / (m * m)
v[,i + 1] =
ifelse(
psi <= psic,
# a * (b + Z_v)^2, where a = m / (1 + b^2)
m / (1 + 2 * psi^-1 - 1 + sqrt(2 * psi^-1) * sqrt(2 * psi^-1 - 1)) * (sqrt(2 * psi^-1 - 1 + sqrt(2 * psi^-1) * sqrt(2 * psi^-1 - 1)) + qnorm(U[, i]))^2,
# p = (psi - 1) / (psi + 1)
ifelse(U[, i] < (psi - 1) / (psi + 1), 0, ((1 - (psi - 1) / (psi + 1)) / m)^-1 * log( (1 - (psi - 1) / (psi + 1)) / (1 - U[,i]) ))
)
}
# Old, not vectorized code:
# if(psi < psic){
#
# psii = 1 / psi
# b2 = 2 * psii - 1 + sqrt(2 * psii * (2 * psii - 1))
# a = m / (1 + b2)
# v[i] = a * (sqrt(b2) + qnorm(U[i - 1]))^2
#
# }else{
#
# p = (psi - 1) / (psi + 1)
# beta = (1 - p) / m
#
# if(U[i - 1] < p){
#
# v[i] = 0
#
# }else{
#
# v[i] = (1 / beta) * log( (1 - p) / (1 - U[i - 1]) )
#
# }
# }
# }
return(v)
}
# test = QE(0.04, 0.5, 0.04, 1, 1, 1, 1000000)
# > MomentsCIR(p = 1:4, kappa = 0.5, theta = 0.04, sigma = 1, v0 = 0.04, t = 1)
# Evt^1|v0 Evt^2|v0 Evt^3|v0 Evt^4|v0
# 0.04000000 0.02688482 0.03050794 0.04777093
|
#########################################
# ER_all
#########################################
# summary Plots
Focusing on this
```{r}
summary(g.er.BEST1$gam)
```
## Predictions
```{r}
pdf(file.path(here::here("03_Plots"), "02a_ERpredictionXdoy.pdf"), height = 4, width = 8)
ggplot() +
geom_point(data = met, aes(y = lER, x = doy, color = treatment)) +
geom_line(data = met, aes(y = ERgamPred, x = doy, color = treatment)) +
geom_ribbon(data = met, aes(ymin = ERgamPred - ERgamPred.se,
ymax = ERgamPred + ERgamPred.se, x = doy, fill = treatment), alpha = 0.5) +
facet_grid(stream ~ treatment)
dev.off()
```
Use model to predict log ER, with Qres, doy and stream set to "centered" values
```{r}
gamPred2 <- predict_gam(g.er.BEST1$gam, exclude_terms = c(
"s(Qres):streamst11U",
"s(Qres):streamst18",
"s(Qres):streamst6",
"s(Qres):streamst9",
"s(doy):streamst11U",
"s(doy):streamst18",
"s(doy):streamst6",
"s(doy):streamst9"),
values = list(
Qres = 0,
doy = 200,
stream = "st6"))
# make the temperature axes sensical
gamPred3 <- gamPred2 %>%
mutate(Tanom.iktCsFlip = -1 * Tanom.iktCs,
invKT.C.StMeanFlip = -1 *invKT.C.StMean)
```
**NOTE: TEMPERATURE AXIS ARE FLIPPED SO LARGE #'S ARE WARM**
During amb year, ER increases with daily temp and with annual temp across streams. Duing the N and P years, this switches. We see ER increasing with annual temp again, but decreasing with daily temp. Cool
```{r}
ErPredSur <- ggplot() +
geom_raster(data = gamPred2, aes(y = -invKT.C.StMean, x = -Tanom.iktCs, fill = fit)) +
facet_wrap(vars(treatment)) +
scale_fill_distiller(palette = "Spectral") +
geom_point(data = met, aes(y = -invKT.C.StMean, x = -Tanom.iktCs, size = lER),
position = position_jitter(width = 0.05, height = 0.025), shape = 21, fill = "transparent", alpha= 0.5) +
scale_size_binned(n.breaks = 4, nice.breaks = TRUE) +
ylab("Mean summer temp (-1 * centered 1/kT)") +
xlab("Mean daily temp (-1 * centered 1/kT)")
```
```{r}
gamPred2w <- gamPred2 %>%
pivot_wider(id_cols = Tanom.iktCs:doy, names_from = treatment, values_from = c(fit,se.fit)) %>%
mutate(N_amb = exp(fit_nitrogen)/exp(fit_ambient),
P_amb = exp(fit_phosphorus)/exp(fit_ambient))
gamPred2w2 <- gamPred2w %>%
select(Tanom.iktCs:doy, fit_ambient:P_amb) %>%
pivot_longer(cols = N_amb:P_amb, names_to = "treatment", values_to = "effectSize") %>%
mutate(treatment = fct_recode(treatment, Nitrogen = "N_amb", Phosphorus = "P_amb"))
```
```{r}
pdf(file.path(here::here("03_Plots"), "02b_ERpredictionSurfaces.pdf"), height = 4, width = 8)
ErPredSur
dev.off()
```
```{r}
pdf(file.path(here::here("03_Plots"), "02c_ERdifferencePlots2.pdf"), height = 5, width = 4)
ggplot(gamPred2w2, aes(y = -invKT.C.StMean, x = -Tanom.iktCs, fill = effectSize)) +
geom_raster() +
scale_fill_distiller(palette = "Spectral") +
ylab("Mean summer temp (-1 * centered 1/kT)") +
xlab("Mean daily temp (-1 * centered 1/kT)") +
facet_wrap(vars(treatment), nrow = 2, ncol = 1)
dev.off()
```
#########################################
# ER_N
#########################################
# summary Plots
Focusing on this
```{r}
summary(g.er.BEST1$gam)
```
## Predictions
```{r}
pdf(file.path(here::here("03_Plots"), "02b_ERpredXdoy_Nonly.pdf"), height = 4, width = 8)
ggplot() +
geom_point(data = met, aes(y = lER, x = doy, color = treatment)) +
geom_line(data = met, aes(y = ERgamPredNonly, x = doy, color = treatment)) +
geom_ribbon(data = met, aes(ymin = ERgamPredNonly - ERgamPredNonly.se,
ymax = ERgamPredNonly + ERgamPredNonly.se, x = doy, fill = treatment), alpha = 0.5) +
facet_grid(stream ~ treatment)
dev.off()
```
Use model to predict log ER, with Qres, doy and stream set to "centered" values
```{r}
gamPred2 <- predict_gam(g.er.BEST1$gam, exclude_terms = c(
"s(Qres):streamst11U",
"s(Qres):streamst18",
"s(Qres):streamst6",
"s(Qres):streamst9",
"s(DOYres)"),
values = list(
Qres = 0,
DOYres = 8.1, # this is the median
stream = "st6"))
```
**NOTE: TEMPERATURE AXIS ARE FLIPPED SO LARGE #'S ARE WARM**
During amb year, ER increases with daily temp and with annual temp across streams. Duing the N and P years, this switches. We see ER increasing with annual temp again, but decreasing with daily temp. Cool
```{r}
#note inversing temp axis here
ErPredSur <- ggplot() +
geom_raster(data = gamPred2, aes(y = invKT.C.StMean, x = Tanom.iktCs, fill = fit)) +
facet_wrap(vars(treatment)) +
scale_fill_distiller(palette = "Spectral") +
geom_point(data = met, aes(y = invKT.C.StMean, x = Tanom.iktCs, size = lER),
position = position_jitter(width = 0.05, height = 0.025), shape = 21, fill = "transparent", alpha= 0.5) +
scale_size_binned(n.breaks = 4, nice.breaks = TRUE) +
ylab("Mean summer temp (-1 * centered 1/kT)") +
xlab("Mean daily temp (-1 * centered 1/kT)")
```
```{r}
gamPred2w <- gamPred2 %>%
pivot_wider(id_cols = invKT.C.StMean:stream, names_from = treatment, values_from = c(fit,se.fit)) %>%
mutate(N_amb = exp(fit_nitrogen)/exp(fit_ambient))
gamPred2w2 <- gamPred2w %>%
# select(Tanom.iktCs:doy, fit_ambient:P_amb) %>%
pivot_longer(cols = N_amb, names_to = "treatment", values_to = "effectSize") %>%
mutate(treatment = fct_recode(treatment, Nitrogen = "N_amb"))
```
```{r}
pdf(file.path(here::here("03_Plots"), "02b_ERpredSurNonly.pdf"), height = 4, width = 8)
ErPredSur
dev.off()
```
```{r}
pdf(file.path(here::here("03_Plots"), "02b_ERdifPlotsNonly.pdf"), height = 5, width = 4)
ggplot() +
geom_raster(data = gamPred2w2, aes(y = -invKT.C.StMean, x = -Tanom.iktCs, fill = effectSize)) +
geom_point(data = met, aes(y = -invKT.C.StMean, x = -Tanom.iktCs, size = lER), alpha = 30/100)+
scale_fill_distiller(palette = "Spectral") +
ylab("Mean summer temp (-1 * centered 1/kT)") +
xlab("Mean daily temp (-1 * centered 1/kT)")
dev.off()
```
#########################################
# ER_P
#########################################
# summary Plots
Focusing on this
```{r}
summary(g.er.BEST1$gam)
```
## Predictions
```{r}
pdf(file.path(here::here("03_Plots"), "02c_Ponly_ERpredXdoyPonly.pdf"), height = 4, width = 8)
ggplot() +
geom_point(data = met, aes(y = lER, x = doy, color = treatment)) +
geom_line(data = met, aes(y = ERgamPred, x = doy, color = treatment)) +
geom_ribbon(data = met, aes(ymin = ERgamPred - ERgamPred.se,
ymax = ERgamPred + ERgamPred.se, x = doy, fill = treatment), alpha = 0.5) +
facet_grid(stream ~ treatment)
dev.off()
```
Use model to predict log ER, with Qres, doy and stream set to "centered" values
```{r}
gamPred2 <- predict_gam(g.er.BEST1$gam,exclude_terms = c(
"s(Qres):streamst11U",
"s(Qres):streamst18",
"s(Qres):streamst6",
"s(Qres):streamst9",
"s(DOYres):streamst11U",
"s(DOYres):streamst18",
"s(DOYres):streamst6",
"s(DOYres):streamst9"),
values = list(
Qres = 0,
DOYres = 6.1)) # median in this dataset
```
```{r}
gamPred2w <- gamPred2 %>%
pivot_wider(id_cols = c(stream:Tanom.iktCs,Qres:DOYres), names_from = treatment, values_from = c(fit,se.fit)) %>%
mutate(P_amb = exp(fit_phosphorus)/exp(fit_ambient))
# gamPred2w2 <- gamPred2w %>%
# select(Tanom.iktCs:doy, fit_ambient:P_amb) %>%
# pivot_longer(cols = N_amb:P_amb, names_to = "treatment", values_to = "effectSize") %>%
# mutate(treatment = fct_recode(treatment, Nitrogen = "N_amb", Phosphorus = "P_amb"))
```
**NOTE: TEMPERATURE AXIS ARE FLIPPED SO LARGE #'S ARE WARM**
During amb year, ER increases with daily temp and with annual temp across streams. Duing the N and P years, this switches. We see ER increasing with annual temp again, but decreasing with daily temp. Cool
```{r}
ErPredSur <- ggplot() +
geom_raster(data = gamPred2, aes(y = stream, x = -Tanom.iktCs, fill = fit)) +
scale_fill_distiller(palette = "Spectral") +
geom_point(data = met, aes(y = stream, x = -Tanom.iktCs, size = lER),
position = position_jitter(width = 0, height = 0.075), shape = 21, fill = "transparent", alpha= 0.5) +
scale_size_binned(n.breaks = 4, nice.breaks = TRUE) +
facet_wrap(vars(treatment)) +
ylab("stream") +
xlab("Mean daily temp (-1 * centered 1/kT)")
```
```{r}
pdf(file.path(here::here("03_Plots"), "02c_ERpredSur_Ponly.pdf"), height = 4, width = 8)
ErPredSur
dev.off()
```
```{r}
pdf(file.path(here::here("03_Plots"), "02c_ERdifPlotsPonly.pdf"), height = 5, width = 4)
ggplot() +
geom_raster(data = gamPred2w, aes(y = stream, x = -Tanom.iktCs, fill = P_amb)) +
geom_point(data = met, aes(y = stream, x = -Tanom.iktCs, size = lER),
position = position_jitter(width = 0, height = 0.075), shape = 21, fill = "transparent", alpha= 0.5) +
scale_fill_distiller(palette = "Spectral") +
xlab("Mean daily temp (-1 * centered 1/kT)") +
ylab("stream")
dev.off()
```
#########################################
# GPP_All
#########################################
# These aren't too useful for interactions
```{r echo=FALSE}
GPPquickPlot1 <- plot(g.gpp.BEST1.gv, allTerms = 1) +
l_points(color = "pink") + l_fitLine(linetype = 3) + l_fitContour() +
l_ciLine(colour = 2) + l_ciBar() + l_fitPoints(size = 1, col = 2) + theme_get() + labs(title = NULL)
print(GPPquickPlot1, pages = 1)
```
```{r echo=FALSE}
GPPquickPlot2 <- plot(g.gpp.BEST2.gv, allTerms = 1) +
l_points(color = "pink") + l_fitLine(linetype = 3) + l_fitContour() +
l_ciLine(colour = 2) + l_ciBar() + l_fitPoints(size = 1, col = 2) + theme_get() + labs(title = NULL)
print(GPPquickPlot2, pages = 1)
```
```{r echo=FALSE}
GPPquickPlot3 <- plot(g.gpp.BEST3.gv, allTerms = 1) +
l_points(color = "pink") + l_fitLine(linetype = 3) + l_fitContour() +
l_ciLine(colour = 2) + l_ciBar() + l_fitPoints(size = 1, col = 2) + theme_get() + labs(title = NULL)
print(GPPquickPlot3, pages = 1)
```
Predict for future ploting
```{r echo= FALSE}
met$GPPgamPred <- predict.gam(g.gppBEST3$gam, met, type = "response")
met$GPPgamPred.se <- predict.gam(g.gppBEST3$gam, met, type = "response", se.fit = TRUE)$se.fit
```
#########################################
# GPP_N
#########################################
# These aren't too useful for interactions
```{r echo=FALSE}
GPPquickPlot1 <- plot(g.gpp.BEST1.gv, allTerms = 1) +
l_points(color = "pink") + l_fitLine(linetype = 3) + l_fitContour() +
l_ciLine(colour = 2) + l_ciBar() + l_fitPoints(size = 1, col = 2) + theme_get() + labs(title = NULL)
print(GPPquickPlot1, pages = 1)
```
```{r echo=FALSE}
GPPquickPlot2 <- plot(g.gpp.BEST2.gv, allTerms = 1) +
l_points(color = "pink") + l_fitLine(linetype = 3) + l_fitContour() +
l_ciLine(colour = 2) + l_ciBar() + l_fitPoints(size = 1, col = 2) + theme_get() + labs(title = NULL)
print(GPPquickPlot2, pages = 1)
```
```{r echo=FALSE}
GPPquickPlot3 <- plot(g.gpp.BEST3.gv, allTerms = 1) +
l_points(color = "pink") + l_fitLine(linetype = 3) + l_fitContour() +
l_ciLine(colour = 2) + l_ciBar() + l_fitPoints(size = 1, col = 2) + theme_get() + labs(title = NULL)
print(GPPquickPlot3, pages = 1)
```
|
/zz_code for figs.R
|
no_license
|
hood211/IceMetab2
|
R
| false | false | 11,181 |
r
|
#########################################
# ER_all
#########################################
# summary Plots
Focusing on this
```{r}
summary(g.er.BEST1$gam)
```
## Predictions
```{r}
pdf(file.path(here::here("03_Plots"), "02a_ERpredictionXdoy.pdf"), height = 4, width = 8)
ggplot() +
geom_point(data = met, aes(y = lER, x = doy, color = treatment)) +
geom_line(data = met, aes(y = ERgamPred, x = doy, color = treatment)) +
geom_ribbon(data = met, aes(ymin = ERgamPred - ERgamPred.se,
ymax = ERgamPred + ERgamPred.se, x = doy, fill = treatment), alpha = 0.5) +
facet_grid(stream ~ treatment)
dev.off()
```
Use model to predict log ER, with Qres, doy and stream set to "centered" values
```{r}
gamPred2 <- predict_gam(g.er.BEST1$gam, exclude_terms = c(
"s(Qres):streamst11U",
"s(Qres):streamst18",
"s(Qres):streamst6",
"s(Qres):streamst9",
"s(doy):streamst11U",
"s(doy):streamst18",
"s(doy):streamst6",
"s(doy):streamst9"),
values = list(
Qres = 0,
doy = 200,
stream = "st6"))
# make the temperature axes sensical
gamPred3 <- gamPred2 %>%
mutate(Tanom.iktCsFlip = -1 * Tanom.iktCs,
invKT.C.StMeanFlip = -1 *invKT.C.StMean)
```
**NOTE: TEMPERATURE AXIS ARE FLIPPED SO LARGE #'S ARE WARM**
During amb year, ER increases with daily temp and with annual temp across streams. Duing the N and P years, this switches. We see ER increasing with annual temp again, but decreasing with daily temp. Cool
```{r}
ErPredSur <- ggplot() +
geom_raster(data = gamPred2, aes(y = -invKT.C.StMean, x = -Tanom.iktCs, fill = fit)) +
facet_wrap(vars(treatment)) +
scale_fill_distiller(palette = "Spectral") +
geom_point(data = met, aes(y = -invKT.C.StMean, x = -Tanom.iktCs, size = lER),
position = position_jitter(width = 0.05, height = 0.025), shape = 21, fill = "transparent", alpha= 0.5) +
scale_size_binned(n.breaks = 4, nice.breaks = TRUE) +
ylab("Mean summer temp (-1 * centered 1/kT)") +
xlab("Mean daily temp (-1 * centered 1/kT)")
```
```{r}
gamPred2w <- gamPred2 %>%
pivot_wider(id_cols = Tanom.iktCs:doy, names_from = treatment, values_from = c(fit,se.fit)) %>%
mutate(N_amb = exp(fit_nitrogen)/exp(fit_ambient),
P_amb = exp(fit_phosphorus)/exp(fit_ambient))
gamPred2w2 <- gamPred2w %>%
select(Tanom.iktCs:doy, fit_ambient:P_amb) %>%
pivot_longer(cols = N_amb:P_amb, names_to = "treatment", values_to = "effectSize") %>%
mutate(treatment = fct_recode(treatment, Nitrogen = "N_amb", Phosphorus = "P_amb"))
```
```{r}
pdf(file.path(here::here("03_Plots"), "02b_ERpredictionSurfaces.pdf"), height = 4, width = 8)
ErPredSur
dev.off()
```
```{r}
pdf(file.path(here::here("03_Plots"), "02c_ERdifferencePlots2.pdf"), height = 5, width = 4)
ggplot(gamPred2w2, aes(y = -invKT.C.StMean, x = -Tanom.iktCs, fill = effectSize)) +
geom_raster() +
scale_fill_distiller(palette = "Spectral") +
ylab("Mean summer temp (-1 * centered 1/kT)") +
xlab("Mean daily temp (-1 * centered 1/kT)") +
facet_wrap(vars(treatment), nrow = 2, ncol = 1)
dev.off()
```
#########################################
# ER_N
#########################################
# summary Plots
Focusing on this
```{r}
summary(g.er.BEST1$gam)
```
## Predictions
```{r}
pdf(file.path(here::here("03_Plots"), "02b_ERpredXdoy_Nonly.pdf"), height = 4, width = 8)
ggplot() +
geom_point(data = met, aes(y = lER, x = doy, color = treatment)) +
geom_line(data = met, aes(y = ERgamPredNonly, x = doy, color = treatment)) +
geom_ribbon(data = met, aes(ymin = ERgamPredNonly - ERgamPredNonly.se,
ymax = ERgamPredNonly + ERgamPredNonly.se, x = doy, fill = treatment), alpha = 0.5) +
facet_grid(stream ~ treatment)
dev.off()
```
Use model to predict log ER, with Qres, doy and stream set to "centered" values
```{r}
gamPred2 <- predict_gam(g.er.BEST1$gam, exclude_terms = c(
"s(Qres):streamst11U",
"s(Qres):streamst18",
"s(Qres):streamst6",
"s(Qres):streamst9",
"s(DOYres)"),
values = list(
Qres = 0,
DOYres = 8.1, # this is the median
stream = "st6"))
```
**NOTE: TEMPERATURE AXIS ARE FLIPPED SO LARGE #'S ARE WARM**
During amb year, ER increases with daily temp and with annual temp across streams. Duing the N and P years, this switches. We see ER increasing with annual temp again, but decreasing with daily temp. Cool
```{r}
#note inversing temp axis here
ErPredSur <- ggplot() +
geom_raster(data = gamPred2, aes(y = invKT.C.StMean, x = Tanom.iktCs, fill = fit)) +
facet_wrap(vars(treatment)) +
scale_fill_distiller(palette = "Spectral") +
geom_point(data = met, aes(y = invKT.C.StMean, x = Tanom.iktCs, size = lER),
position = position_jitter(width = 0.05, height = 0.025), shape = 21, fill = "transparent", alpha= 0.5) +
scale_size_binned(n.breaks = 4, nice.breaks = TRUE) +
ylab("Mean summer temp (-1 * centered 1/kT)") +
xlab("Mean daily temp (-1 * centered 1/kT)")
```
```{r}
gamPred2w <- gamPred2 %>%
pivot_wider(id_cols = invKT.C.StMean:stream, names_from = treatment, values_from = c(fit,se.fit)) %>%
mutate(N_amb = exp(fit_nitrogen)/exp(fit_ambient))
gamPred2w2 <- gamPred2w %>%
# select(Tanom.iktCs:doy, fit_ambient:P_amb) %>%
pivot_longer(cols = N_amb, names_to = "treatment", values_to = "effectSize") %>%
mutate(treatment = fct_recode(treatment, Nitrogen = "N_amb"))
```
```{r}
pdf(file.path(here::here("03_Plots"), "02b_ERpredSurNonly.pdf"), height = 4, width = 8)
ErPredSur
dev.off()
```
```{r}
pdf(file.path(here::here("03_Plots"), "02b_ERdifPlotsNonly.pdf"), height = 5, width = 4)
ggplot() +
geom_raster(data = gamPred2w2, aes(y = -invKT.C.StMean, x = -Tanom.iktCs, fill = effectSize)) +
geom_point(data = met, aes(y = -invKT.C.StMean, x = -Tanom.iktCs, size = lER), alpha = 30/100)+
scale_fill_distiller(palette = "Spectral") +
ylab("Mean summer temp (-1 * centered 1/kT)") +
xlab("Mean daily temp (-1 * centered 1/kT)")
dev.off()
```
#########################################
# ER_P
#########################################
# summary Plots
Focusing on this
```{r}
summary(g.er.BEST1$gam)
```
## Predictions
```{r}
pdf(file.path(here::here("03_Plots"), "02c_Ponly_ERpredXdoyPonly.pdf"), height = 4, width = 8)
ggplot() +
geom_point(data = met, aes(y = lER, x = doy, color = treatment)) +
geom_line(data = met, aes(y = ERgamPred, x = doy, color = treatment)) +
geom_ribbon(data = met, aes(ymin = ERgamPred - ERgamPred.se,
ymax = ERgamPred + ERgamPred.se, x = doy, fill = treatment), alpha = 0.5) +
facet_grid(stream ~ treatment)
dev.off()
```
Use model to predict log ER, with Qres, doy and stream set to "centered" values
```{r}
gamPred2 <- predict_gam(g.er.BEST1$gam,exclude_terms = c(
"s(Qres):streamst11U",
"s(Qres):streamst18",
"s(Qres):streamst6",
"s(Qres):streamst9",
"s(DOYres):streamst11U",
"s(DOYres):streamst18",
"s(DOYres):streamst6",
"s(DOYres):streamst9"),
values = list(
Qres = 0,
DOYres = 6.1)) # median in this dataset
```
```{r}
gamPred2w <- gamPred2 %>%
pivot_wider(id_cols = c(stream:Tanom.iktCs,Qres:DOYres), names_from = treatment, values_from = c(fit,se.fit)) %>%
mutate(P_amb = exp(fit_phosphorus)/exp(fit_ambient))
# gamPred2w2 <- gamPred2w %>%
# select(Tanom.iktCs:doy, fit_ambient:P_amb) %>%
# pivot_longer(cols = N_amb:P_amb, names_to = "treatment", values_to = "effectSize") %>%
# mutate(treatment = fct_recode(treatment, Nitrogen = "N_amb", Phosphorus = "P_amb"))
```
**NOTE: TEMPERATURE AXIS ARE FLIPPED SO LARGE #'S ARE WARM**
During amb year, ER increases with daily temp and with annual temp across streams. Duing the N and P years, this switches. We see ER increasing with annual temp again, but decreasing with daily temp. Cool
```{r}
ErPredSur <- ggplot() +
geom_raster(data = gamPred2, aes(y = stream, x = -Tanom.iktCs, fill = fit)) +
scale_fill_distiller(palette = "Spectral") +
geom_point(data = met, aes(y = stream, x = -Tanom.iktCs, size = lER),
position = position_jitter(width = 0, height = 0.075), shape = 21, fill = "transparent", alpha= 0.5) +
scale_size_binned(n.breaks = 4, nice.breaks = TRUE) +
facet_wrap(vars(treatment)) +
ylab("stream") +
xlab("Mean daily temp (-1 * centered 1/kT)")
```
```{r}
pdf(file.path(here::here("03_Plots"), "02c_ERpredSur_Ponly.pdf"), height = 4, width = 8)
ErPredSur
dev.off()
```
```{r}
pdf(file.path(here::here("03_Plots"), "02c_ERdifPlotsPonly.pdf"), height = 5, width = 4)
ggplot() +
geom_raster(data = gamPred2w, aes(y = stream, x = -Tanom.iktCs, fill = P_amb)) +
geom_point(data = met, aes(y = stream, x = -Tanom.iktCs, size = lER),
position = position_jitter(width = 0, height = 0.075), shape = 21, fill = "transparent", alpha= 0.5) +
scale_fill_distiller(palette = "Spectral") +
xlab("Mean daily temp (-1 * centered 1/kT)") +
ylab("stream")
dev.off()
```
#########################################
# GPP_All
#########################################
# These aren't too useful for interactions
```{r echo=FALSE}
GPPquickPlot1 <- plot(g.gpp.BEST1.gv, allTerms = 1) +
l_points(color = "pink") + l_fitLine(linetype = 3) + l_fitContour() +
l_ciLine(colour = 2) + l_ciBar() + l_fitPoints(size = 1, col = 2) + theme_get() + labs(title = NULL)
print(GPPquickPlot1, pages = 1)
```
```{r echo=FALSE}
GPPquickPlot2 <- plot(g.gpp.BEST2.gv, allTerms = 1) +
l_points(color = "pink") + l_fitLine(linetype = 3) + l_fitContour() +
l_ciLine(colour = 2) + l_ciBar() + l_fitPoints(size = 1, col = 2) + theme_get() + labs(title = NULL)
print(GPPquickPlot2, pages = 1)
```
```{r echo=FALSE}
GPPquickPlot3 <- plot(g.gpp.BEST3.gv, allTerms = 1) +
l_points(color = "pink") + l_fitLine(linetype = 3) + l_fitContour() +
l_ciLine(colour = 2) + l_ciBar() + l_fitPoints(size = 1, col = 2) + theme_get() + labs(title = NULL)
print(GPPquickPlot3, pages = 1)
```
Predict for future ploting
```{r echo= FALSE}
met$GPPgamPred <- predict.gam(g.gppBEST3$gam, met, type = "response")
met$GPPgamPred.se <- predict.gam(g.gppBEST3$gam, met, type = "response", se.fit = TRUE)$se.fit
```
#########################################
# GPP_N
#########################################
# These aren't too useful for interactions
```{r echo=FALSE}
GPPquickPlot1 <- plot(g.gpp.BEST1.gv, allTerms = 1) +
l_points(color = "pink") + l_fitLine(linetype = 3) + l_fitContour() +
l_ciLine(colour = 2) + l_ciBar() + l_fitPoints(size = 1, col = 2) + theme_get() + labs(title = NULL)
print(GPPquickPlot1, pages = 1)
```
```{r echo=FALSE}
GPPquickPlot2 <- plot(g.gpp.BEST2.gv, allTerms = 1) +
l_points(color = "pink") + l_fitLine(linetype = 3) + l_fitContour() +
l_ciLine(colour = 2) + l_ciBar() + l_fitPoints(size = 1, col = 2) + theme_get() + labs(title = NULL)
print(GPPquickPlot2, pages = 1)
```
```{r echo=FALSE}
GPPquickPlot3 <- plot(g.gpp.BEST3.gv, allTerms = 1) +
l_points(color = "pink") + l_fitLine(linetype = 3) + l_fitContour() +
l_ciLine(colour = 2) + l_ciBar() + l_fitPoints(size = 1, col = 2) + theme_get() + labs(title = NULL)
print(GPPquickPlot3, pages = 1)
```
|
context("test-tbl_stack")
testthat::skip_on_cran()
library(survival)
t1 <-
glm(response ~ trt, trial, family = binomial) %>%
tbl_regression(
exponentiate = TRUE,
label = list("trt" ~ "Treatment (unadjusted)")
)
t2 <-
glm(response ~ trt + grade + stage + marker, trial, family = binomial) %>%
tbl_regression(
include = "trt",
exponentiate = TRUE,
label = list("trt" ~ "Treatment (adjusted)")
)
t3 <-
coxph(Surv(ttdeath, death) ~ trt, trial) %>%
tbl_regression(
exponentiate = TRUE,
label = list("trt" ~ "Treatment (unadjusted)")
)
t4 <-
coxph(Surv(ttdeath, death) ~ trt + grade + stage + marker, trial) %>%
tbl_regression(
include = "trt",
exponentiate = TRUE,
label = list("trt" ~ "Treatment (adjusted)")
)
row1 <- tbl_merge(list(t1, t3), tab_spanner = c("Tumor Response", "Death"))
row2 <- tbl_merge(list(t2, t4))
test_that("Stacking tbl_regression objects", {
expect_error(
tbl_stack(list(t1, t2), group_header = c("Group 1", "Group 2")),
NA
)
# must pass items as list
expect_error(
tbl_stack(t1, t2),
NULL
)
# must pass acceptable objects
expect_error(
tbl_stack(list(mtcars)),
NULL
)
})
test_that("Stacking tbl_merge objects", {
expect_error(
tbl_stack(list(row1, row2)),
NA
)
})
test_that("Stacking tbl_summary objects", {
yy <- tbl_summary(trial, by = response) %>%
add_p() %>%
add_q()
tt <- tbl_summary(trial, by = trt) %>%
add_p() %>%
add_q()
expect_error(
zz <- tbl_stack(list(yy, tt)),
NA
)
# no error if the list is named
lst_summary <- list(yy, tt) %>% set_names("one", "two")
expect_error(
tbl_stack(lst_summary, group_header = c("Group 1", "Group 2")),
NA
)
})
|
/tests/testthat/test-tbl_stack.R
|
permissive
|
shijianasdf/gtsummary
|
R
| false | false | 1,752 |
r
|
context("test-tbl_stack")
testthat::skip_on_cran()
library(survival)
t1 <-
glm(response ~ trt, trial, family = binomial) %>%
tbl_regression(
exponentiate = TRUE,
label = list("trt" ~ "Treatment (unadjusted)")
)
t2 <-
glm(response ~ trt + grade + stage + marker, trial, family = binomial) %>%
tbl_regression(
include = "trt",
exponentiate = TRUE,
label = list("trt" ~ "Treatment (adjusted)")
)
t3 <-
coxph(Surv(ttdeath, death) ~ trt, trial) %>%
tbl_regression(
exponentiate = TRUE,
label = list("trt" ~ "Treatment (unadjusted)")
)
t4 <-
coxph(Surv(ttdeath, death) ~ trt + grade + stage + marker, trial) %>%
tbl_regression(
include = "trt",
exponentiate = TRUE,
label = list("trt" ~ "Treatment (adjusted)")
)
row1 <- tbl_merge(list(t1, t3), tab_spanner = c("Tumor Response", "Death"))
row2 <- tbl_merge(list(t2, t4))
test_that("Stacking tbl_regression objects", {
expect_error(
tbl_stack(list(t1, t2), group_header = c("Group 1", "Group 2")),
NA
)
# must pass items as list
expect_error(
tbl_stack(t1, t2),
NULL
)
# must pass acceptable objects
expect_error(
tbl_stack(list(mtcars)),
NULL
)
})
test_that("Stacking tbl_merge objects", {
expect_error(
tbl_stack(list(row1, row2)),
NA
)
})
test_that("Stacking tbl_summary objects", {
yy <- tbl_summary(trial, by = response) %>%
add_p() %>%
add_q()
tt <- tbl_summary(trial, by = trt) %>%
add_p() %>%
add_q()
expect_error(
zz <- tbl_stack(list(yy, tt)),
NA
)
# no error if the list is named
lst_summary <- list(yy, tt) %>% set_names("one", "two")
expect_error(
tbl_stack(lst_summary, group_header = c("Group 1", "Group 2")),
NA
)
})
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cloudresourcemanager_objects.R
\name{Project.labels}
\alias{Project.labels}
\title{Project.labels Object}
\usage{
Project.labels()
}
\value{
Project.labels object
}
\description{
Project.labels Object
}
\details{
Autogenerated via \code{\link[googleAuthR]{gar_create_api_objects}}
The labels associated with this Project.Label keys must be between 1 and 63 characters long and must conformto the following regular expression: \[a-z\](\[-a-z0-9\]*\[a-z0-9\])?.Label values must be between 0 and 63 characters long and must conformto the regular expression (\[a-z\](\[-a-z0-9\]*\[a-z0-9\])?)?.No more than 256 labels can be associated with a given resource.Clients should store labels in a representation such as JSON that does notdepend on specific characters being disallowed.Example: <code>'environment' : 'dev'</code>Read-write.
}
\seealso{
Other Project functions: \code{\link{Project}},
\code{\link{projects.create}},
\code{\link{projects.update}}
}
|
/googlecloudresourcemanagerv1beta1.auto/man/Project.labels.Rd
|
permissive
|
GVersteeg/autoGoogleAPI
|
R
| false | true | 1,036 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cloudresourcemanager_objects.R
\name{Project.labels}
\alias{Project.labels}
\title{Project.labels Object}
\usage{
Project.labels()
}
\value{
Project.labels object
}
\description{
Project.labels Object
}
\details{
Autogenerated via \code{\link[googleAuthR]{gar_create_api_objects}}
The labels associated with this Project.Label keys must be between 1 and 63 characters long and must conformto the following regular expression: \[a-z\](\[-a-z0-9\]*\[a-z0-9\])?.Label values must be between 0 and 63 characters long and must conformto the regular expression (\[a-z\](\[-a-z0-9\]*\[a-z0-9\])?)?.No more than 256 labels can be associated with a given resource.Clients should store labels in a representation such as JSON that does notdepend on specific characters being disallowed.Example: <code>'environment' : 'dev'</code>Read-write.
}
\seealso{
Other Project functions: \code{\link{Project}},
\code{\link{projects.create}},
\code{\link{projects.update}}
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/groupZscores.R
\name{groupZscores}
\alias{groupZscores}
\title{Calculate z-scores for each peak}
\usage{
groupZscores(zscore)
}
\arguments{
\item{zscore}{A vector of numeric.
It is the z-scores of ratios for each window.}
}
\value{
A data.frame with column names as "zscore", "group", "grp.zscore",
and "pvalue".
}
\description{
Detect peaks and calculate z-scores for each peak
}
\examples{
x <- seq_len(500)
a <- 2 * 2*pi/length(x)
y <- 20 * sin(x*a)
noise1 <- 20 * 1/10 * sin(x*a*10)
zscore <- y+noise1
groupZscores(zscore)
}
|
/man/groupZscores.Rd
|
no_license
|
LihuaJulieZhu/NADfinder
|
R
| false | true | 609 |
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/groupZscores.R
\name{groupZscores}
\alias{groupZscores}
\title{Calculate z-scores for each peak}
\usage{
groupZscores(zscore)
}
\arguments{
\item{zscore}{A vector of numeric.
It is the z-scores of ratios for each window.}
}
\value{
A data.frame with column names as "zscore", "group", "grp.zscore",
and "pvalue".
}
\description{
Detect peaks and calculate z-scores for each peak
}
\examples{
x <- seq_len(500)
a <- 2 * 2*pi/length(x)
y <- 20 * sin(x*a)
noise1 <- 20 * 1/10 * sin(x*a*10)
zscore <- y+noise1
groupZscores(zscore)
}
|
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