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C-3PR.R
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C-3PR.R
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# C-3PR
# ========
# ### Evaluating Scientific **C**laims: **P**ost **P**ublication **P**eer **R**eview, **R**eplication and **R**eproduction
#
# An R function library created in the contex of analysing and visualising data generated by several Open Science intiatives.
#
# *C-3PR Created by [Fred Hasselman](http://fredhasselman.com) on behalf of the ManyLabs1, ManyLabs2, RPP and Curate Science projects*
#
# Some functions included are based on code by others (adjusted in varying degrees of mutilation)
# SOURCE FROM GITHUB -------------------------------------------------
#
# Use this code (devtools package) to source it directly from GitHub:
# require(devtools)
# source_url("https://raw.githubusercontent.com/FredHasselman/toolboxR/master/C-3PR.R")
# Package install / load / unload -----------------------------------------
# Packages in the list argument need will be installed if necessary and loaded
in.IT <- function(need=NULL,inT=TRUE){
ip <- .packages(all.available=TRUE)
if(any((need %in% ip)==FALSE)){
if(inT==TRUE){
install.packages(need[!(need %in% ip)])
} else {
cat('Package(s):\n',paste(need[(need %in% ip)==FALSE],sep='\n'),'\nnot installed.\nUse in.IT(c("packagename1","packagename2",...),inT=TRUE)')
need <- need[(need %in% ip)==TRUE]
}
}
ok <- sapply(1:length(need),function(p) require(need[[p]],character.only=TRUE))
}
# Packages in the list argument loose will be unloaded, if necessary (unT=T) uninstalled
un.IT <- function(loose,unT=FALSE){
dp <- .packages()
if(any(loose %in% dp)){
for(looseLib in loose[(loose %in% dp)]){detach(paste0("package:",looseLib), unload=TRUE,character.only=TRUE)}
}
rm(dp)
if(unT==TRUE){
dp <- .packages(all.available=TRUE)
if(any(loose %in% dp)){remove.packages(loose[(loose %in% dp)])}
}
}
in.IO <- function(){
# I/O and data handling tools
in.IT(c("xlsx","plyr","doBy","reshape2","RCurl","XML","httr","dplyr"))
}
in.PAR <- function(){
# Parallel computing tools
in.IT(c("parallel","doParallel","foreach"))
}
in.PLOT <- function(useArial = F,afmPATH="~/Dropbox/Public"){
# Load packages for plotting with default option to setup Arial as the pdf font for use in figures.
if(useArial==T){
# Set up PDF device on MAC OSX to use Arial as a font in Graphs
set.Arial(afmPATH)
}
in.IT(c("lattice","latticeExtra","gplots","ggplot2","grid","gridExtra","scales","beanplot","effects","RColorBrewer"))
}
# DATA loading and cleaning -----------------------------------------------
df.Clean <- function(df,Sep="."){
in.IT('dplyr')
nms <- colnames(df)
rws <- rownames(df)
# Change punctuation and blankss in variable names to points
nmsP <- gsub("([[:punct:]]|[[:blank:]])+","+",nms)
nmsPP <- gsub("(^[+]|[+]$)+","",nmsP)
nmsPP <- gsub("[+]",Sep,nmsPP)
# Check for double names
ifelse(length(unique(nmsPP))==length(nmsPP),{nms <- nmsPP},{
id2 <- which(plyr::laply(nmsPP,function(n) sum(nmsPP%in%n))>1)
nms <- nmsPP
nms[id2] <- paste(nmsPP[id2],id2,sep=".")})
colnames(df) <- nms
df <- dplyr::select(df,which(nms%in%nms[nms!=""]))
df[ ,1] <- paste0("Row.",seq(1,nrow(df)))
colnames(df)[1] <- paste("Local","ID",sep=Sep)
return(list(df=df,
nms=nms,
rws=rws))
}
get.GoogleSheet <- function(url=NULL,data=c('ML1data','ML2masteRkey','RPPdata')[3],dfCln=FALSE,Sep = "."){
in.IT(c('dplyr','httr'))
if(is.null(url)){
switch(data,
ML1data = url <- 'https://docs.google.com/spreadsheets/d/19ay71M8jiqIZhSj3HR0vqaJUwAae1QHzBybjBu5yBg8/export?format=csv',
ML2masteRkey = url <- 'https://docs.google.com/spreadsheets/d/1fqK3WHwFPMIjNVVvmxpMEjzUETftq_DmP5LzEhXxUHA/export?format=csv',
RPPdata = url <- ''
)}
# GET(url) will only get 100 rows, thanks to Sacha Epskamp for this "complete scrape" code.
tmp <- tempfile()
info <- GET(url, write_disk(tmp, overwrite = TRUE))
df <- tbl_df(read.csv(tmp,stringsAsFactors=F, fill = T,header=T))
if(dfCln==TRUE){df <- df.Clean(df)} else {df$df <- df}
return(list(df = df$df,
info = list(Info=info,
GoogleSheet.colnames=tbl_df(data.frame(ori.colnames=df$nms)),
GoogleSheet.rownames=tbl_df(data.frame(ori.rownames=df$rws)))))
}
get.OSFfile <- function(# Function to download OSF file modified from code by Sacha Epskamp
code, #Either "https://osf.io/XXXXX/" or just the code
dir = tempdir(), # Output location
scanMethod, # "readLines" or "RCurl". Leave missing to automatically chose
downloadMethod = c("httr","downloader","curl"), # First one is chosen
dataFrame = TRUE,
sep = ',',
dfCln = FALSE
){
# Check if input is code:
if (!grepl("osf\\.io",code)){
URL <- sprintf("https://osf.io/%s/",code)
} else URL <- code
# Scan page:
if (grepl("Windows",Sys.info()[['sysname']],ignore.case=TRUE)){
try(setInternet2(TRUE))
}
if (missing(scanMethod)){
scanMethod <- ifelse(grepl("Windows",Sys.info()[['sysname']],ignore.case=TRUE), "readLines", "RCurl")
}
if (scanMethod == "readLines"){
Page <- paste(readLines(URL),collapse="\n")
} else if (scanMethod == "RCurl"){
library("RCurl")
Page <- RCurl::getURL(URL)
} else if (scanMethod == "httr"){
Page <- httr::GET(URL)
Page <- paste(Page,collapse="\n")
} else stop("Invalid scanMethod")
# Extract download link(s):
# Link <- regmatches(Page, regexpr("(?<=download: \\').*?\\?action=download(?=\\')", Page, perl = TRUE))
#
# # Stop if no download link:
# if (length(Link)==0){
# stop("No download link found")
# }
# # (just in case) if more than one, warning:
# if (length(Link)>1){
# warning("Multiple download links found, only first is used")
# Link <- Link[1]
# }
# Create download link:
URL <- gsub("/$","",URL)
# Link <- paste0(URL,"/?action=download&version=1")
Link <- paste0(URL,"/?action=download")
# Extract file name:
FileName <- regmatches(Page,gregexpr("(?<=\\<title\\>OSF \\| ).*?(?=\\</title\\>)", Page, perl=TRUE))[[1]]
FullPath <- paste0(dir,"/",FileName)
info <- NULL
# Download file:
if (downloadMethod[[1]]=="httr"){
library("httr")
info <- httr::GET(Link, httr::write_disk(FullPath, overwrite = TRUE))
} else if (downloadMethod[[1]]=="downloader"){
library("downloader")
downloader:::download(Link, destfile = FullPath, quiet=TRUE)
} else if (downloadMethod[[1]]=="curl"){
system(sprintf("curl -J -L %s > %s", Link, FullPath), ignore.stderr = TRUE)
} else stop("invalid downloadMethod")
df <- NULL
if(dataFrame==TRUE){
if(grepl('xls',FileName)){
df <- tbl_df(read.xlsx2(file=FullPath,sheetIndex=1))
} else {
df <- tbl_df(read.table(FullPath,stringsAsFactors=F,fill = T,header=T,sep=sep, comment.char = "",quote = "\""))
}
if(dfCln==TRUE){df <- df.Clean(df)} else {df$df <- df}
return(list(df = df$df,
info = list(FilePath=FullPath,
Info=info,
ori.Colnames=tbl_df(data.frame(ori.colnames=df$nms)),
ori.Rownames=tbl_df(data.frame(ori.rownames=df$rws))
)))
} else {
# Return location of file:
return(FilePath=FullPath)
}
}
get.Order <- function(df, S1=TRUE){
ifelse(S1,{
url <- "https://docs.google.com/spreadsheets/d/1al8b5nv9AoNdOOlI-RfXf5bCTJY2ophdUIiYhCFGegI/pub?gid=269287357&single=true&output=csv"
cols <- 3:15
},{
url <- "https://docs.google.com/spreadsheets/d/1AvJguRhN8i7MsbcjnGtEnNZ9b-LNTNKINSoHm8Z2f28/pub?gid=1370595041&single=true&output=csv"
cols <- 3:17
}
)
df.Order <- get.GoogleSheet(url = url)$df
ProblemID <- list()
cnt = 0
for(i in 1:nrow(df)){
if((nchar(df$StudyOrder[i])==0)|(df$IDiffOrder[i]=="")){
cnt = cnt + 1
if((nchar(df$StudyOrder[i])==0)&(df$IDiffOrder[i]=="")){
df$StudyOrderN[i] <- NA
df$IDiffOrderN[i] <- NA
Problem = paste("No study order strings in",df$StudyOrder[i],"and",df$IDiffOrder[i])
} else {
if(nchar(df$StudyOrder[i])==0){ df$StudyOrderN[i] <- NA}
if(df$IDiffOrder[i]==""){
df$IDiffOrderN[i] <- NA
Problem = paste("Wrong study order var? Ind.Diff var",df$IDiffOrder[i],"is empty")
}
}
ProblemID[[cnt]] <- cbind(rowNum = i,
fileName = df$.id[i],
ResponseID = df$ResponseID[i],
Problem = Problem,
StudyOrder = df$StudyOrder[i],
IDiffOrder = df$IDiffOrder[i])
} else {
OrderString <- unlist(strsplit(x = df$StudyOrder[i], split = "[|]"))
ls <- list()
Problem.s <- list()
cnt2 <- 0
for(s in 1:length(OrderString)){
ls[[s]] <- colnames(df.Order)[cols][df.Order[df.Order$Filename%in%df$.id[i], cols] %in% OrderString[[s]]]
if(length(ls[[s]])==0){
cnt2 = cnt2 + 1
if(OrderString[[s]]%in%df.Order[df.Order$Filename%in%df$.id[i], cols]){
Problem.s[[cnt2]] <- paste(OrderString[[s]],"(", colnames(df.Order)[cols][df.Order[df.Order$Filename%in%df$.id[i], cols] == OrderString[[s]]], ")")
ls[[s]] <- NA
} else {
Problem.s[[cnt2]] <- paste(OrderString[[s]], "( mismatch )")
}
}
}
if(cnt2!=0){
cnt = cnt + 1
ProblemID[[cnt]] <- cbind(rowNum = i, fileName = df$.id[i],
ResponseID = df$ResponseID[i],
Problem = paste(unlist(Problem.s), collapse="|"),
StudyOrder = paste(unlist(ls) ,collapse="|"),
IDiffOrder = df$IDiffOrder[i])
}
df$StudyOrderN[i] <- paste(unlist(ls) ,collapse="|")
}
df$IDiffOrderN[i] <- df$IDiffOrder[i]
}
return(list(df = df,
Problems = ldply(ProblemID))
)
}
# PLOTS -------------------------------------------------------------------
disp <- function(message='Hello world!', header = TRUE, footer = TRUE){
mWidth <- max(laply(message,nchar))
if(is.character(header)){
hWidth <- max(laply(header,nchar))
mWidth <- max(hWidth,mWidth)
}
dmessage <- list()
for(m in 1:length(message)){
# b <- floor((mWidth-nchar(message[m]))/2)
e <- mWidth-nchar(message[m])
dmessage[[m]] <- paste0('§ ',message[m]) #,paste0(rep(' ',e),collapse=""),'\n\t')
#paste0('§ ',paste0(rep(" ",mWidth),collapse=""),' §'))
}
# if(m > 1){dmessage[[m]] <- paste0(dmessage[[m]],}
# mWidth <- max(laply(dmessage, nchar))
banner <- paste0(rep('~', mWidth), collapse = "")
if(is.character(header)){
b <- floor((nchar(banner)-nchar(header))/2)
e <- ceiling((nchar(banner)-nchar(header))/2)
leader <- paste0('\n\t',paste0(rep('~',b),collapse=""),header,paste0(rep('~',e),collapse=""))
}
if(header == TRUE){
leader <- banner
}
if(header == FALSE){
leader <- paste0('§') #,paste0(rep(" ",nchar(banner)-2),collapse="")) #,'§')
}
if(footer){
cat(paste0('\n\t',leader,'\n\t',dmessage,'\n\t',banner,'\n'))
} else {
cat(paste0('\n\t',leader,'\n\t',dmessage))
}
}
gg.theme <- function(type=c("clean","noax")[1],useArial = F, afmPATH="~/Dropbox"){
require(ggplot2)
if(useArial){
set.Arial(afmPATH)
bf_font="Arial"
} else {bf_font="Helvetica"}
switch(type,
clean = theme_bw(base_size = 16, base_family=bf_font) +
theme(axis.text.x = element_text(size = 14),
axis.title.y = element_text(vjust = +1.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.background = element_blank(),
legend.key = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black")),
noax = theme(line = element_blank(),
text = element_blank(),
title = element_blank(),
plot.background = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
)
}
plotHolder <- function(useArial = F,afmPATH="~/Dropbox"){
require(ggplot2)
ggplot() +
geom_blank(aes(1,1)) +
theme(line = element_blank(),
text = element_blank(),
title = element_blank(),
plot.background = element_blank(),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank()
# axis.title.x = element_blank(),
# axis.title.y = element_blank(),
# axis.text.x = element_blank(),
# axis.text.y = element_blank(),
# axis.ticks = element_blank()
)
}
set.Arial <- function(afmPATH="~/Dropbox"){
# Set up PDF device on MAC OSX to use Arial as a font in Graphs
if(nchar(afmPATH>0)){
if(file.exists(paste0(afmPATH,"/Arial.afm"))){
Arial <- Type1Font("Arial",
c(paste(afmPATH,"/Arial.afm",sep=""),
paste(afmPATH,"/Arial Bold.afm",sep=""),
paste(afmPATH,"/Arial Italic.afm",sep=""),
paste(afmPATH,"/Arial Bold Italic.afm",sep="")))
if(!"Arial" %in% names(pdfFonts())){pdfFonts(Arial=Arial)}
if(!"Arial" %in% names(postscriptFonts())){postscriptFonts(Arial=Arial)}
return()
} else {disp(header='useArial=TRUE',message='The directory did not contain the *.afm version of the Arial font family')}
} else {disp(header='useArial=TRUE',message='Please provide the path to the *.afm version of the Arial font family')}
}
#' Graphical display of a textual table.
#'
#' @param d data.frame or matrix
#' @param widths optional vector to specify column widths
#' @param heights optional vector to specify row heights
#' @param fg.par control parameters for text grobs
#' @param bg.par control parameters for rect grobs
#' @param padding unit of length 2
#' @export
#' @examples
#' \donttest{
#' d <- head(iris, 3)
#' core <- gtable_table(d,
#' fg.par = list(col=1:5, fontsize=c(10,12,15)),
#' bg.par = list(fill=1:2, alpha=0.5))
#' colhead <- gtable_table(t(colnames(d)))
#' rowhead <- gtable_table(c("", rownames(d)))
#' g <- rbind(colhead, core)
#' g <- cbind(rowhead, g)
#' grid.newpage()
#' grid.draw(g)
#' g2 <- gtable_table(t(colnames(d)), widths = unit(rep(1, ncol(d)), "null"))
#' grid.newpage()
#' grid.draw(g)
#' }
gtable_table <- function(d, widths, heights,
fg.par = list(col = "black"),
bg.par = list(fill = NA),
padding = unit(c(4, 4), "mm")){
label_matrix <- as.matrix(d)
nc <- ncol(label_matrix)
nr <- nrow(label_matrix)
n <- nc*nr
fg.par <- lapply(fg.par, rep, length.out = n)
bg.par <- lapply(bg.par, rep, length.out = n)
fg.param <- data.frame(fg.par, label = as.vector(label_matrix),
stringsAsFactors=FALSE)
bg.param <- data.frame(bg.par, id = seq_len(n),
stringsAsFactors=FALSE)
labels <- plyr::mlply(fg.param, cell_content)
backgrounds <- plyr::mlply(bg.param, cell_background)
label_grobs <- matrix(labels, ncol = nc)
## some calculations of cell sizes
row_heights <- function(m){
do.call(unit.c, apply(m, 1, function(l)
max(do.call(unit.c, lapply(l, grobHeight)))))
}
col_widths <- function(m){
do.call(unit.c, apply(m, 2, function(l)
max(do.call(unit.c, lapply(l, grobWidth)))))
}
if(missing(widths))
widths <- col_widths(label_grobs) + padding[1]
if(missing(heights))
heights <- row_heights(label_grobs) + padding[2]
## place labels in a gtable
g <- gtable_matrix("table", grobs=label_grobs,
widths = widths,
heights = heights)
## add the background
g <- gtable_add_grob(g, backgrounds, t=rep(seq_len(nr), each=nc),
l=rep(seq_len(nc), nr), z=0, name="fill")
g
}
cell_content <- function(...){
dots <- list(...)
gpar.names <- c("col", "cex", "fontsize", "lineheight",
"font", "fontfamily", "fontface", "alpha")
other.names <- c("label", "hjust", "vjust", "rot", "x", "y")
gpar.args <- dots[intersect(names(dots), gpar.names)]
gp <- do.call(gpar, gpar.args)
other.args <- dots[intersect(names(dots), other.names)]
do.call(textGrob, c(other.args, list(gp = gp)))
}
cell_background <- function(...){
dots <- list(...)
gpar.names <- c("fill", "col", "lty", "lwd", "cex", "alpha",
"lineend", "linejoin", "linemitre",
"lex")
gpar.args <- dots[intersect(names(dots), gpar.names)]
gp <- do.call(gpar, gpar.args)
do.call(rectGrob, list(gp = gp))
}
gtable_arrange <- function(..., grobs=list(), as.table=TRUE,
top = NULL, bottom = NULL,
left = NULL, right = NULL, draw=TRUE){
require(gtable)
# alias
gtable_add_grobs <- gtable_add_grob
dots <- list(...)
params <- c("nrow", "ncol", "widths", "heights",
"respect", "just", "z") # TODO currently ignored
layout.call <- intersect(names(dots), params)
params.layout <- dots[layout.call]
if(is.null(names(dots)))
not.grobnames <- FALSE else
not.grobnames <- names(dots) %in% layout.call
if(!length(grobs))
grobs <- dots[! not.grobnames ]
## figure out the layout
n <- length(grobs)
nm <- n2mfrow(n)
if(is.null(params.layout$nrow) & is.null(params.layout$ncol))
{
params.layout$nrow = nm[1]
params.layout$ncol = nm[2]
}
if(is.null(params.layout$nrow))
params.layout$nrow = ceiling(n/params.layout$ncol)
if(is.null(params.layout$ncol))
params.layout$ncol = ceiling(n/params.layout$nrow)
if(is.null(params.layout$widths))
params.layout$widths <- unit(rep(1, params.layout$ncol), "null")
if(is.null(params.layout$heights))
params.layout$heights <- unit(rep(1,params.layout$nrow), "null")
positions <- expand.grid(row = seq_len(params.layout$nrow),
col = seq_len(params.layout$ncol))
if(as.table) # fill table by rows
positions <- positions[order(positions$row),]
positions <- positions[seq_along(grobs), ] # n might be < ncol*nrow
## build the gtable, similar steps to gtable_matrix
gt <- gtable(name="table")
gt <- gtable_add_cols(gt, params.layout$widths)
gt <- gtable_add_rows(gt, params.layout$heights)
gt <- gtable_add_grobs(gt, grobs, t = positions$row,
l = positions$col)
## titles given as strings are converted to text grobs
if (is.character(top))
top <- textGrob(top)
if (is.character(bottom))
bottom <- textGrob(bottom)
if (is.character(right))
right <- textGrob(right, rot = -90)
if (is.character(left))
left <- textGrob(left, rot = 90)
if(!is.null(top)){
gt <- gtable_add_rows(gt, heights=grobHeight(top), 0)
gt <- gtable_add_grobs(gt, top, t=1, l=1, r=ncol(gt))
}
if(!is.null(bottom)){
gt <- gtable_add_rows(gt, heights=grobHeight(bottom), -1)
gt <- gtable_add_grobs(gt, bottom, t=nrow(gt), l=1, r=ncol(gt))
}
if(!is.null(left)){
gt <- gtable_add_cols(gt, widths=grobWidth(left), 0)
gt <- gtable_add_grobs(gt, left, t=1, b=nrow(gt), l=1, r=1)
}
if(!is.null(right)){
gt <- gtable_add_cols(gt, widths=grobWidth(right), -1)
gt <- gtable_add_grobs(gt, right, t=1, b=nrow(gt), l=ncol(gt), r=ncol(gt))
}
if(draw){
grid.newpage()
grid.draw(gt)
}
invisible(gt)
}
#function to create geom_ploygon calls
fill_viol<-function(gr.df,gr,qtile,probs){
# SETUP VIOLIN QUANTILE PLOTS -----------------------------------
# This is adapted from: http://stackoverflow.com/questions/22278951/combining-violin-plot-with-box-plot
# g.df <- rbind(g.df[1,],g.df,g.df[nrow(g.df),])
# g.df$y[1] <-min(qtile)
# g.df$y[nrow(g.df)]<-max(qtile)
#fill_viol<-function(v,gr){
# quants<-mutate(v,
# x.l=x-violinwidth/2,
# x.r=x+violinwidth/2,
# cuts=cut(y,quantile(df[df$grp==gr,"val"])))
# # add 1/2 width each way to each x value
# plotquants<-data.frame(x=c(quants$x.l,rev(quants$x.r)), # left x bottom to top, then right x top to bottom
# y=c(quants$y,rev(quants$y)), # double up the y values to match
# id=c(quants$cuts,rev(quants$cuts)))# cut by quantile to create polygon id
# geom_polygon(aes(x,y,fill=as.factor(id)),data=plotquants) # return the geom_ploygon object
# }
#cuts <- cut(g.df$y, breaks = breaks, include.lowest=T, right=T)
#levels(cuts)
ifelse(is.null(qtile),{
cuts <- cut(gr.df$y, breaks = quantile(gr.df$y, probs, na.rm=T, type=3, include.lowest = T, right = T), na.rm=T)},{
cuts <- cut(gr.df$y, breaks = qtile, na.rm=T)
}
)
#qtile<-qtiles[gr,]
quants <- mutate(gr.df,
x.l=x-violinwidth/2,
x.r=x+violinwidth/2,
cuts=cuts)
plotquants <- data.frame(x=c(quants$x.l,rev(quants$x.r)),
y=c(quants$y,rev(quants$y)),
id=c(quants$cuts,rev(quants$cuts)))
#cut by quantile to create polygon id
geom <- geom_polygon(aes(x=x,y=y,fill=factor(id)),data=plotquants,alpha=1)
return(list(quants=quants,plotquants=plotquants,geom=geom))
}
vioQtile <- function(gg=NULL,qtiles=NULL,probs=seq(0,1,.25),labels=paste(probs[-1]*100),withData=FALSE){
require(ggplot2)
# SETUP VIOLIN QUANTILE PLOTS -----------------------------------
# This is adapted from: http://stackoverflow.com/questions/22278951/combining-violin-plot-with-box-plot
#
# Changed:
# - Deal with 'empty' quantile groups
# - Deal with original data
# - More input, more output
g.df <- ggplot_build(gg)$data[[1]] # use ggbuild to get the outline co-ords
# gg <- gg +
# geom_dotplot(aes(y=EffectSize),fill="grey80", color="grey90", stackdir='center', binaxis='y', dotsize=.4, alpha=.9, stackratio=1.4, binpositions="all", method="histodot", na.rm=T, binwidth=.05)
#
ifelse(is.null(qtiles),{
gg <- gg + lapply(unique(g.df$group), function(x) fill_viol(g.df[g.df$group==x, ],x,NULL,probs)$geom)},{
gg <- gg + lapply(unique(g.df$group), function(x) fill_viol(g.df[g.df$group==x, ],x,qtiles[x, ],probs)$geom)}
)
#lapply(unique(g.df$group), function(x) fill_viol(g.df[g.df$group==x,],qtiles[x,],probs)$geom) +
gg <- gg + geom_hline(aes(yintercept=0)) +
scale_fill_grey(name="Quantile\n",labels=labels,guide=guide_legend(reverse=T,label.position="right")) +
stat_summary(fun.y=median, geom="point", size=8, color="grey80",shape=21,fill="white")
# scale_fill_continuous(name="Quantile\n",labels=paste(probs[-1]*100),guide=guide_legend(reverse=T),low="grey20",high="grey80")
# scale_fill_gradient2(low = muted("sienna4",l=60),
# mid = muted("thistle4",l=60),
# high = muted("lightsteelblue4",l=60),
# midpoint =0)
if(withData){
ifelse(is.null(qtiles),{
ggData <- lapply(unique(g.df$group), function(x) fill_viol(g.df[g.df$group==x,],x,NULL,probs))},{
ggData <- lapply(unique(g.df$group), function(x) fill_viol(g.df[g.df$group==x,],x,qtiles[x,],probs))
}
)
return(list(ggGraph=gg,ggData=ggData))
} else {
return(gg)
}
}
# MANYLABS 2 --------------------------------------------------------------
get.results <- function(key,dfr,s,group=NULL){
dfr <- tbl_df(dfr)
disp('Get info to create a dataset for the current study',header='get.results')
inf <- get.info(key[s,],colnames(dfr))
disp('# Generate chain to select variables for the data frame and create a filter chain for the variables to use for analysis\n')
# Info based on KeyTable information in study.vars, cases.include, site.include, params.NA
fltr <- get.chain(inf)
cat('# Apply the df chain to select relevant subset of variables\n')
dfr <- eval(parse(text=paste("dfr",fltr$df)))
cat('# Get a list containing the data frames to be used in the analysis\n')
ifelse(is.null(group),{
sr <- get.sourceData(fltr,dfr,inf)
sr <- list(sr)},{
grp <- unique(eval(parse(text=paste0("dfr$",group))))
sr <- llply(grp, function(sID) get.sourceData(fltr,filter(dfr,.id==sID),inf))
})
cat("# Organize and Calculate variables for the analysis using function according to ML2.info[s,'stat.vars']\n")
var <- llply(sr, function(sID){
test <- try.CATCH(eval(parse(text=paste0(key[s,'stat.vars'],'(sID)',collapse=""))))
if(is.null(test$warning)){
return(test$value)
} else {
cat(s,key[s,'study.name'],' stat.test failed\n')
}
})
cat("# Run the analysis in 'stat.test': ",key[[s,'stat.test']],"\n")
stat.params <- inf$stat.params
stat.test <- llply(var, function(vID){
test <- try.CATCH(with(vID,eval(parse(text=key[s,'stat.test']))))
if(is.null(test$warning)){
return(test$value)
} else {
cat(s,key[[s,'study.name']],' stat.test failed\n')
}
})
return(list(info=inf,
var=var,
fltr=fltr,
dfr=dfr,
sr=sr,
stat.test=stat.test,
group = group))
}
get.analysis <- function(IDs=NULL,group=NULL,save=FALSE,dataf=NULL){
in.IT(c("plyr","dplyr","RCurl"))
cat('# Load Key Table\n')
# Load Key Table
ML2.key <- get.GoogleSheet(data='ML2masteRkey')$df
IDs=which(ML2.key$study.global.include==1)
group <- 'Source.Global'
# Load Source Info Table
ML2.sit <- get.GoogleSheet(url='https://docs.google.com/spreadsheets/d/1Qn_kVkVGwffBAmhAbpgrTjdxKLP1bb2chHjBMVyGl1s/export?format=csv')$df
if(is.null(IDs)){IDs <- seq_along(ML2.key[,1])}
# NOTE: CHANGE DIR TO FINAL LOCATION
if(is.null(dataf)){
cat('# Load data\n')
ML2.S1 <- readRDS("~/Dropbox/Manylabs2/TestOutput/ML2_RawData_S1.rds")
ML2.S2 <- readRDS("~/Dropbox/Manylabs2/TestOutput/ML2_RawData_S2.rds")
}
# Prepare list objects
primary <- eval(parse(text= paste0('list(',paste0(unlist(ML2.key[IDs,'study.analysis']),collapse=' = list(), '),' = list())') ))
data <- eval(parse(text= paste0('list(',paste0(unlist(ML2.key[IDs,'study.analysis']),collapse=' = list(), '),' = list())') ))
cat("# Start loop based on elements of IDs\n")
for(s in IDs){
cat("\n\n\n========",s,"===",ML2.key[[s,'study.analysis']],"========\n")
cat("# Get the correct slate according to information in ML2.key['study.slate']\n")
ifelse(ML2.key[s,'study.slate'] == 1, ML2.df <- ML2.S1,ML2.df<-ML2.S2)
cat('# Add a unique ID as $uID\n')
ML2.df$uID = seq(1,nrow(ML2.df))
ML2 <- get.results(ML2.key,ML2.df,s,group)
# Data list for calculating Effect Sizes CI based on NCP
primary[[s]] <- list(analysis.id = c(ML2.key[s,'study.id'],ML2.key[s,'study.slate'],ML2.key[s,'study.name']),
analysis.name = ML2.key[s,'study.analysis'],
analysis.group = group,
stat.varfun = ML2.key[s,'stat.vars'],
stat.type = ML2.key[s,'stat.type'],
stat.ncp = eval(parse(text=ML2.key[s,'stat.ncp'])),
stat.df = eval(parse(text=ML2.key[s,'stat.df'])),
stat.N = ML2$var$N,
stat.info = ML2$info,
analysis.extra = list(stat.test = stat.test))
# Data list for output to spreadsheet
data[[s]] <- list(stat.analysis.name = ML2.key[s,'study.analysis'],
stat.info = ML2.in,
stat.data.cleanchain= ML2.id,
stat.data.raw = ML2.sr$RawDataFilter,
stat.data.cleaned = ML2.sr[1:length(ML2.sr)-1],
stat.data.analysed = ML2.var,
stat.test.result = stat.test)
# prime <- data.frame(cbind(study.id = ML2.key[s,'study.id'],
# study.slate = ML2.key[s,'study.slate'],
# study.name = ML2.key[s,'study.name'],
# analysis.name = ML2.key[s,'study.analysis'],
# stat.varfun = ML2.key[s,'stat.vars'],
# stat.type = ML2.key[s,'stat.type'],
# stat.ncp = NA, #eval(parse(text=ML2.key[s,'stat.ncp'])),
# stat.df1 = NA, #eval(parse(text=ML2.key[s,'stat.df'])),
# stat.df2 = NA,
# stat.n1 = NA, #ML2$var$N,
# stat.n2 = NA, #ML2$var$N,
# analysis.group= NA)) #ML2$info)
primer <- list()
for(i in seq_along(ML2$var)){
primed <- prime
stat.test <- ML2$stat.test[[i]]
#cat('# Data list for calculating Effect Sizes CI based on NCP\n')
primed$stat.ncp <- eval(parse(text=ML2.key[s,'stat.ncp']))
df <- eval(parse(text=ML2.key[s,'stat.df']))
ifelse(length(df)<2,{df2 <- NA},{df2 <- df[2]})
primed$stat.df1 <- df[1]
primed$stat.df2 <- df2
N <- ML2$var[[i]]$N[[1]]
ifelse(length(N)<2,{n2 <- NA},{n2 <- N[2]})
primed$stat.n1 <- N[1]
primed$stat.n2 <- n2
primed$analysis.group <- ML2$group[i]
primer[[i]] <- primed
}
results <- ldply(primer)
cat('# Data list for output to spreadsheet\n')
data[[s]] <- list(stat.analysis.name = ML2.key[s,'study.analysis'],
stat.info = ML2$info,
stat.data.cleanchain= ML2$fltr,
stat.data.raw = sapply(ML2$sr, function(rdat) rdat$RawDataFilter),
stat.data.cleaned = sapply(ML2$sr, function(cdat) cdat[1:length(cdat)-1]),
stat.data.analysed = ML2$var,
stat.test.result = ML2$stat.test)
}
if(save){# Save to RData and xlsx
setwd("~/Dropbox/Manylabs2/TestOutput")
save.ML2(data,study=IDs)}
# x <- ML2.primary[IDs]
# y <- ML2.data[IDs]
# names(x) <- ML2.key[IDs,'study.analysis']
# names(y) <- ML2.key[IDs,'study.analysis']
return(list(ML2.primary=primer,ML2.data=data))
}
# Save ML2 data -----------------------------------------------------------
save.ML2 <- function(data,type=c('all','csv','xlsx','R')[1],toDir=getwd(),prefix='ML2_data_',studies=1:length(data)){
cat(toDir)
switch(type,
all = toSave<-c(xlsx=TRUE ,R=TRUE ,csv=TRUE ),
csv = toSave<-c(xlsx=FALSE,R=FALSE,csv=TRUE ),
xlsx = toSave<-c(xlsx=TRUE ,R=FALSE,csv=TRUE ),
R = toSave<-c(xlsx=FALSE,R=TRUE ,csv=FALSE)
)
outlist <- llply(seq_along(studies),function(s) (data[[s]]$stat.test.result))
if(toSave['xlsx']){
require(xlsx)
# Save results for each analysis to spreadsheet, sources as
for(s in studies){
wb <- createWorkbook(type="xlsx")
rdf <- melt(data=data[[s]]$stat.data.raw)
sheet <- createSheet(wb,sheetName=paste(unique(rdf$Source.Global)))
addDataFrame(as.data.frame(rdf), sheet, startRow=1, startColumn=1)
#autoSizeColumn(sheet,colIndex=1:ncol(data[[s]]$stat.data.precleaned[[tab]]))
rm(sheet)
# # Save data for each analysis to spreadsheet
# for(s in studies){
# if(length(sum(data[[s]]$stat.data.analysed$N))>0){
# wb <- createWorkbook(type="xlsx")
#
# sheet0 <- createSheet(wb,sheetName="stat.test.result")
# addDataFrame(as.data.frame(capture.output(outlist[[s]])), sheet0, startRow=1, startColumn=1)
# rm(sheet0)
#
# sheet1 <- createSheet(wb,sheetName="stat.data.analysed")
# header <- createRow(sheet1, 1)
# cols <- createCell(header, colIndex=1:(length(data[[s]]$stat.data.analysed)-1))
# rows <- createRow(sheet1, 2:(sum(data[[s]]$stat.data.analysed$N)+1))
# cells <- createCell(rows, colIndex=1:(length(data[[s]]$stat.data.analysed)-1))
# for(c in 1:(length(data[[s]]$stat.data.analysed)-1)){
# if(is.data.frame(data[[s]]$stat.data.analysed[[c]])){
# addDataFrame(data[[s]]$stat.data.analysed[[c]], sheet1, startRow=1, startColumn=1,showNA=SN)
# } else {
# #addDataFrame(as.data.frame(data[[s]]$stat.data.analysed[[c]]), sheet1, startRow=1, startColumn=1,showNA=SN)
#
# setCellValue(cols[[1,c]],names(data[[s]]$stat.data.analysed)[c],showNA=SN)
#
# if(is.factor(data[[s]]$stat.data.analysed[[c]])){
# mapply(setCellValue, cells[seq(1,length(data[[s]]$stat.data.analysed[[c]])),c],paste(data[[s]]$stat.data.analysed[[c]]),showNA=SN)
# } else {
# mapply(setCellValue, cells[seq(1,length(data[[s]]$stat.data.analysed[[c]])),c],data[[s]]$stat.data.analysed[[c]],showNA=SN)
# }
# }
# #autoSizeColumn(sheet1,colIndex=c)
# }
# rm(sheet1,header,cols,rows,cells)
#
# sheet2 <- createSheet(wb,sheetName="stat.data.raw")
# addDataFrame(data[[s]]$stat.data.raw, sheet2, startRow=1, startColumn=1,showNA=SN,characterNA="NA")
# #autoSizeColumn(sheet2,colIndex=1:ncol(data[[s]]$stat.data.raw))
# rm(sheet2)
#
# # for(tab in seq_along(data[[s]]$stat.data.cleaned)){
# # sheet <- createSheet(wb,sheetName=paste("cleaned.",names(data[[s]]$stat.data.cleaned[tab])))
# # addDataFrame(data[[s]]$stat.data.cleaned[[tab]], sheet, startRow=1, startColumn=1)
# # #autoSizeColumn(sheet,colIndex=1:ncol(data[[s]]$stat.data.precleaned[[tab]]))
# # rm(sheet)
# # }
# saveWorkbook(wb,file=paste0(toDir,'/',prefix,data[[s]]$stat.analysis.name,".xlsx"))
}
saveWorkbook(wb,file=paste0(toDir,'/',prefix,data[[s]]$stat.analysis.name,Sys.Date(),".xlsx"))
}
else {
cat('Skipped: ',s)
}
if(toSave['R']){
save(data,file=paste0(toDir,'/',prefix,"ALL.RData"))
}
}
save.ML2.results <- function(results,outlist,toDir=getwd(),prefix='ML2_results_',studies=1:nrow(results)){
SN = FALSE
require(xlsx)
# Save results for each analysed to spreadsheet
wb <- createWorkbook(type="xlsx")
sheet1 <- createSheet(wb,sheetName="ML2 Results Summary")
addDataFrame(results, sheet1, startRow=1, startColumn=1,showNA=SN)
for(s in studies){
sheet <- createSheet(wb,sheetName=paste(names(outlist)[[s]]))
addDataFrame(as.data.frame(capture.output(outlist[[s]])), sheet, startRow=1, startColumn=1)
#autoSizeColumn(sheet,colIndex=1:ncol(data[[s]]$stat.data.precleaned[[tab]]))
rm(sheet)
saveWorkbook(wb,file=paste0(toDir,'/',prefix,Sys.Date(),".xlsx"))
}
}
# varfuns - Functions used to prepare data for analysis ------------------------------
# func: Clean Source Label
clean.Source <- function(source.raw, SourceTable){
source.clean <- gsub("([[:blank:]]|[[:punct:]]|[[:cntrl:]])+","",source.raw)
for(s in seq_along(SourceTable$Source.Field.Raw)){
ID <- which(source.clean%in%SourceTable$Source.Field.Raw[[s]])
source.clean[ID] <- SourceTable$Source.Field.Correct[[s]]
}
return(as.data.frame(source.clean))
}
# func: Get additional changes, variables, etc.
get.fieldAdd <- function(data,stable){
data$Source.Global <- NA
data$Source.Primary <- NA
data$Source.Secondary <- NA
data$Country <- NA
data$Language <- NA
data$Execution <- NA
data$SubjectPool <- NA
data$Setting <- NA
data$Tablet <- NA
data$Pencil <- NA
data$StudyOrder <- NA
data$IDiffOrder <- NA
for(s in seq_along(stable$Source)){
ID <- which((stable$Source[[s]]==data$source)&(stable$Filename[[s]]==data$.id))
if(length(ID)>0){
data$Source.Global[ID]<- stable$Source.Global[[s]]
data$Source.Primary[ID] <- stable$Source.Global[[s]]
data$Source.Secondary[ID] <- stable$Source[[s]]
data$Country[ID] <- stable$Country[[s]]
data$Language[ID] <- stable$Language[[s]]
data$Execution[ID] <- stable$Execution[[s]]
data$SubjectPool[ID] <- stable$SubjectPool[[s]]
data$Setting[ID] <- stable$Setting[[s]]
data$Tablet[ID] <- stable$Tablet[[s]]
data$Pencil[ID] <- stable$Pencil[[s]]
data[ID, "StudyOrder"] <- data[ID, stable$StudyOrder[[s]]]
data[ID, "IDiffOrder"] <- data[ID, stable$IDiffOrder[[s]]]
}
}
return(as.data.frame(data))
}
scanColumns <- function(pattern, data){
# Function to scan columns of a dataframe for a regex pattern and return a variable (in rows) by case (in columns) logical matrix
# Includes some error catching, correcting and reporting
idS <- list()
cNames <- colnames(data)
for(c in seq(1,ncol(data))){
tmp <- try.CATCH(c(grepl(pattern,data[[c]],useBytes = T,ignore.case=T)))
if(is.null(tmp$warning)){
idS[[c]]<-tmp$value
} else {
cat('Error in column: ', colnames(data)[c],'->\t',paste0(tmp$warning),'\n')
}
}
idD <- ldply(idS)
cs <- rowSums(idD)
colID <- which(cs>0)
idS <- t(idD[colID, ])
return(list(idMatrix = idS,
idColnames = cNames[colID])
)
}