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Function for R and ggplot2 to create log scale density plots from dataframe, spiting on a factor. #R #ggplot2 #densityPlot #GPLv2 #CHI2014
#!/usr/bin/R
# Functions for R and ggplot2 to create log-scale density plots
# Main function: density.log(...)
# Produce a dataframe used to produce a density plot
# Input:
# data: dataframe,
# var: variable to plot on y-axis
# split: factor to split on
# n=512: See ?density
# adjust=1: See ?density
# title="": x-axis title for plot
#
# Output:
# A list with three named values
# points: a dataframe of the points to be on the density plot
# summary: mean, standard deviation, max, min, median, 25th-percentile, 75th-percentile of var broken out by split
# plot: a ggplot2 object of the log density plot
#
# Example code
if (FALSE) {#This is just to make it easier to copy and paste the example code. Do *not* change to TRUE
source("density_functions.R") #Reads in functions in this file. Make sure this file exists with the same filename
ex.data<-data.frame(factor=c("A","B"),x=sample(1:10^5,1000,replace=T),y=sample(1:1000,1000,replace=T))
density.output<-density.log(ex.data,var="y",split="factor",title="Example plot")
print(density.output$summary)
print(density.output$plot)
}# End of example code
# Author: Scott A. Hale (http://www.scotthale.net/)
# License: GPLv2
# If you use this in support of an academic publication, please cite:
#
# Hale, S. A. (2014) Global Connectivity and Multilinguals in the Twitter Network.
# In Proceedings of the 2014 ACM Annual Conference on Human Factors in Computing Systems,
# ACM (Montreal, Canada).
#
# More details, related code, and the original academic paper using this code
# is available at http://www.scotthale.net/pubs/?chi2014
#
density.dataframe<-function(data,var,split=NA,n=512,adjust=1) {
dfOutput<-data.frame()
if (!is.na(split)) {
if (is.factor(data[, split])) {
vals<-levels(data[, split])
} else {
vals<-unique(data[, split])
}
for (val in vals) {
tmp<-density(log10(data[data[,split]==val,var]),n=n,adjust=adjust,na.rm=TRUE)
d<-data.frame(x=tmp$x,y=tmp$y,split=val)
dfOutput<-rbind(dfOutput,d)
}
} else {
tmp<-density(log10(data[,var]),n=n,adjust=adjust,na.rm=TRUE)
dfOutput<-data.frame(x=tmp$x,y=tmp$y)
}
dfOutput$x10<-10^dfOutput$x
#summary(log10(dfEditCountDensity$x10))
#summary(dfEditCountDensity$x)
return(dfOutput)
}
density.summary<-function(dataf,var,split) {
require(plyr)
dfSummary <- ddply(dataf, c(split), function(df) {
return(
c(
mean=mean(df[, var]),
sd=sd(df[, var]),
max=max(df[, var]),
min=min(df[, var]),
median=median(df[, var]),
p25=summary(df[, var])[2],
p75=summary(df[, var])[5]
))})
names(dfSummary)<-c("split","mean","sd","max","min","median","p25","p75")
return(dfSummary)
}
density.plot<-function(dfDensity,dfSummary,title) {
require(ggplot2)
require(scales)
plot <- ggplot(dfDensity,aes(x=x10,y=y,color=split,linetype=split)) + geom_path()
plot <- plot + scale_x_log10(title,labels=comma) + scale_y_continuous("Density")
plot <- plot + geom_vline(data=dfSummary, aes(xintercept=mean,color=split,linetype=split),size=1)#,linetype="dashed"
plot <- plot + theme_bw() +
theme(legend.title=element_blank(),legend.direction = "horizontal",legend.position = "bottom",
legend.text=element_text(size=16),
axis.title.x=element_text(size=18),axis.text.x=element_text(size=16),
axis.title.y=element_text(size=18),axis.text.y=element_text(size=16))
return(plot)
}
density.log<-function(data,var,split=NA,n=512,adjust=1,title="") {
tmp.points <- density.dataframe(data,var,split,n,adjust)
tmp.summary <- density.summary(data,var,split)
return(list(
points=tmp.points,
summary=tmp.summary,
plot=density.plot(tmp.points,tmp.summary,title)
));
}
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