Error bars of groups of scores

data1.csv

plotErrorBars.r

#!/usr/bin/env rscript

library(ggplot2)
library(gridExtra)

## Summarizes data.
## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
##   data: a data frame.
##   measurevar: the name of a column that contains the variable to be summariezed
##   groupvars: a vector containing names of columns that contain grouping variables
##   na.rm: a boolean that indicates whether to ignore NA's
##   conf.interval: the percent range of the confidence interval (default is 95%)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
                      conf.interval=.95, .drop=TRUE) {
    require(plyr)

    # New version of length which can handle NA's: if na.rm==T, don't count them
    length2 <- function (x, na.rm=FALSE) {
        if (na.rm) sum(!is.na(x))
        else       length(x)
    }

    # This is does the summary; it's not easy to understand...
    datac <- ddply(data, groupvars, .drop=.drop,
                   .fun= function(xx, col, na.rm) {
                           c( N    = length2(xx[,col], na.rm=na.rm),
                              mean = mean   (xx[,col], na.rm=na.rm),
                              sd   = sd     (xx[,col], na.rm=na.rm)
                              )
                          },
                    measurevar,
                    na.rm
             )

    # Rename the "mean" column    
    datac <- rename(datac, c("mean"=measurevar))

    datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean

    # Confidence interval multiplier for standard error
    # Calculate t-statistic for confidence interval: 
    # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
    ciMult <- qt(conf.interval/2 + .5, datac$N-1)
    datac$ci <- datac$se * ciMult

    return(datac)
}
# From
# http://wiki.stdout.org/rcookbook/Graphs/Plotting%20means%20and%20error%20bars%20(ggplot2)/#Helper functions
#
#

X <-read.delim("data1.csv", sep=",", header=TRUE)
summary(X)

# check out data
#p1 <- ggplot(data=X) +
#	geom_histogram(aes(x=in_common_vals))
#p2 <- ggplot(data=X) +
#	geom_histogram(aes(x=dist_score))
#p3 <- ggplot(data=X) +
#	geom_histogram(aes(x=rating))
#
#png(filename = "./array.png", width = 640, height = 640, units = 'px')
#print (
#	plot(X)
#)
#dev.off()

#png(filename = "./graphs.png", width = 540, height = 900, units = 'px')
#print(
#	grid.arrange(p1, p2, p3, ncol=1) 
#)
#dev.off()

# this is what we want...
pdat <- summarySE(X, measurevar="rating", groupvars="dist_score")
pdat[1:9,]
pf <- ggplot(data=pdat, aes(x=dist_score, y=rating)) + 
	geom_point() +
	geom_line(color="red", size=0.5) +
	geom_smooth(aes(x=dist_score, y=rating), method = lm, color="blue", size=2) + 
	geom_errorbar(aes(ymax=rating+se, ymin=rating-se), width=4)

png(filename = "./fit-bars.png", width = 540, height = 480, units = 'px')
print(
	grid.arrange(pf, ncol=1) 
)
dev.off()