We all love robust measures like medians and interquartile ranges, but lets face it, in many fields, boxplots almost never show up in published articles, while means and sta
The first plot was just covered in a blog post on imachordata.com. (hat tip to David Smith on blog.revolution-computing.com) You can also read the related documentation from Hadley on ggplot2.
Here's the example code:
library(ggplot2)
data(mpg)
#create a data frame with averages and standard deviations
hwy.avg<-ddply(mpg, c("class", "year"), function(df)
return(c(hwy.avg=mean(df$hwy), hwy.sd=sd(df$hwy))))
#create the barplot component
avg.plot<-qplot(class, hwy.avg, fill=factor(year), data=hwy.avg, geom="bar", position="dodge")
#first, define the width of the dodge
dodge <- position_dodge(width=0.9)
#now add the error bars to the plot
avg.plot+geom_linerange(aes(ymax=hwy.avg+hwy.sd, ymin=hwy.avg-hwy.sd), position=dodge)+theme_bw()
It ends up looking like this: