I have used the following ggplot
command:
ggplot(survey, aes(x = age)) + stat_bin(aes(n = nrow(h3), y = ..count.. / n), binwidth = 10)
+ scale
Note that this solution will not work nicely in case ggplot will show less factors than your variable actually contains (which could happen if you had been for example subsetting):
library(ggplot2)
labeli <- function(variable, value){
names_li <- list("versicolor"="versi", "virginica"="virg")
return(names_li[value])
}
dat <- subset(iris,Species!="setosa")
ggplot(dat, aes(Petal.Length)) + stat_bin() + facet_grid(Species ~ ., labeller=labeli)
A simple solution (besides adding all unused factors in names_li, which can be tedious) is to drop the unused factors with droplevels(), either in the original dataset, or in the labbeler function, see:
labeli2 <- function(variable, value){
value <- droplevels(value)
names_li <- list("versicolor"="versi", "virginica"="virg")
return(names_li[value])
}
dat <- subset(iris,Species!="setosa")
ggplot(dat, aes(Petal.Length)) + stat_bin() + facet_grid(Species ~ ., labeller=labeli2)