Let ggplot2 histogram show classwise percentages on y axis

Question

library(ggplot2)
data = diamonds[, c('carat', 'color')]
data = data[data$color %in% c('D', 'E'), ]

I would like to compare the histogram of carat across color D and E, and use the classwise percentage on the y-axis. The solutions I have tried are as follows:

Solution 1:

ggplot(data=data, aes(carat, fill=color)) +  geom_bar(aes(y=..density..), position='dodge', binwidth = 0.5) + ylab("Percentage") +xlab("Carat")

This is not quite right since the y-axis shows the height of the estimated density.

Solution 2:

 ggplot(data=data, aes(carat, fill=color)) +  geom_histogram(aes(y=(..count..)/sum(..count..)), position='dodge', binwidth = 0.5) + ylab("Percentage") +xlab("Carat")

This is also not I want, because the denominator used to calculate the ratio on the y-axis are the total count of D + E.

Is there a way to display the classwise percentages with ggplot2's stacked histogram? That is, instead of showing (# of obs in bin)/count(D+E) on y axis, I would like it to show (# of obs in bin)/count(D) and (# of obs in bin)/count(E) respectively for two color classes. Thanks.

Answer 1

When I tried Rorschach's answer it wasn't working for me for reasons that weren't readily apparent but I wanted to comment to say if you were open to adding density lines to a histogram once you do that it will automatically change the y axis to percent.

For example I have a count of "doses" by a binary outcome (0,1)

this code produces the following graph:

ggplot(data, aes(x=siadoses, fill=recallbin, color=recallbin)) +
  geom_histogram(binwidth=1, alpha=.5, position='identity') 

But when I include a density plot to my ggplot code and add y=..density.. I get this plot with percent on the Y

ggplot(data, aes(x=siadoses, fill=recallbin, color=recallbin)) +
  geom_histogram(aes(y=..density..), binwidth=1, alpha=.5, position='identity') +
  geom_density(alpha=.2)

kind of a work around to your original question but thought I would share.

Answer 2

It seems that binning the data outside of ggplot2 is the way to go. But I would still be interested to see if there is a way to do it with ggplot2.

library(dplyr)
breaks = seq(0,4,0.5)

data$carat_cut = cut(data$carat, breaks = breaks)

data_cut = data %>%
  group_by(color, carat_cut) %>%
  summarise (n = n()) %>%
  mutate(freq = n / sum(n))

ggplot(data=data_cut, aes(x = carat_cut, y=freq*100, fill=color)) + geom_bar(stat="identity",position="dodge") + scale_x_discrete(labels = breaks) +  ylab("Percentage") +xlab("Carat")