Show percent % instead of counts in charts of categorical variables

I'm plotting a categorical variable and instead of showing the counts for each category value.

I'm looking for a way to get ggplot to display the percentage of values in that category. Of course, it is possible to create another variable with the calculated percentage and plot that one, but I have to do it several dozens of times and I hope to achieve that in one command.

I was experimenting with something like

qplot(mydataf) +
  stat_bin(aes(n = nrow(mydataf), y = ..count../n)) +
  scale_y_continuous(formatter = "percent")

but I must be using it incorrectly, as I got errors.

To easily reproduce the setup, here's a simplified example:

mydata <- c ("aa", "bb", NULL, "bb", "cc", "aa", "aa", "aa", "ee", NULL, "cc");
mydataf <- factor(mydata);
qplot (mydataf); #this shows the count, I'm looking to see % displayed.

In the real case, I'll probably use ggplot instead of qplot, but the right way to use stat_bin still eludes me.

I've also tried these four approaches:

ggplot(mydataf, aes(y = (..count..)/sum(..count..))) + 
  scale_y_continuous(formatter = 'percent');

ggplot(mydataf, aes(y = (..count..)/sum(..count..))) + 
  scale_y_continuous(formatter = 'percent') + geom_bar();

ggplot(mydataf, aes(x = levels(mydataf), y = (..count..)/sum(..count..))) + 
  scale_y_continuous(formatter = 'percent');

ggplot(mydataf, aes(x = levels(mydataf), y = (..count..)/sum(..count..))) + 
  scale_y_continuous(formatter = 'percent') + geom_bar();

but all 4 give:

Error: ggplot2 doesn't know how to deal with data of class factor

The same error appears for the simple case of

ggplot (data=mydataf, aes(levels(mydataf))) +
  geom_bar()

so it's clearly something about how ggplot interacts with a single vector. I'm scratching my head, googling for that error gives a single result.

Solution 1:

Since this was answered there have been some meaningful changes to the ggplot syntax. Summing up the discussion in the comments above:

 require(ggplot2)
 require(scales)

 p <- ggplot(mydataf, aes(x = foo)) +  
        geom_bar(aes(y = (..count..)/sum(..count..))) + 
        ## version 3.0.0
        scale_y_continuous(labels=percent)

Here's a reproducible example using mtcars:

 ggplot(mtcars, aes(x = factor(hp))) +  
        geom_bar(aes(y = (..count..)/sum(..count..))) + 
        scale_y_continuous(labels = percent) ## version 3.0.0

This question is currently the #1 hit on google for 'ggplot count vs percentage histogram' so hopefully this helps distill all the information currently housed in comments on the accepted answer.

Remark: If hp is not set as a factor, ggplot returns:

Solution 2:

this modified code should work

p = ggplot(mydataf, aes(x = foo)) + 
    geom_bar(aes(y = (..count..)/sum(..count..))) + 
    scale_y_continuous(formatter = 'percent')

if your data has NAs and you dont want them to be included in the plot, pass na.omit(mydataf) as the argument to ggplot.

hope this helps.

Solution 3:

With ggplot2 version 2.1.0 it is

+ scale_y_continuous(labels = scales::percent)

Solution 4:

As of March 2017, with ggplot2 2.2.1 I think the best solution is explained in Hadley Wickham's R for data science book:

ggplot(mydataf) + stat_count(mapping = aes(x=foo, y=..prop.., group=1))

stat_count computes two variables: count is used by default, but you can choose to use prop which shows proportions.