`stat_bin()` should have the area (instead of height) represent the count. about ggplot2 HOT 3 OPEN

Hi thanks for the suggestions!

Yes, using areas for histograms satisfies the proportional ink principle, but below are a few reasons I don't think we should do it.

• Users have come to expect counts by default. We have parted with defaults before, but I don't think we should depart a very clear and simple metric (counts) in favour of more complicated metrics.
• Counts and the proposed metric are only the same when the width of the bars are 1. If you replace the breaks by binwidth = 0.01, you see several values reach 200 with the proposed metric, whereas the data only has 32 observations in total.
• after_stat(sum(count) * density) sums the counts over groups, which it shouldn't as density is calculated within groups. The appropriate metric would be after_stat(count / width). As this is available as a simple combination of already available computed variables, I don't think this merits a novel computed variable.

from ggplot2.

Yes, changing a default is a pain... IMO it's worth it, but I don't have a huge community to serve ;)

At the very least, I think this should be written somewhere in the docs (as this is how histograms are commonly defined*). Additionally, an example with after_stat(count / width) can be added, with or without (or both) non-equi-width bins.

I'm willing to make (the world's smallest) PR if you'd like.

If you replace the breaks by binwidth = 0.01, you see several values reach 200 with the proposed metric, whereas the data only has 32 observations in total.

I don't see this as an issue - in PDFs, densities can also exceed 1 - it's just stats being stats 🤷‍♂️

* I only came to notice this when I was teaching histograms and a student pointed out that my plot didn't match what I had just said.

from ggplot2.

it's just stats being stats

Agreed, but it was meant to illustrate how it departed from counts even for equi-bins 🤓

Adding an example is a good idea, we'd welcome a PR for this.

from ggplot2.