How to interpret the different ggplot2 densities?

Question

I am confused about the meaning of the following variants of `geom_density` in ggplot: Can someone please explain the difference between these four calls: - `geom_density(aes_string(x=myvar))` - `geom_density(aes_string(x=myvar, y=..density..))` - `geom_density(aes_string(x=myvar, y=..scaled..))` - `geom_density(aes_string(x=myvar, y=..count../sum(..count..)))` My understanding is that: - `geom_density` alone will produce a density whose area under the curve sums to 1 - `geom_density` with `..density..` basically does the same... ? - the `..count../sum(..count..)` will normalize the peak heights to be more like a normalized histogram, ensuring that all the heights sum to 1 - the `..count..` by itself without the denominator will just multiply each bin by # of items in it - the `..scaled..` parameter will make it so the maximum value of the density is 1. I find `..scaled..` very counterintuitive and have never seen it used if my interpretation of it is correct so I'd like to ignore tha…

Accepted Answer

The default aesthetic for `stat_density` is `..density..`, so a call to `geom_density` which uses `stat_density` by default, will plot `y = ..density..` by default.

You can see how the various columns are caculated by looking at the source code

`..scaled..` is defined as

```
densdf$scaled <- densdf$y / max(densdf$y, na.rm = TRUE)
```

Feel free to ignore it if you wish.

Looking at the source code for stat_bin

The results are computed as such

```
res <- within(results, {
    count[is.na(count)] <- 0
    density <- count / width / sum(abs(count), na.rm=TRUE)
    ncount <- count / max(abs(count), na.rm=TRUE)
    ndensity <- density / max(abs(density), na.rm=TRUE)
  })
```

So if you want to compare the results of `geom_histogram` (using the default `stat = 'bin'`), then you can set `y = ..density..` and it will calculate `count / sum(count)` for you (accounting for the width of the bins)

If you wanted to compare `geom_density(aes(y=..scaled..))` with `stat_bin`, then you would use `geom_histogram(aes(y = ..ndensity..))`

You could get them on the same scale by using `..count..` in both as well, however you would need to adjust the `adjust` parameter in `stat_density` to get the appropriately detailed approximation of the curve.

How to interpret the different ggplot2 densities?

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