stat_density2d computes bins on entire data, not on groups

[tjebo]

This is kind of a follow up from issue #4003

When using bins argument, the bins seem to be calculated on the entire data, but not on the groups as defined per aes. I am not sure that this is what one expects when using this argument.

The below example shows this on the iris dataset, with bins = 5. - not showing 5 bins for each group. But I guess it calculates 5 bins for the absolute density values for the whole data. I am not sure if this is very helpful, because one does not get a real idea which bin is shown and also it is difficult to compare the groups.

I believe it would make more sense if bins would be calculated by group, because I guess one wants to compare probability distributions rather than absolute densities (?). The second plot shows what I believe one would rather expect when using bins. The function to calculate is (modified function from this SO answer)

library(dplyr)
library(ggplot2)

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_density2d(bins = 5) +
  geom_point()

The following is more what one would expect (at least I would do so). For the generation of man_calc_dens data frame, see below.

ggplot(mapping = aes(color = Species)) +
  geom_point(data = iris, aes(Sepal.Length, Sepal.Width, )) +
  geom_polygon(data = man_calc_dens, aes(x = x, y = y, group = interaction(Species, prob)), fill = NA) 

functions and manually calculated density

probc <- function(data, x = NULL, y = NULL, n = 50, prob = 0.95, ...) {
  
  post1 <- MASS::kde2d(data[[x]], data[[y]], n = n, ...)
  
  dx <- diff(post1$x[1:2])
  dy <- diff(post1$y[1:2])
  sz <- sort(post1$z)
  c1 <- cumsum(sz) * dx * dy
  
  levels <- sapply(prob, function(x) {
    approx(c1, sz, xout = 1 - x)$y
  })
  
  df <- as.data.frame(grDevices::contourLines(post1$x, post1$y, post1$z, levels = levels))
  df$x <- round(as.numeric(df$x), 3)
  df$y <- round(as.numeric(df$y), 3)
  df$level <- round(as.numeric(df$level), 2)
  df$prob <- rep(as.character(prob), nrow(df))
  
  
  df
}

man_calc_dens <- 
  iris %>% 
  split(., .$Species) %>%
  
  purrr::map(., .f = function(u) {dplyr::bind_rows(
  purrr::map(seq(0.2, 0.8, 0.2), function(p) 
    probc(data = u, x = "Sepal.Length", y = "Sepal.Width", prob = p))
  )}
) %>% 
  bind_rows(.id = "Species")

Session info

devtools::session_info()
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[clauswilke]

You can contour on the normalized density to obtain your desired result.

library(ggplot2)

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_density2d(bins = 5, contour_var = "ndensity") +
  geom_point()

^{Created on 2020-07-02 by the reprex package (v0.3.0)}