Here's one way to do this.
library(ggplot2)
library(plyr)
#Data setup
set.seed(123)
dat <- data.frame(x = rep(1:3,each = 100),
y = c(rnorm(100,-1),rnorm(100,0),rnorm(100,1)))
First we'll use ggplot::ggplot_build
to capture all the calculated variables that go into plotting the violin plot:
p <- ggplot() +
geom_violin(data = dat,aes(x = factor(x),y = y))
p_build <- ggplot2::ggplot_build(p)$data[[1]]
Next, if we take a look at the source code for geom_violin
we see that it does some specific transformations of this computed data frame before handing it off to geom_polygon
to draw the actual outlines of the violin regions.
So we'll mimic that process and simply draw the filled polygons manually:
#This comes directly from the source of geom_violin
p_build <- transform(p_build,
xminv = x - violinwidth * (x - xmin),
xmaxv = x + violinwidth * (xmax - x))
p_build <- rbind(plyr::arrange(transform(p_build, x = xminv), y),
plyr::arrange(transform(p_build, x = xmaxv), -y))
I'm omitting a small detail from the source code about duplicating the first row in order to ensure that the polygon is closed.
Now we do two final modifications:
#Add our fill variable
p_build$fill_group <- ifelse(p_build$y >= 0,'Above','Below')
#This is necessary to ensure that instead of trying to draw
# 3 polygons, we're telling ggplot to draw six polygons
p_build$group1 <- with(p_build,interaction(factor(group),factor(fill_group)))
And finally plot:
#Note the use of the group aesthetic here with our computed version,
# group1
p_fill <- ggplot() +
geom_polygon(data = p_build,
aes(x = x,y = y,group = group1,fill = fill_group))
p_fill
Note that in general, this will clobber nice handling of any categorical x axis labels. So you will often need to do the plot using a continuous x axis and then if you need categorical labels, add them manually.