ggplot2 shade area under density curve by group

Here is one way (and, as @joran says, this is an extension of the response here):

#  same data, just renaming columns for clarity later on#  also, use data tableslibrary(data.table)set.seed(1)value <- c(rnorm(50, mean = 1), rnorm(50, mean = 3))site  <- c(rep("site1", 50), rep("site2", 50))dt    <- data.table(site,value)#  generate kdfgg <- dt[,list(x=density(value)$x, y=density(value)$y),by="site"]#  calculate quantilesq1 <- quantile(dt[site=="site1",value],0.01)q2 <- quantile(dt[site=="site2",value],0.75)# generate the plotggplot(dt) + stat_density(aes(x=value,color=site),geom="line",position="dodge")+  geom_ribbon(data=subset(gg,site=="site1" & x>q1),              aes(x=x,ymax=y),ymin=0,fill="red", alpha=0.5)+  geom_ribbon(data=subset(gg,site=="site2" & x<q2),              aes(x=x,ymax=y),ymin=0,fill="blue", alpha=0.5)

Produces this:

The problem with @jlhoward's solution is that you need to manually add goem_ribbon for each group you have. I wrote my own ggplot stat wrapper following this vignette. The benefit of this is that it automatically works with group_by and facet and you don't need to manually add geoms for each group.

StatAreaUnderDensity <- ggproto(  "StatAreaUnderDensity", Stat,  required_aes = "x",  compute_group = function(data, scales, xlim = NULL, n = 50) {    fun <- approxfun(density(data$x))    StatFunction$compute_group(data, scales, fun = fun, xlim = xlim, n = n)  })stat_aud <- function(mapping = NULL, data = NULL, geom = "area",                    position = "identity", na.rm = FALSE, show.legend = NA,                     inherit.aes = TRUE, n = 50, xlim=NULL,                      ...) {  layer(    stat = StatAreaUnderDensity, data = data, mapping = mapping, geom = geom,     position = position, show.legend = show.legend, inherit.aes = inherit.aes,    params = list(xlim = xlim, n = n, ...))}

Now you can use stat_aud function just like other ggplot geoms.

set.seed(1)x <- c(rnorm(500, mean = 1), rnorm(500, mean = 3))y <- c(rep("group 1", 500), rep("group 2", 500))t_critical = 1.5tibble(x=x, y=y)%>%ggplot(aes(x=x,color=y))+  geom_density()+  geom_vline(xintercept = t_critical)+  stat_aud(geom="area",           aes(fill=y),           xlim = c(0, t_critical),               alpha = .2)

tibble(x=x, y=y)%>%ggplot(aes(x=x))+  geom_density()+  geom_vline(xintercept = t_critical)+  stat_aud(geom="area",           fill = "orange",           xlim = c(0, t_critical),               alpha = .2)+  facet_grid(~y)

You need to use fill. color controls the outline of the density plot, which is necessary if you want non-black outlines.

ggplot(xy, aes(x, color=y, fill = y, alpha=0.4)) + geom_density()

To get something like that. Then you can remove the alpha part of the legend by using

ggplot(xy, aes(x, color = y, fill = y, alpha=0.4)) + geom_density()+ guides(alpha='none')