I frequently use kernel density plots to illustrate distributions. These are easy and fast to create in R like so:
set.seed(1)
draws <- rnorm(100)^2
dens
This question needs a lattice
answer. Here's a very basic one, simply adapting the method employed by Dirk and others:
#Set up the data
set.seed(1)
draws <- rnorm(100)^2
dens <- density(draws)
#Put in a simple data frame
d <- data.frame(x = dens$x, y = dens$y)
#Define a custom panel function;
# Options like color don't need to be hard coded
shadePanel <- function(x,y,shadeLims){
panel.lines(x,y)
m1 <- min(which(x >= shadeLims[1]))
m2 <- max(which(x <= shadeLims[2]))
tmp <- data.frame(x1 = x[c(m1,m1:m2,m2)], y1 = c(0,y[m1:m2],0))
panel.polygon(tmp$x1,tmp$y1,col = "blue")
}
#Plot
xyplot(y~x,data = d, panel = shadePanel, shadeLims = c(1,3))