Back to back historgram

library(Hmisc)
age <- rnorm(1000,50,10)
sex <- sample(c('female','male'),1000,TRUE)
out <- histbackback(split(age, sex), probability=TRUE, xlim=c(-.06,.06), main = 'Back to Back Histogram')
#! just adding color
barplot(-out$left, col="red" , horiz=TRUE, space=0, add=TRUE, axes=FALSE)
barplot(out$right, col="blue", horiz=TRUE, space=0, add=TRUE, axes=FALSE)

Density curve over a histogram

This post was updated.

x <- rnorm(1000)
hist(x, freq = FALSE, col = "grey")
curve(dnorm, col = 2, add = TRUE)

This thread (specifically the Ted Harding answer) from the r-help-list augments the usefulness of this simple tip.

This kind of plots cab be easily produced using the lessR package.

For example, from ?color.density:

library(lessR)
# generate 100 random normal data values
y <- rnorm(100)
# normal curve and general density curves superimposed over histogram
# all defaults
color.density(y)

How to Superimpose Histograms

Function inspired by the code of Martin Maechler found on the R-List at http://tolstoy.newcastle.edu.au/R/help/06/06/30059.html

superhist2pdf <- function(x, filename = "super_histograms.pdf",
dev = "pdf", title = "Superimposed Histograms", nbreaks ="Sturges") {
junk = NULL
grouping = NULL
for(i in 1:length(x)) {
junk = c(junk,x[[i]])
grouping <- c(grouping, rep(i,length(x[[i]]))) }
grouping <- factor(grouping)
n.gr <- length(table(grouping))
xr <- range(junk)
histL <- tapply(junk, grouping, hist, breaks=nbreaks, plot = FALSE)
maxC <- max(sapply(lapply(histL, "[[", "counts"), max))
if(dev == "pdf") { pdf(filename, version = "1.4") } else{}
if((TC <- transparent.cols <- .Device %in% c("pdf", "png"))) {
cols <- hcl(h = seq(30, by=360 / n.gr, length = n.gr), l = 65, alpha = 0.5) }
else {
h.den <- c(10, 15, 20)
h.ang <- c(45, 15, -30) }
if(TC) {
plot(histL[[1]], xlim = xr, ylim= c(0, maxC), col = cols[1], xlab = "x", main = title) }
else { plot(histL[[1]], xlim = xr, ylim= c(0, maxC), density = h.den[1], angle = h.ang[1], xlab = "x") }
if(!transparent.cols) {
for(j in 2:n.gr) plot(histL[[j]], add = TRUE, density = h.den[j], angle = h.ang[j]) } else {
for(j in 2:n.gr) plot(histL[[j]], add = TRUE, col = cols[j]) }
invisible()
if( dev == "pdf") {
dev.off() }
}

# How to use the function:
d1 = rnorm(1:100)
d2 = rnorm(1:100) + 4
# the input object MUST be a list!
l1 = list(d1,d2)
superhist2pdf(l1, nbreaks="Sturges")