Tukukkk

Here is an even simpler solution using base graphics and alpha-blending (which does not work on all graphics devices):

set.seed(42)

p1 <- hist(rnorm(500,4))                     # centered at 4

p2 <- hist(rnorm(500,6))                     # centered at 6

plot( p1, col=rgb(0,0,1,1/4), xlim=c(0,10))  # first histogram

plot( p2, col=rgb(1,0,0,1/4), xlim=c(0,10), add=T)  # second

The key is that the colours are semi-transparent.

Edit, more than two years later: As this just got an upvote, I figure I may as well add a visual of what the code produces as alpha-blending is so darn useful:

Here's a function I wrote that uses pseudo-transparency to represent overlapping histograms

plotOverlappingHist <- function(a, b, colors=c("white","gray20","gray50"),

                                breaks=NULL, xlim=NULL, ylim=NULL){



  ahist=NULL

  bhist=NULL



  if(!(is.null(breaks))){

    ahist=hist(a,breaks=breaks,plot=F)

    bhist=hist(b,breaks=breaks,plot=F)

  } else {

    ahist=hist(a,plot=F)

    bhist=hist(b,plot=F)



    dist = ahist$breaks[2]-ahist$breaks[1]

    breaks = seq(min(ahist$breaks,bhist$breaks),max(ahist$breaks,bhist$breaks),dist)



    ahist=hist(a,breaks=breaks,plot=F)

    bhist=hist(b,breaks=breaks,plot=F)

  }



  if(is.null(xlim)){

    xlim = c(min(ahist$breaks,bhist$breaks),max(ahist$breaks,bhist$breaks))

  }



  if(is.null(ylim)){

    ylim = c(0,max(ahist$counts,bhist$counts))

  }



  overlap = ahist

  for(i in 1:length(overlap$counts)){

    if(ahist$counts[i] > 0 & bhist$counts[i] > 0){

      overlap$counts[i] = min(ahist$counts[i],bhist$counts[i])

    } else {

      overlap$counts[i] = 0

    }

  }



  plot(ahist, xlim=xlim, ylim=ylim, col=colors[1])

  plot(bhist, xlim=xlim, ylim=ylim, col=colors[2], add=T)

  plot(overlap, xlim=xlim, ylim=ylim, col=colors[3], add=T)

}

Here's another way to do it using R's support for transparent colors

a=rnorm(1000, 3, 1)

b=rnorm(1000, 6, 1)

hist(a, xlim=c(0,10), col="red")

hist(b, add=T, col=rgb(0, 1, 0, 0.5) )

The results end up looking something like this:

Already beautiful answers are there, but I thought of adding this. Looks good to me.
(Copied random numbers from @Dirk). library(scales) is needed`

set.seed(42)

hist(rnorm(500,4),xlim=c(0,10),col='skyblue',border=F)

hist(rnorm(500,6),add=T,col=scales::alpha('red',.5),border=F)

The result is...

Update: This overlapping function may also be useful to some.

hist0 <- function(...,col='skyblue',border=T) hist(...,col=col,border=border) 

I feel result from hist0 is prettier to look than hist

hist2 <- function(var1, var2,name1='',name2='',

              breaks = min(max(length(var1), length(var2)),20), 

              main0 = "", alpha0 = 0.5,grey=0,border=F,...) {    



library(scales)

  colh <- c(rgb(0, 1, 0, alpha0), rgb(1, 0, 0, alpha0))

  if(grey) colh <- c(alpha(grey(0.1,alpha0)), alpha(grey(0.9,alpha0)))



  max0 = max(var1, var2)

  min0 = min(var1, var2)



  den1_max <- hist(var1, breaks = breaks, plot = F)$density %>% max

  den2_max <- hist(var2, breaks = breaks, plot = F)$density %>% max

  den_max <- max(den2_max, den1_max)*1.2

  var1 %>% hist0(xlim = c(min0 , max0) , breaks = breaks,

                 freq = F, col = colh[1], ylim = c(0, den_max), main = main0,border=border,...)

  var2 %>% hist0(xlim = c(min0 , max0),  breaks = breaks,

                 freq = F, col = colh[2], ylim = c(0, den_max), add = T,border=border,...)

  legend(min0,den_max, legend = c(

    ifelse(nchar(name1)==0,substitute(var1) %>% deparse,name1),

    ifelse(nchar(name2)==0,substitute(var2) %>% deparse,name2),

    "Overlap"), fill = c('white','white', colh[1]), bty = "n", cex=1,ncol=3)



  legend(min0,den_max, legend = c(

    ifelse(nchar(name1)==0,substitute(var1) %>% deparse,name1),

    ifelse(nchar(name2)==0,substitute(var2) %>% deparse,name2),

    "Overlap"), fill = c(colh, colh[2]), bty = "n", cex=1,ncol=3) }

The result of

par(mar=c(3, 4, 3, 2) + 0.1) 

set.seed(100) 

hist2(rnorm(10000,2),rnorm(10000,3),breaks = 50)

is

Here is an example of how you can do it in "classic" R graphics:

## generate some random data

carrotLengths <- rnorm(1000,15,5)

cucumberLengths <- rnorm(200,20,7)

## calculate the histograms - don't plot yet

histCarrot <- hist(carrotLengths,plot = FALSE)

histCucumber <- hist(cucumberLengths,plot = FALSE)

## calculate the range of the graph

xlim <- range(histCucumber$breaks,histCarrot$breaks)

ylim <- range(0,histCucumber$density,

              histCarrot$density)

## plot the first graph

plot(histCarrot,xlim = xlim, ylim = ylim,

     col = rgb(1,0,0,0.4),xlab = 'Lengths',

     freq = FALSE, ## relative, not absolute frequency

     main = 'Distribution of carrots and cucumbers')

## plot the second graph on top of this

opar <- par(new = FALSE)

plot(histCucumber,xlim = xlim, ylim = ylim,

     xaxt = 'n', yaxt = 'n', ## don't add axes

     col = rgb(0,0,1,0.4), add = TRUE,

     freq = FALSE) ## relative, not absolute frequency

## add a legend in the corner

legend('topleft',c('Carrots','Cucumbers'),

       fill = rgb(1:0,0,0:1,0.4), bty = 'n',

       border = NA)

par(opar)

The only issue with this is that it looks much better if the histogram breaks are aligned, which may have to be done manually (in the arguments passed to hist).

Here's the version like the ggplot2 one I gave only in base R. I copied some from @nullglob.

generate the data

carrots <- rnorm(100000,5,2)

cukes <- rnorm(50000,7,2.5)

You don't need to put it into a data frame like with ggplot2. The drawback of this method is that you have to write out a lot more of the details of the plot. The advantage is that you have control over more details of the plot.

## calculate the density - don't plot yet

densCarrot <- density(carrots)

densCuke <- density(cukes)

## calculate the range of the graph

xlim <- range(densCuke$x,densCarrot$x)

ylim <- range(0,densCuke$y, densCarrot$y)

#pick the colours

carrotCol <- rgb(1,0,0,0.2)

cukeCol <- rgb(0,0,1,0.2)

## plot the carrots and set up most of the plot parameters

plot(densCarrot, xlim = xlim, ylim = ylim, xlab = 'Lengths',

     main = 'Distribution of carrots and cucumbers', 

     panel.first = grid())

#put our density plots in

polygon(densCarrot, density = -1, col = carrotCol)

polygon(densCuke, density = -1, col = cukeCol)

## add a legend in the corner

legend('topleft',c('Carrots','Cucumbers'),

       fill = c(carrotCol, cukeCol), bty = 'n',

       border = NA)

@Dirk Eddelbuettel: The basic idea is excellent but the code as shown can be improved. [Takes long to explain, hence a separate answer and not a comment.]

The hist() function by default draws plots, so you need to add the plot=FALSE option. Moreover, it is clearer to establish the plot area by a plot(0,0,type="n",...) call in which you can add the axis labels, plot title etc. Finally, I would like to mention that one could also use shading to distinguish between the two histograms. Here is the code:

set.seed(42)

p1 <- hist(rnorm(500,4),plot=FALSE)

p2 <- hist(rnorm(500,6),plot=FALSE)

plot(0,0,type="n",xlim=c(0,10),ylim=c(0,100),xlab="x",ylab="freq",main="Two histograms")

plot(p1,col="green",density=10,angle=135,add=TRUE)

plot(p2,col="blue",density=10,angle=45,add=TRUE)

And here is the result (a bit too wide because of RStudio :-) ):

Plotly's R API might be useful for you. The graph below is here.

library(plotly)

#add username and key

p <- plotly(username="Username", key="API_KEY")

#generate data

x0 = rnorm(500)

x1 = rnorm(500)+1

#arrange your graph

data0 = list(x=x0,

         name = "Carrots",

         type='histogramx',

         opacity = 0.8)



data1 = list(x=x1,

         name = "Cukes",

         type='histogramx',

         opacity = 0.8)

#specify type as 'overlay'

layout <- list(barmode='overlay',

               plot_bgcolor = 'rgba(249,249,251,.85)')  

#format response, and use 'browseURL' to open graph tab in your browser.

response = p$plotly(data0, data1, kwargs=list(layout=layout))



url = response$url

filename = response$filename



browseURL(response$url)

Full disclosure: I'm on the team.