272

I am using R and I have two data frames: carrots and cucumbers. Each data frame has a single numeric column that lists the length of all measured carrots (total: 100k carrots) and cucumbers (total: 50k cucumbers).

I wish to plot two histograms—carrot length and cucumbers lengths—on the same plot. They overlap, so I guess I also need some transparency. I also need to use relative frequencies not absolute numbers since the number of instances in each group is different.

Something like this would be nice, but how can I create it from my two tables?

Overlapped density

Improve this question
3
  • Btw, which software are you planning to use? For open source, I'd recommend gnuplot.info [gnuplot]. In its documentation, I believe you will find certain technique and sample scripts to do what you want. Commented Aug 22, 2010 at 14:21
  • 1
    I'm using R as the tag suggests (edited post to make this clear) Commented Aug 22, 2010 at 14:36
  • 1
    someone posted some code snippet to do it in this thread: stackoverflow.com/questions/3485456/… Commented Aug 22, 2010 at 16:11

9 Answers 9

Reset to default
326

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:

enter image description here

Improve this answer
Sign up to request clarification or add additional context in comments.

5 Comments

+1 thank you all, can this be converted to a smoother gistogram (like had.co.nz/ggplot2/graphics/55078149a733dd1a0b42a57faf847036.png)?
Why did you separate out the plot commands? You can put all of those options into the hist commands and just two it in the two lines.
@John How would you do it?
Put the options in the plot command directly into the hist command as I said. Posting the code isn't what comments are for.
@John Why separate? I can't read Dirk's mind, but I would write it like that because the code is more clearly readable that way. There is one line for the calculation (hist) and one line for the graphical representation (plot).
228

That image you linked to was for density curves, not histograms.

If you've been reading on ggplot then maybe the only thing you're missing is combining your two data frames into one long one.

So, let's start with something like what you have, two separate sets of data and combine them.

carrots <- data.frame(length = rnorm(100000, 6, 2)) cukes <- data.frame(length = rnorm(50000, 7, 2.5)) # Now, combine your two dataframes into one. # First make a new column in each that will be # a variable to identify where they came from later. carrots$veg <- 'carrot' cukes$veg <- 'cuke' # and combine into your new data frame vegLengths vegLengths <- rbind(carrots, cukes)

After that, which is unnecessary if your data is in long format already, you only need one line to make your plot.

ggplot(vegLengths, aes(length, fill = veg)) + geom_density(alpha = 0.2)

enter image description here

Now, if you really did want histograms the following will work. Note that you must change position from the default "stack" argument. You might miss that if you don't really have an idea of what your data should look like. A higher alpha looks better there. Also note that I made it density histograms. It's easy to remove the y = ..density.. to get it back to counts.

ggplot(vegLengths, aes(length, fill = veg)) + geom_histogram(alpha = 0.5, aes(y = ..density..), position = 'identity')

enter image description here

On additional thing, I commented on Dirk's question that all of the arguments could simply be in the hist command. I was asked how that could be done. What follows produces exactly Dirk's figure.

set.seed(42) hist(rnorm(500,4), col=rgb(0,0,1,1/4), xlim=c(0,10)) hist(rnorm(500,6), col=rgb(1,0,0,1/4), xlim=c(0,10), add = TRUE)
Improve this answer

2 Comments

If you'd like to stay with histograms, use ggplot(vegLengths, aes(length, fill = veg)) + geom_bar(pos="dodge"). This will make interlaced histograms, like in MATLAB.
Thx for the answer! The 'position="identity"' part is actually important as otherwise the bars are stacked which is misleading when combined with a density that by default seems to be "identity", i.e., overlayed as opposed to stacked.
47

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: alt text

Improve this answer

1 Comment

+1 for an option available on all graphics devices (e.g. postscript)
40

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...

enter image description here

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

enter image description here

Improve this answer

Comments

25

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)

enter image description here

Improve this answer

Comments

25

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).

Improve this answer

3 Comments

Very nice. It also reminded me of that one stackoverflow.com/questions/3485456/…
Upping this because this answer is the only one (besides those in ggplot) which directly accounts for if your two histograms have substantially different sample sizes.
I like this method, note that you can synchronize breaks by defining them with seq(). For example: breaks=seq(min(data$some_property), max(data$some_property), by=(max_prop - min_prop)/20)
15

@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 :-) ):

enter image description here

Improve this answer

1 Comment

upping this because it is a very simple option using base and viable on postscript devices.
6

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.

Graph

Improve this answer

Comments

2

So many great answers but since I've just written a function (plotMultipleHistograms() in 'basicPlotteR' package) function to do this, I thought I would add another answer.

The advantage of this function is that it automatically sets appropriate X and Y axis limits and defines a common set of bins that it uses across all the distributions.

Here's how to use it:

# Install the plotteR package install.packages("devtools") devtools::install_github("JosephCrispell/basicPlotteR") library(basicPlotteR) # Set the seed set.seed(254534) # Create random samples from a normal distribution distributions <- list(rnorm(500, mean=5, sd=0.5), rnorm(500, mean=8, sd=5), rnorm(500, mean=20, sd=2)) # Plot overlapping histograms plotMultipleHistograms(distributions, nBins=20, colours=c(rgb(1,0,0, 0.5), rgb(0,0,1, 0.5), rgb(0,1,0, 0.5)), las=1, main="Samples from normal distribution", xlab="Value")

enter image description here

The plotMultipleHistograms() function can take any number of distributions, and all the general plotting parameters should work with it (for example: las, main, etc.).

Improve this answer

Comments

Start asking to get answers

Find the answer to your question by asking.

Ask question

Explore related questions

See similar questions with these tags.