How to plot two histograms together in R

Question

I am using R and I have two data frames  carrots and cucumbers  Each data frame has a single numeric column which lists the length of all measured carrots  total  100k carrots  and cucumbers  total  50k cucumbers    I wish to plot two histogram - 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 I don t understand how to create it from my two tables

User · Answer

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).

User · Answer

Here s a function I wrote that uses pseudo-transparency to represent overlapping histograms  plotOverlappingHist  lt - 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   gt  0  amp  bhist counts i   gt  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

User · Answer

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  quot devtools quot   devtools  install github  quot JosephCrispell basicPlotteR quot   library basicPlotteR     Set the seed set seed 254534     Create random samples from a normal distribution distributions  lt - 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  quot Samples from normal distribution quot   xlab  quot Value quot     The plotMultipleHistograms   function can take any number of distributions  and all the general plotting parameters should work with it  for example  las  main  etc

User · Answer

Here is an even simpler solution using base graphics and alpha-blending  which does not work on all graphics devices    set seed 42  p1  lt - hist rnorm 500 4                         centered at 4 p2  lt - 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

User · Answer

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  lt - hist rnorm 500 4  plot FALSE  p2  lt - 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  -

User · Answer

Plotly s R API might be useful for you  The graph below is here   library plotly   add username and key p  lt - 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  lt - 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

User · Answer

Here s the version like the ggplot2 one I gave only in base R   I copied some from  nullglob   generate the data  carrots  lt - rnorm 100000 5 2  cukes  lt - 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  lt - density carrots  densCuke  lt - density cukes     calculate the range of the graph xlim  lt - range densCuke x densCarrot x  ylim  lt - range 0 densCuke y  densCarrot y   pick the colours carrotCol  lt - rgb 1 0 0 0 2  cukeCol  lt - 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

User · Answer

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  lt - function     col  skyblue  border T  hist     col col border border     I feel result from hist0 is prettier to look than hist  hist2  lt - function var1  var2 name1    name2                   breaks   min max length var1   length var2   20                  main0       alpha0   0 5 grey 0 border F             library scales    colh  lt - c rgb 0  1  0  alpha0   rgb 1  0  0  alpha0     if grey  colh  lt - c alpha grey 0 1 alpha0    alpha grey 0 9 alpha0       max0   max var1  var2    min0   min var1  var2     den1 max  lt - hist var1  breaks   breaks  plot   F  density   gt   max   den2 max  lt - hist var2  breaks   breaks  plot   F  density   gt   max   den max  lt - max den2 max  den1 max  1 2   var1   gt   hist0 xlim   c min0   max0    breaks   breaks                   freq   F  col   colh 1   ylim   c 0  den max   main   main0 border border        var2   gt   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    gt   deparse name1       ifelse nchar name2   0 substitute var2    gt   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    gt   deparse name1       ifelse nchar name2   0 substitute var2    gt   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

User · Answer

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  lt - data frame length   rnorm 100000  6  2   cukes  lt - 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  lt -  carrot  cukes veg  lt -  cuke     and combine into your new data frame vegLengths vegLengths  lt - 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      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

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