Side-by-side plots with ggplot2

Question

I would like to place two plots side by side using the ggplot2 package  i e  do the equivalent of par mfrow c 1 2     For example  I would like to have the following two plots show side-by-side with the same scale   x  lt - rnorm 100  eps  lt - rnorm 100 0  2  qplot x 3 x eps  qplot x 2 x eps    Do I need to put them in the same data frame   qplot displ  hwy  data mpg  facets       year    geom smooth

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One downside of the solutions based on grid arrange is that they make it difficult to label the plots with letters  A  B  etc    as most journals require   I wrote the cowplot package to solve this  and a few other  issues  specifically the function plot grid      library cowplot   iris1  lt - ggplot iris  aes x   Species  y   Sepal Length       geom boxplot     theme bw    iris2  lt - ggplot iris  aes x   Sepal Length  fill   Species       geom density alpha   0 7    theme bw       theme legend position   c 0 8  0 8    plot grid iris1  iris2  labels    AUTO       The object that plot grid   returns is another ggplot2 object  and you can save it with ggsave   as usual   p  lt - plot grid iris1  iris2  labels    AUTO   ggsave  plot pdf   p    Alternatively  you can use the cowplot function save plot    which is a thin wrapper around ggsave   that makes it easy to get the correct dimensions for combined plots  e g    p  lt - plot grid iris1  iris2  labels    AUTO   save plot  plot pdf   p  ncol   2     The ncol   2 argument tells save plot   that there are two plots side-by-side  and save plot   makes the saved image twice as wide    For a more in-depth description of how to arrange plots in a grid see this vignette  There is also a vignette explaining how to make plots with a shared legend   One frequent point of confusion is that the cowplot package changes the default ggplot2 theme  The package behaves that way because it was originally written for internal lab uses  and we never use the default theme  If this causes problems  you can use one of the following three approaches to work around them   1  Set the theme manually for every plot  I think it s good practice to always specify a particular theme for each plot  just like I did with   theme bw   in the example above  If you specify a particular theme  the default theme doesn t matter   2  Revert the default theme back to the ggplot2 default  You can do this with one line of code   theme set theme gray      3  Call cowplot functions without attaching the package  You can also not call library cowplot  or require cowplot  and instead call cowplot functions by prepending cowplot    E g   the above example using the ggplot2 default theme would become      Commented out  we don t call this   library cowplot   iris1  lt - ggplot iris  aes x   Species  y   Sepal Length       geom boxplot    iris2  lt - ggplot iris  aes x   Sepal Length  fill   Species       geom density alpha   0 7      theme legend position   c 0 8  0 8    cowplot  plot grid iris1  iris2  labels    AUTO       Updates    As of cowplot 1 0  the default ggplot2 theme is not changed anymore  As of ggplot2 3 0 0  plots can be labeled directly  see e g  here

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The above solutions may not be efficient if you want to plot multiple ggplot plots using a loop  e g  as asked here  Creating multiple plots in ggplot with different Y-axis values using a loop   which is a desired step in analyzing the unknown  or large  data-sets  e g   when you  want to plot Counts of all variables in a data-set    The code below shows how to do that using the mentioned above  multiplot     the source of which is here  http   www cookbook-r com Graphs Multiple graphs on one page  ggplot2     plotAllCounts  lt - function  dt        plots  lt - list      for i in 1 ncol dt         strX   names dt  i      print sprintf   i  strX    s   i  strX       plots  i    lt - ggplot dt    xlab strX          geom point aes string strX  stat  count          columnsToPlot  lt - floor sqrt ncol dt      multiplot plotlist   plots  cols   columnsToPlot      Now run the function - to get Counts for all variables printed using ggplot on one page  dt   ggplot2  diamonds plotAllCounts dt    One things to note is that   using  aes get strX    which you would normally use in loops when working with ggplot   in the above code instead of aes string strX  will NOT draw the desired plots  Instead  it will plot the last plot many times  I have not figured out why - it may have to do the aes and aes string are called in ggplot   Otherwise  hope you ll find the function useful

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You can use the following multiplot function from  Winston Chang s R cookbook  multiplot plot1  plot2  cols 2      multiplot  lt - function      plotlist NULL  cols        require grid         Make a list from the     arguments and plotlist     plots  lt - c list       plotlist       numPlots   length plots         Make the panel     plotCols   cols                            Number of columns of plots     plotRows   ceiling numPlots plotCols    Number of rows needed  calculated from   of cols        Set up the page     grid newpage       pushViewport viewport layout   grid layout plotRows  plotCols        vplayout  lt - function x  y          viewport layout pos row   x  layout pos col   y         Make each plot  in the correct location     for  i in 1 numPlots            curRow   ceiling i plotCols          curCol    i-1     plotCols   1         print plots  i    vp   vplayout curRow  curCol

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Any ggplots side-by-side  or n plots on a grid   The function grid arrange   in the gridExtra package will combine multiple plots  this is how you put two side by side    require gridExtra  plot1  lt - qplot 1  plot2  lt - qplot 1  grid arrange plot1  plot2  ncol 2    This is useful when the two plots are not based on the same data  for example if you want to plot different variables without using reshape      This will plot the output as a side effect  To print the side effect to a file  specify a device driver  such as pdf  png  etc   e g   pdf  foo pdf   grid arrange plot1  plot2  dev off     or  use arrangeGrob   in combination with ggsave     ggsave  foo pdf   arrangeGrob plot1  plot2     This is the equivalent of making two distinct plots using par mfrow   c 1 2    This not only saves time arranging data  it is necessary when you want two dissimilar plots     Appendix  Using Facets  Facets are helpful for making similar plots for different groups  This is pointed out below in many answers below  but I want to highlight this approach with examples equivalent to the above plots    mydata  lt - data frame myGroup   c  a    b    myX   c 1 1    qplot data   mydata       x   myX       facets    myGroup   ggplot data   mydata         geom bar aes myX          facet wrap  myGroup      Update  the plot grid function in the cowplot is worth checking out as an alternative to grid arrange  See the answer by  claus-wilke below and this vignette for an equivalent approach  but the function allows finer controls on plot location and size  based on this vignette

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In my experience gridExtra grid arrange works perfectly  if you are trying to generate plots in a loop  Short Code Snippet  gridExtra  grid arrange plot1  plot2  ncol   2      Updating this comment to show how to use grid arrange   within a for loop to generate plots for different factors of a categorical variable  for  bin i in levels athlete clean BMI cat      plot BMI  lt - athlete clean   gt   filter BMI cat    bin i    gt   group by BMI cat Team    gt   summarize count BMI team   n      gt              mutate percentage cbmiT   round count BMI team sum count BMI team    100 2     gt              arrange -count BMI team    gt   top n 10 count BMI team    gt              ggplot aes x   reorder Team count BMI team   y   count BMI team  fill   Team                 geom bar stat    quot identity quot                 theme bw                   facet wrap  Medal                labs title   paste  quot Top 10 Participating Teams with  n quot  bin i  quot  BMI quot  sep  quot  quot    y    quot Number of Athletes quot                     x   paste  quot Teams -  quot  bin i  quot  BMI Category quot   sep  quot  quot                  geom text aes label   paste percentage cbmiT  quot   quot  sep    quot  quot                            size   3  check overlap   T   position   position stack vjust   0 7                  theme axis text x   element text angle   00  vjust   0 5   plot title   element text hjust   0 5   legend position    quot none quot                 coord flip    plot BMI Medal  lt - athlete clean   gt              filter  is na Medal   BMI cat    bin i    gt              group by BMI cat Team    gt              summarize count BMI team   n      gt              mutate percentage cbmiT   round count BMI team sum count BMI team    100 2     gt              arrange -count BMI team    gt   top n 10 count BMI team    gt              ggplot aes x   reorder Team count BMI team   y   count BMI team  fill   Team                 geom bar stat    quot identity quot                 theme bw                   facet wrap  Medal                labs title   paste  quot Top 10 Winning Teams with  n quot  bin i  quot  BMI quot  sep  quot  quot    y    quot Number of Athletes quot                     x   paste  quot Teams -  quot  bin i  quot  BMI Category quot   sep  quot  quot                  geom text aes label   paste percentage cbmiT  quot   quot  sep    quot  quot                            size   3  check overlap   T   position   position stack vjust   0 7                  theme axis text x   element text angle   00  vjust   0 5   plot title   element text hjust   0 5   legend position    quot none quot                 coord flip    gridExtra  grid arrange plot BMI  plot BMI Medal  ncol   2      One of the Sample Plots from the above for loop is included below  The above loop will produce multiple plots for all levels of BMI category  Sample Image If you wish to see a more comprehensive use of grid arrange   within for loops  check out https   rpubs com Mayank7j 2020 olympic data 2000 2016

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Yes  methinks you need to arrange your data appropriately  One way would be this   X  lt - data frame x rep x 2                   y c 3 x eps  2 x eps                   case rep c  first   second    each 100    qplot x  y  data X  facets       case    geom smooth     I am sure there are better tricks in plyr or reshape -- I am still not really up to speed  on all these powerful packages by Hadley

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Update  This answer is very old  gridExtra  grid arrange   is now the recommended approach  I leave this here in case it might be useful     Stephen Turner posted the arrange   function on Getting Genetics Done blog  see post for application instructions   vp layout  lt - function x  y  viewport layout pos row x  layout pos col y  arrange  lt - function      nrow NULL  ncol NULL  as table FALSE     dots  lt - list       n  lt - length dots   if is null nrow   amp  is null ncol     nrow   floor n 2    ncol   ceiling n nrow    if is null nrow     nrow   ceiling n ncol    if is null ncol     ncol   ceiling n nrow              NOTE see n2mfrow in grDevices for possible alternative grid newpage   pushViewport viewport layout grid layout nrow ncol       ii p  lt - 1  for ii row in seq 1  nrow     ii table row  lt - ii row   if as table   ii table row  lt - nrow - ii table row   1    for ii col in seq 1  ncol       ii table  lt - ii p    if ii p  gt  n  break    print dots  ii table    vp vp layout ii table row  ii col      ii p  lt - ii p   1

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Using the patchwork package  you can simply use   operator  library ggplot2  library patchwork   p1  lt - ggplot mtcars    geom point aes mpg  disp   p2  lt - ggplot mtcars    geom boxplot aes gear  disp  group   gear     p1   p2   Other operators include   to stack plots to place plots side by side  and    to group elements  For example you can configure a top row of 3 plots and a bottom row of one plot with  p1   p2   p3   p  For more examples  see the package documentation

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Using the reshape package you can do something like this   library ggplot2  wide  lt - data frame x   rnorm 100   eps   rnorm 100  0   2   wide first  lt - with wide  3   x   eps  wide second  lt - with wide  2   x   eps  long  lt - melt wide  id vars   c  x    eps    ggplot long  aes x   x  y   value     geom smooth     geom point     facet grid    variable

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Using tidyverse   x  lt - rnorm 100  eps  lt - rnorm 100 0  2  df  lt - data frame x  eps    gt      mutate p1   3 x eps  p2   2 x eps    gt      tidyr  gather  plot    value   3 4    gt      ggplot aes x   x   y   value          geom point          geom smooth          facet wrap  plot  ncol  2   df

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The cowplot package gives you a nice way to do this  in a manner that suits publication   x  lt - rnorm 100  eps  lt - rnorm 100 0  2  A   qplot x 3 x eps  geom   c  point    smooth    theme gray   B   qplot x 2 x eps  geom   c  point    smooth    theme gray   cowplot  plot grid A  B  labels   c  A    B    align    v

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ggplot2 is based on grid graphics  which provide a different system for arranging plots on a page  The par mfrow     command doesn t have a direct equivalent  as grid objects  called grobs  aren t necessarily drawn immediately  but can be stored and manipulated as regular R objects before being converted to a graphical output  This enables greater flexibility than the draw this now model of base graphics  but the strategy is necessarily a little different    I wrote grid arrange   to provide a simple interface as close as possible to par mfrow   In its simplest form  the code would look like   library ggplot2  x  lt - rnorm 100  eps  lt - rnorm 100 0  2  p1  lt - qplot x 3 x eps  p2  lt - qplot x 2 x eps   library gridExtra  grid arrange p1  p2  ncol   2      More options are detailed in this vignette   One common complaint is that plots aren t necessarily aligned e g  when they have axis labels of different size  but this is by design  grid arrange makes no attempt to special-case ggplot2 objects  and treats them equally to other grobs  lattice plots  for instance   It merely places grobs in a rectangular layout   For the special case of ggplot2 objects  I wrote another function  ggarrange  with a similar interface  which attempts to align plot panels  including facetted plots  and tries to respect the aspect ratios when defined by the user   library egg  ggarrange p1  p2  ncol   2    Both functions are compatible with ggsave    For a general overview of the different options  and some historical context  this vignette offers additional information

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There is also multipanelfigure package that is worth to mention  See also this answer     library ggplot2  theme set theme bw     q1  lt - ggplot mtcars    geom point aes mpg  disp   q2  lt - ggplot mtcars    geom boxplot aes gear  disp  group   gear   q3  lt - ggplot mtcars    geom smooth aes disp  qsec   q4  lt - ggplot mtcars    geom bar aes carb    library magrittr  library multipanelfigure  figure1  lt - multi panel figure columns   2  rows   2  panel label type    none     show the layout figure1     figure1   lt  gt     fill panel q1  column   1  row   1    lt  gt     fill panel q2  column   2  row   1    lt  gt     fill panel q3  column   1  row   2    lt  gt     fill panel q4  column   2  row   2  figure1       complex layout figure2  lt - multi panel figure columns   3  rows   3  panel label type    upper-roman   figure2     figure2   lt  gt     fill panel q1  column   1 2  row   1    lt  gt     fill panel q2  column   3  row   1    lt  gt     fill panel q3  column   1  row   2    lt  gt     fill panel q4  column   2 3  row   2 3  figure2     Created on 2018-07-06 by the reprex package  v0 2 0 9000

[r] Side-by-side plots with ggplot2

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