How to assign colors to categorical variables in ggplot2 that have stable mapping

Question

I ve been getting up to speed with R in the last month   Here is my question   What is a good way to assign colors to categorical variables in ggplot2 that have stable mapping  I need consistent colors across a set of graphs that have different subsets and different number of categorical variables    For example   plot1  lt - ggplot data  aes xData  yData color categoricaldData     geom line     where categoricalData has 5 levels   And then  plot2  lt - ggplot data subset  aes xData subset  yData subset                                    color categoricaldData subset     geom line     where categoricalData subset has 3 levels   However  a particular level that is in both sets will end up with a different color  which makes it harder to read the graphs together   Do I need to create a vector of colors in the data frame  Or is there another way to assigns specific colors to categories

User · Answer

The easiest solution is to convert your categorical variable to a factor prior to the subsetting. Bottomline is that you need a factor variable with exact the same levels in all your subsets.

library(ggplot2)
dataset <- data.frame(category = rep(LETTERS[1:5], 100), 
    x = rnorm(500, mean = rep(1:5, 100)), y = rnorm(500, mean = rep(1:5, 100)))
dataset$fCategory <- factor(dataset$category)
subdata <- subset(dataset, category %in% c("A", "D", "E"))

With a character variable

ggplot(dataset, aes(x = x, y = y, colour = category)) + geom_point()
ggplot(subdata, aes(x = x, y = y, colour = category)) + geom_point()

With a factor variable

ggplot(dataset, aes(x = x, y = y, colour = fCategory)) + geom_point()
ggplot(subdata, aes(x = x, y = y, colour = fCategory)) + geom_point()

User · Answer

I am in the same situation pointed out by malcook in his comment  unfortunately the answer by Thierry does not work with ggplot2 version 0 9 3 1   png  figure  d png   set seed 2014  library ggplot2  dataset  lt - data frame category   rep LETTERS 1 5   100       x   rnorm 500  mean   rep 1 5  100        y   rnorm 500  mean   rep 1 5  100    dataset fCategory  lt - factor dataset category  subdata  lt - subset dataset  category  in  c  A    D    E     ggplot dataset  aes x   x  y   y  colour   fCategory     geom point   ggplot subdata  aes x   x  y   y  colour   fCategory     geom point     Here it is the first figure     and the second figure     As we can see the colors do not stay fixed  for example E switches from magenta to blu   As suggested by malcook in his comment and by hadley in his comment the code which uses limits works properly    ggplot subdata  aes x   x  y   y  colour   fCategory                geom point          scale colour discrete drop TRUE          limits   levels dataset fCategory     gives the following figure  which is correct     This is the output from sessionInfo     R version 3 0 2  2013-09-25  Platform  x86 64-pc-linux-gnu  64-bit   locale    1  LC CTYPE en US UTF-8       LC NUMERIC C                 3  LC TIME en US UTF-8        LC COLLATE en US UTF-8       5  LC MONETARY en US UTF-8    LC MESSAGES en US UTF-8      7  LC PAPER en US UTF-8       LC NAME C                    9  LC ADDRESS C               LC TELEPHONE C              11  LC MEASUREMENT en US UTF-8 LC IDENTIFICATION C         attached base packages   1  methods   stats     graphics  grDevices utils     datasets  base       other attached packages   1  ggplot2 0 9 3 1  loaded via a namespace  and not attached     1  colorspace 1 2-4   dichromat 2 0-0    digest 0 6 4       grid 3 0 2           5  gtable 0 1 2       labeling 0 2       MASS 7 3-29        munsell 0 4 2        9  plyr 1 8           proto 0 3-10       RColorBrewer 1 0-5 reshape2 1 2 2      13  scales 0 2 3       stringr 0 6 2

User · Answer

This is an old post  but I was looking for answer to this same question    Why not try something like   scale color manual values   c  foo      999999    bar      E69F00      If you have categorical values  I don t see a reason why this should not work

User · Answer

Based on the very helpful answer by joran I was able to come up with this solution for a stable color scale for a boolean factor  TRUE  FALSE    boolColors  lt - as character c  TRUE    5aae61    FALSE    7b3294    boolScale  lt - scale colour manual name  myboolean   values boolColors   ggplot myDataFrame  aes date  duration        geom point aes colour   myboolean       boolScale   Since ColorBrewer isn t very helpful with binary color scales  the two needed colors are defined manually   Here myboolean is the name of the column in myDataFrame holding the TRUE FALSE factor  date and duration are the column names to be mapped to the x and y axis of the plot in this example

User · Answer

For simple situations like the exact example in the OP  I agree that Thierry s answer is the best  However  I think it s useful to point out another approach that becomes easier when you re trying to maintain consistent color schemes across multiple data frames that are not all obtained by subsetting a single large data frame  Managing the factors levels in multiple data frames can become tedious if they are being pulled from separate files and not all factor levels appear in each file   One way to address this is to create a custom manual colour scale as follows    Some test data dat  lt - data frame x runif 10  y runif 10           grp   rep LETTERS 1 5  each   2  stringsAsFactors   TRUE    Create a custom color scale library RColorBrewer  myColors  lt - brewer pal 5  Set1   names myColors   lt - levels dat grp  colScale  lt - scale colour manual name    grp  values   myColors    and then add the color scale onto the plot as needed    One plot with all the data p  lt - ggplot dat aes x y colour   grp     geom point   p1  lt - p   colScale   A second plot with only four of the levels p2  lt - p     droplevels subset dat 4 10       colScale   The first plot looks like this     and the second plot looks like this     This way you don t need to remember or check each data frame to see that they have the appropriate levels

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