Palettes in R

October 25, 2012
By

(This article was first published on Mollie's Research Blog, and kindly contributed to R-bloggers)

In its simplest form, a palette in R is simply a vector of colors. This vector can be include the hex triplet or R color names.

The default palette can be seen through palette():

> palette("default") # you'll only need this line if you've previously changed the palette from the default
> palette()
[1] "black"   "red"     "green3"  "blue"    "cyan"    "magenta" "yellow"
[8] "gray"

Defining your own palettes

If you want to make your own palette, you can just create your own vector of colors. It’s fine for your vector to include a mixture of hex triplets and R color names. You can use the palette function above, but generally it’s best to just store each palette as a standard vector. For one thing, you can use more than one palette that way. Here’s how you can define your own palette:

colors <- c("#A7A7A7",
"dodgerblue",
"firebrick",
"forestgreen",
"gold")

Now let’s try using our palette. For now let’s just color each bar of a histogram. This is a silly example, but I think it’s the easiest way to show how to get R to utilize your palette. In the following example, there are six bars, but only five colors. You can see that R will cycle through your palette to fill all the shapes.

hist(discoveries,
col = colors)

A more sensible use of color is to use a different color for each of a number of summary statistics:

colors <- c("#A7A7A7",
"dodgerblue",
"firebrick",
"forestgreen",
"gold")
hist(discoveries,
col = colors[1])
abline(v = mean(discoveries),
col = colors[2],
lwd = 5)
abline(v = median(discoveries),
col = colors[3],
lwd = 5)
abline(v = min(discoveries),
col = colors[4],
lwd = 5)
abline(v = max(discoveries),
col = colors[5],
lwd = 5)
legend(x = "topright", # location of legend within plot area
 col = colors[2:5],

c("Mean", "Median", "Minimum", "Maximum"), lwd = 5)

Predefined palettes: default R palettes

The package grDevices (you probably already have this loaded) contains a number of palettes.

?rainbow
rainbowcols <- rainbow(6)
hist(discoveries,
col = rainbowcols)

For rainbow, you can adjust the saturation and value. For example:

rainbowcols <- rainbow(6,
s = 0.5)
hist(discoveries,
col = rainbowcols)

heatcols <- heat.colors(6)
hist(discoveries,
col = heatcols)

As well as rainbow and heat.colors, there are also terrain.colors, topo.colors, and cm.colors.

Predefined palettes: RColorBrewer

library(RColorBrewer)
display.brewer.all()

The above lines will show us all the RColorBrewer palettes (output shown below). The top section of palettes are sequential, the middle section are qualitative, and the lower section are diverging. Here is some information about how to choose a good palette.

RColorBrewer palettes

RColorBrewer works a little different than how we’ve defined palettes previously. We’ll have to use brewer.pal to create a palette.

library(RColorBrewer)
darkcols <- brewer.pal(8, "Dark2")
hist(discoveries,
col = darkcols)

Even though we have to provide brewer.pal with the number of colors we want, we won’t necessarily need to use all those colors later. We can still choose a color from the vector like we have previously. When we’re setting a col setting to the full palette, we’ll be more concerned with how many colors are included in the palette , but even there, we can choose a subset of the whole palette:

darkcols <- brewer.pal(8, "Dark2")
hist(discoveries,
col = darkcols[1:2])

Here‘s the code from this post.

Now that we’re familiar with making our own palettes and using the built-in palettes in grDevices and RColorBrewer, I’m planning a future post about a more practical (but also more complicated) example of using palettes: making maps.

To leave a comment for the author, please follow the link and comment on their blog: Mollie's Research Blog.

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