Custom Discrete Color Scales for ggplot2

Building discrete color scales for ggplot2 with some cool features for binary categorical variables.


Garrick Aden-Buie


August 15, 2019



This is a quick post demonstrating a custom discrete color scale for ggplot2. The goal is to create a branded color scale that we can apply to a ggplot2 plot with + scale_color_branded().

I’m going to demonstrate how to customize the order of the colors used in the palette by showcasing a neat setup for setting the colors of binary variables. Whenever two discrete values are used for the color scale, the palette will automatically choose a primary color and a softer secondary (or other) color. Both will be parameterized so that we can change the colors as needed, choosing automatically from a branded color palette.

A color palette for our “brand”

Suppose we have color palette for our brand or organization. I just grabbed a random color palette from (but I think it actually looks okay!). We’ll store this in a list called branded_colors, that you would ideally export from the package containing your brand’s ggplot2 themes.

branded_colors <- list(
  "blue"   = "#00798c",
  "red"    = "#d1495b",
  "yellow" = "#edae49",
  "green"  = "#66a182",
  "navy"   = "#2e4057",
  "grey"   = "#8d96a3"

Create a palette function

Next we create the palette function. Ultimately, what we need from a palette function is a function that takes a single argument n and returns n colors.

But in our case, we want to give the user some control over which colors are used, so our palette function needs to have some additional parameters. To balance both needs, we’ll use a closure, which is fancy word for a function that returns another function. The outer function sets up the color choices and the inner function returns a simple function of n that gives ggplot2 our brand’s colors.

What’s great is that we can do whatever we want inside these functions. You can use this opportunity to re-order the colors for specific values of n, for example.

We’re going to make specific decisions about the colors used when n == 2. We want the palette to return two colors, branded_colors[primary] and another color. The other argument specifies the name of the second (or other) color, and we’ll first try to lookup the color name from the brand colors, but we’ll also let the user specify a manual color.

branded_pal <- function(
  primary = "blue",
  other = "grey",
  direction = 1
) {
  stopifnot(primary %in% names(branded_colors))

  function(n) {
    if (n > 6) warning("Branded Color Palette only has 6 colors.")

    if (n == 2) {
      other <- if (!other %in% names(branded_colors)) {
      } else {
      color_list <- c(other, branded_colors[primary])
    } else {
      color_list <- branded_colors[1:n]

    color_list <- unname(unlist(color_list))
    if (direction >= 0) color_list else rev(color_list)

Here are a few examples of the colors given by the pallete function returned by branded_pal() for various values of n.

[1] "#8d96a3" "#00798c"
branded_pal(other = "green")(2)
[1] "#66a182" "#00798c"
[1] "#00798c" "#d1495b" "#edae49"
branded_pal(direction = -1)(3)
[1] "#edae49" "#d1495b" "#00798c"

Create a discrete color scale

Now, we wrap this palette into a scale_colour_branded() function, using ggplot2::discrete_scale() to turn our palette into a ggplot2 scale. Note that ggplot2 convention is to spell colour with a u and to create an alias to the American spelling.

scale_colour_branded <- function(
  primary = "blue",
  other = "grey",
  direction = 1,
) {
    "colour", "branded",
    branded_pal(primary, other, direction),

scale_color_branded <- scale_colour_branded


Let’s see our new discrete color scale in action. We’ll create a simple plot using mtcars using the binary variable vs (engine shape, V or straight) for the point colors.

Here’s our plot using the standard ggplot2 colors.


# Convert vs, gear to character so ggplot2 uses discrete scales
mtcars$vs   <- paste(mtcars$vs)
mtcars$gear <- paste(mtcars$gear)

g <- ggplot(mtcars) +
  aes(mpg, wt, color = vs) +
  geom_point(size = 3) +


Using our discrete color scale automatically uses our brand’s primary color with the brand’s grey color we chose as the default other value.

g + scale_color_branded()

The default value is designed to highlight TRUE values and soften FALSE values, but you may want complementary colors instead.

g + scale_color_branded(other = "yellow")

When the level ordering doesn’t match the colors, we let the user reverse the direction of the palette by setting direction = -1.

g + scale_color_branded(other = "yellow", direction = -1)

If you’d rather use a custom color for the other color, we’ve given the user a small amount of leeway to deviate from the brand colors.

g + scale_color_branded(other = "#a2d729")

But when there are more than 2 categorical levels, the brand color palette is used…

g + aes(color = gear) + scale_color_branded()

…and primary and other options are ignored…

g + aes(color = gear) + scale_color_branded(other = "yellow")

…but the direction argument is still helpful.

g + aes(color = gear) + scale_color_branded(direction = -1)

Final thoughts

I used this technique to create a branded ggplot2 theme at work and I’ve found that this special treatment of binary categorical variables is incredibly helpful.

I frequently need to communicate two values or trends in a way that allows for comparisons but clearly highlights one category. A common example would be comparing local cancer rates with statewide averages, and in these cases having a muted color for the comparison is incredibly helpful.

Here’s an example using the Texas housing sales data that ships with ggplot2 to showcase monthly total home sales in Austin with Dallas sale volume shown for reference.

Indicate discrete/continuous in the function name

If you’re using this as guidance for building your own discrete color scale, my final tip would be to modify the name of the scale function and append _d to the end to indicate that the scale is discrete.


You’ll want to leave space in your package for a continuous color scale that will receive a _c() suffix.

Create fill scale functions, too

Finally, you’ll also want to create scale_fill_...() functions as well. You can create those using the same branded_palette() functions you used for scale_color_branded(), with the small tweak of replacing "color" with "fill" as the first argument to ggplot2::discrete_scale()

What about three colors?

Emily Riederer has a neat function in her Rtistic package template that I highly recommend you check out if you’re making a package of branded ggplot2 and rmarkdown themes.

She calls it scale_color_opinionated() and it works similarly to the functions I’ve laid out, except that it provides a color scale specifically for categorical variables with three levels: "good", "neutral", and "bad".

If this sounds like something you do frequently, consider adding an opinionated function to your package!


Thanks for reading! Find me on Twitter @grrrck to let me know if this post was helpful.

And thanks to Benjamin Wolfe for inspiring this post!