HTLTC-R: Color packages

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RColorBrewer

The RColorBrewer package, based on work by Cynthia Brewer1, provides designed palettes of colors to use.

# If not available, the package can be installed using `install.packages`.
library(RColorBrewer)

Now, let’s display the RColorBrewer palettes:

There are three main classes of palettes in RColorBrewer:

  • Sequential:
    • useful for showing numerical values from low-to-high, where one is interesting and the other is not, e.g. p-values, correlations
    • typical application: heatmap
  • Qualitative:
    • equal perceptual weight/importance
    • useful for showing categories, groups. Special case: “Paired” palette is good for non-experimental factors (e.g. cell lines, strains of mice), or binary experiment factors (e.g. Treated Vs Non-treated)
    • typical application: bar plot
  • Diverging:
    • similar to Sequential palette
    • useful for showing values ranging from extreme negative to extreme positive by going through neutral (non-extreme & non-interesting) values, e.g. z-scores, t-statistics

We can also see a single RColorBrewer palette by defining the name and size of the palette using the brewer.pal(). For example, the following code selects “Dark2” palette that contains eight colors.

display.brewer.pal(n = 8, name = 'Dark2')

colorspace

Another package to generate the sets of colors is the colorspace package2, which creates colors in a number of color spaces as well as functions to convert between color spaces. By contrast, the palettes in RColorBrewer are fixed.

The three dimensions of the HCL color model:

  • Hue: “color or shade”. \(H \in [0,360]\)

  • Chroma: “colorfulness, or intensity of color as compared to gray”. \(C \in [0,100]\)

  • Luminance: “brightness”, or amount of gray. \(L \in [0,100]\)

library(colorspace)

The rainbow_hcl() function provides a qualitative palette that is good for coding a categorical variable:

palette(rainbow_hcl(12))
par(mar = rep(0.5, 4))
barplot(rep(1, 12), col = 1:12, ann = FALSE, axes = FALSE)

# Compare base and colorspace palettes
par(mfrow = c(1, 2), mar = rep(0, 4), oma = c(0.5, 0, 0.5, 0))
barplot(rep(1, 12), col = rainbow_hcl(12), ann = FALSE, axes = FALSE)
barplot(rep(1, 12), col = rainbow(12),     ann = FALSE, axes = FALSE)

The sequential_hcl() function provides a sequential palette that is good for coding numerical information that ranges in a certain interval (low values are considered to be uninteresting and high values are interesting). Below is an example of sequential palette that only varies luminance.

palette(sequential_hcl(12, c = 0, power = 2.2))
par(mar = rep(0.5, 4))
barplot(rep(1, 12), col = 1:12, ann = FALSE, axes = FALSE)

The diverge_hcl() function provides a diverging palette that is also good for coding numerical information that ranges in a certain interval but with a neutral value. Below are examples for several diverging palettes that have different pairs of hues and decreasing luminance contrasts:

par(mar = rep(0.25, 4), mfrow = c(4, 1), oma = c(0.25, 0, 0.25, 0))
# diverging red-blue colors
palette(diverge_hcl(7))
barplot(rep(1, 7), col = 1:7, ann = FALSE, axes = FALSE)
palette(diverge_hcl(7, c = 100, l = c(50, 90), power = 1))
barplot(rep(1, 7), col = 1:7, ann = FALSE, axes = FALSE)
palette(diverge_hcl(7, h = c(130, 43), c = 100, l = c(70, 90)))
barplot(rep(1, 7), col = 1:7, ann = FALSE, axes = FALSE)
# diverging cyan-magenta colors
palette(diverge_hcl(7, h = c(180, 330), c = 59, l = c(75, 95)))
barplot(rep(1, 7), col = 1:7, ann = FALSE, axes = FALSE)

  1. http://colorbrewer2.org/↩︎

  2. Achim Zeileis, Kurt Hornik, Paul Murrell (2009). Escaping RGBland: Selecting Colors for Statistical Graphics. Computational Statistics & Data Analysis, 53(9), 3259-3270. doi:10.1016/j.csda.2008.11.033↩︎