11 Colour scales and legends – ggplot2: Elegant Graphics for Data Analysis (3e)

11 Colour scales and legends

You are reading the work-in-progress third edition of the ggplot2 book. This chapter should be readable but is currently undergoing final polishing.

After position, the most commonly used aesthetics are those based on colour, and there are many ways to map values to colours in ggplot2. Because colour is complex, the chapter starts with a discussion of colour theory (Section 11.1) with special reference to colour blindness (Section 11.1.1). Mirroring the structure in the previous chapters, the next three sections are dedicated to continuous colour scales (Section 11.2), discrete colour scales (Section 11.3), and binned colour scales (Section 11.4). The chapter concludes by discussing date/time colour scales (Section 11.5), transparency scales (Section 11.6), and the mechanics of legend positioning (Section 11.7).

11.1 A little colour theory

Before we look at the details, it’s useful to learn a little bit of colour theory. Colour theory is complex because the underlying biology of the eye and brain is complex, and this introduction will only touch on some of the more important issues. An excellent and more detailed exposition is available online at http://tinyurl.com/clrdtls.

At the physical level, colour is produced by a mixture of wavelengths of light. To characterise a colour completely, we need to know the complete mixture of wavelengths. Fortunately for us the human eye only has three different colour receptors, and so we can summarise the perception of any colour with just three numbers. You may be familiar with the RGB encoding of colour space, which defines a colour by the intensities of red, green and blue light needed to produce it. One problem with this space is that it is not perceptually uniform: the two colours that are one unit apart may look similar or very different depending on where they are in the colour space. This makes it difficult to create a mapping from a continuous variable to a set of colours. There have been many attempts to come up with colours spaces that are more perceptually uniform. We’ll use a modern attempt called the HCL colour space, which has three components of h ue, c hroma and l uminance:

Hue ranges from 0 to 360 (an angle) and gives the “colour” of the colour (blue, red, orange, etc).

Chroma is the “purity” of a colour, ranging from 0 (grey) to a maximum that varies with luminance.

Luminance is the lightness of the colour, ranging from 0 (black) to 1 (white).

The three dimensions have different properties. Hues are arranged around a colour wheel and are not perceived as ordered: e.g. green does not seem “larger” than red, and blue does not seem to be “in between” green or red. In contrast, both chroma and luminance are perceived as ordered: pink is perceived as lying between red and white, and grey is seen to fall between black and white.

The combination of these three components does not produce a simple geometric shape. The figure below attempts to show the 3d shape of the space. Each slice is a constant luminance (brightness) with hue mapped to angle and chroma to radius. You can see the centre of each slice is grey and the colours get more intense as they get closer to the edge.

The shape of the HCL colour space. Hue is mapped to angle, chroma to radius and each slice shows a different luminance. The HCL space is a pretty odd shape, but you can see that colours near the centre of each slice are grey, and as you move towards the edges they become more intense. Slices for luminance 0 and 100 are omitted because they would, respectively, be a single black point and a single white point.

11.1.1 Colour blindness

An additional complication is that a sizeable minority of people do not possess the usual complement of colour receptors and so can distinguish fewer colours than others. Because of this, it is important to consider how a colour palette will look to people with common forms of colour blindness. A simple heuristic is to avoid red-green contrasts, and to check your plots with systems that simulate colour blindness. In addition to the many online tools that can assist with this (e.g., https://www.vischeck.com/), there are several R packages provide tools you may find helpful. The dichromat package (Lumley 2007) provides tools for simulating colour blindness, and a set of colour schemes known to work well for colour-blind people. Another useful tool is the colorBlindness package (Ou 2021) which provides a displayAllColors() function that helps you approximate the appearance of a given set of colours under different forms of colour blindness. As an illustration, it quickly reveals that colours provided by the rainbow() palette are not appropriate if you are trying to create plots that are readable by colour blind people, nor do they reproduce well in greyscale:

colorBlindness::displayAllColors(rainbow(6))

By way of contrast, colours...