Use meaningful color schemes in your figures

Some of the best figure design ideas come from cartographers. If you've ever read a Tufte book you've seen lots of examples. Let's talk about using color effectively. Penn State geography professor Cindy Brewer's ColorBrewer tool for selecting color schemes for figures has been conveniently packaged into an R library called RColorBrewer. You'll have to read up on the RColorBrewer documentation to see how it works, but here I just want to point out how to use color schemes in a meaningful way based on the type of data you're presenting.

In general, three useful color schemes are diverging, sequential, and qualitative. The example palettes shown here can be reproduced with the following R code:

install.packages("RColorBrewer")
library(RColorBrewer)
display.brewer.all(type="seq")
display.brewer.all(type="div")
display.brewer.all(type="qual")

A diverging color scheme is useful for de-emphasizing the mean value, or for drawing attention to departures from a critical midpoint in either direction, such as in a normal distribution. Here's an example color palette and an example of how you might use a diverging scheme:





A sequential color scheme is useful for de-emphasizing the zero, or the lower bound to the data, while highlighting the importance of increasing values. Here's a palette and an example barchart.





Finally, a qualitative color scheme provides high contrast between adjacent values, and is useful for categorical or nominal data. Here's a palette and for an example check out the picture from the previously mentioned genetic diversity in Africa paper:




See Cindy Brewer's explanation of this topic for more details and examples of each scheme.