The “On…” series is a collection of short blog posts relating to data visualization, economics, presentation skills, or data communication. In each, I discuss an issue, concept, or idea that I have not fully developed, a work in progress, or just some thoughts about a topic or issue I’d like to share.

In working with a client a few weeks ago, I came across this 1997 paper about supply chain management. Notice how the solid colors in the two segments of the area chart requires the author to draw the dotted lines to make it clear where the ‘consumption’ series sits when it is blocked (or occluded) by the ‘shipments’ series.

Marshall Fisher area chart with one series hidden, or occluded, by the other.

This got me thinking about using transparent colors. Not every color we include in our visualizations necessarily needs to be solid. Using transparent colors enables us and our readers to see overlapping data points, areas, bars, and other encodings. Perhaps not surprisingly, how our eyes and brains perceive this information depends on the relative values of the different colors.

This idea of using transparent colors is a current issue is the various maps of the coronavirus outbreak around the world. In The Johns Hopkins University tracker (this screenshot taken on March 31, 2020), the solid colors of the different bubbles make it impossible to distinguish the different values.

The Johns Hopkins University coronavirus tracker map with solid bubbles

In the version from the New York Times, however, (also taken on March 31) the transparent colors make it easier to see all of the bubbles (and, along with the light gray states and white background, makes the entire visual seem lighter).

The New York Times coronavirus tracker with transparent bubbles

My recommendation here is to explore the use of transparent colors (literally called “Transparency” in Excel and the ‘alpha’ term in R) so you can keep more data but make them visual to your reader.