When it Comes to Dataviz, Color is Complicated: Part 2

This is Part 2 (in a series of 2) on why color is a complex and confusing topic. In Part 1 I looked at cases where colors might not be interpreted as expected. Here I'll cover the difficulties of picking a suitable palette.

Be Subtle

Even if you avoid color contrast illusions and palettes that are difficult for those with CVD to interpret, it's still easy to make something that looks bad. Strong, saturated, vibrant colors stand out... so long as they're used sparingly. If everything is strong, saturated and vibrant you'll get something unpleasant like the chart below.

In general, use muted colors for anything that will take up a large area (like bars in bar charts). Use stronger colors for smaller items (such as points) and to highlight. Using a color that's simply different to the norm, rather than significantly more vivid, can also be effective for highlighting something significant:

Be Consistent

If you use color to distinguish two or more categories in one chart it makes sense to repeat the color scheme when the same categories appear in another chart in the same document. If you keep swapping and switching your audience might get confused and draw the wrong conclusions from your presentations of data. That's worse than not showing the data at all.

Sometimes this advice may come in to conflict with the advice above regarding object size and color vibrancy: if one chart shows bars and another points then something has to give. The best option may be a compromise set of colors that are slightly more vivid than you'd like for the bars and slightly less vivid than ideal for the points. Another option is to use the same hues (eg "red", "blue", "yellow") but vary the lightness depending on the chart type. (It's also plausible that a dot plot might be as good as or better than a bar chart anyway.)

As discussed here, you should also try to follow common conventions where applicable. This, of course, may be at odds with my advice about traffic light colors in Part 1. I did say color was complicated!

Get Some Assistance

Unless we want to draw particular attention to one category, the colors we select for our categories should be of similar vividness. That is, one should not stand out more than the others. This is a tricky task. You can't, for example, just compare the sums of the red, green and blue values a color picker tool will give you. Human perception of color simply doesn't work that way. Creating color scales that encode numerical values is just as, if not more, difficult.

You may be coming to the conclusion that creating a good color palette for visualizations can be hard. The easy way out is not to bother. That doesn't mean you have to accept your software's defaults though.

One of my favorite resources is ColorBrewer. It offers an interactive palette selector that was designed with maps in mind. There is, however, no inherent reason not to use it for other visualizations. You can pick from a range of "sequential", "diverging" and "qualitative" palettes. The qualitative palettes are best for encoding categorical information. Sequential and diverging palettes can be used for encoding values; the latter should be used when you wish to highlight how the high and low values differ from some middle value (perhaps the mean or median or simply a 0 point when both positive and negative values are possible). There's an option to export a chosen palette as a JavaScript array that I find particularly helpful.

Printing is Problematic

ColorBrewer lets you restrict palettes to only those that are CVD friendly, those that remain distinguishable when photocopied in black and white, and/or those that work well when color printed. This latter option illustrates another issue when it comes to color: The range of colors that a typical monitor can display (its "gamut") is less than a human can see but greater than can be printed on a basic CMYK printer. What you see on your laptop screen is generally not what you get on paper.

But Wait! There's Much More

My goal here wasn't to make you scared of using color, but to point out some of the dangers in order that you may be able to avoid them. I've skimmed over most of the underlying science, partly because it's not exactly trivial and partly because it's not really my area of expertise.

Everything I have covered barely scratches the surface. I don't have space to tell you about the problems with rainbow color palettes or why brown is a bit weird or anything about opponent process theory or perceptual color models or to explain the difference between luminance, brightness, and lightness (these confuse me all the time). All these things and a lot more are covered in chapters 3 and 4 of Colin Ware's book Information Visualization (mentioned in Part 1). It does get quite technical a times but I highly recommend it for anyone who wants to know about the science of color and of information visualization.

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