Colors for Visualizing Data

Lisa Charlotte Rost
Creating & writing about dataviz for @datawrapper. #visber & @datavisclub organizer. Fan of numbers, systems & overviews. I believe in maps, not the territory.

Take a deep dive into Colors for Visualizing Data with this hand-curated series of 10 time-tested articles and videos from around the web. We’ll guide you through, one link per day, in the app or in your inbox.

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If you care about visualizing data, you'll eventually care about colors. They can make your visualization more attractive and engaging – or boring and dull. What do we need colors in data vis for? How to pick beautiful ones? How to keep them accessible – and how to use them to communicate your data in a truthful way? Join me for ten links (including 1.5 exercises) to find out.


Lisa Charlotte Rost is one of the foremost experts on data visualization design. She is a designer & blogger at Datawrapper, a charting tool based in Berlin. Before joining Datawrapper in 2017, she created data visualizations for newsrooms like Bloomberg, Tagesspiegel, SPIEGEL, NPR and ZEIT Online. ?

  • How to pick more beautiful colors and 9 other articles
  • Average reading time: 7 minutes
  • Topics covered: colors, design, visual design, knowledge, chart
  • From experts like Andy Kirk, Susie Lu, Gregor Aisch, and more
  • You want to learn something new and are looking for a good, compact introduction.
  • You want to sharpen your skills on something you got into recently.
  • You want to catch up on a topic that has been in the news lately.
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Glad to see you made it all the way through this Deep Dive. We hope you found it useful and can put some of the insights to good use in your own daily life.

How does it work?

Day 1
...
Day 10
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Deep Dives are carefully hand-curated series of time-tested articles and videos from around the web.

We’ll guide you through, one link per day, in the app or in your inbox.

Deep Dives come in bites that are short enough to fit in your day...

...but add up to a satisfying learning experience.

Let's start simple. In this chapter of his book ?Fundamentals of Data?, Claus Wilke explains what we use colors in data vis for. Keep in mind the difference between a qualitative (= individual hues) and a sequential or diverging color scale (=gradients); the next articles will build on that knowledge.

Lisa Charlotte Rost

After learning that we need both nice distinctive colors and nice gradients for our data visualizations, let's start with reading more about distinctive colors. Here's a long article I wrote about how to pick more beautiful ones.

Lisa Charlotte Rost

In the previous article, you learned to extend your color palette by using non-pure colors or changing the saturation or lightness. Today we'll limit ourselves again: How can (or, actually, should) you change your data visualizations so that colorblind people will be able to decipher them, too??

Lisa Charlotte Rost

Instead of reading something today, here's an exercise for you: Using the great tool Viz Palette, spend a minute or two to come up with seven colors that don't have any color conflicts in the Color Report at the bottom of the page, not even for lines or small points. Then check the Color Report for colorblind people (at the top, ?Color Population?). Spend the rest of your 10 minutes trying to adjust the colors so that they have no color conflicts for anyone. (It's likely you fail. That's ok.)

Lisa Charlotte Rost

Grey is the most important color in data visualization. In this nice and short overview of ways to use grey when communicating data, Andy Kirk explains that thinking about the color for highlighted data points is as important as thinking about the color for non-highlighted ones. (If you don't know it already, check out visualisingdata.com after reading the article! It's one of the most important blogs in the data vis scene.)

Lisa Charlotte Rost

Before moving on to learn more about gradients, here's a reminder that colors convey meaning. In this article, I explain why people use pink and blue to communicate gender data, why we shouldn't do this, and which alternatives exist.?

Lisa Charlotte Rost

Samantha Zhang from former tech company Graphiq, before getting acquired by Amazon in 2017, wrote a short, easy-to-read article about how they designed gradients for their data visualizations. Don't miss the explanation of rule 2, ?Follow natural patterns of color?.?

Lisa Charlotte Rost

When applying gradients to a choropleth or heat map, you can use different kinds of interpolations, or ?stops?. In this last article, I ponder how to use them for a good compromise between drawing attention to the facts that you want to draw attention to and using the data in an honest way.

Lisa Charlotte Rost

Thanks for joining me on this colorful journey into the world of data visualization! I hope it answered some questions and provoked new ones. To learn more, the best way is to experiment, play around, and ask for feedback, e.g. in the Data Vis Society Slack or on the Data is Beautiful Subreddit. If you're interested in more data vis principles, consider browsing around the Datawrapper blog for which I'm responsible. And don't hesitate to get in touch with me, either on Twitter (@lisacrost) or via email.?

Lisa Charlotte Rost

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