Five Guidelines for Better Data Visualizations
This being the last newsletter of 2024, I thought I’d share the five primary guidelines I follow whenever I’m creating a data visualization, be it a static chart in a report, an interactive graph in a dashboard, or a standalone visual for social media.
Show the Data. Sounds obvious, but many of us don’t end up showing our data, or at least not showing it clearly enough, because we include too much or too little, or we distract our readers with too many other unnecessary elements. This doesn’t mean we need to show all of the data all of the time but we should try to make clear what we want our readers to focus on.
Reduce the Clutter. Too many of our data visualization tools put too much stuff on our charts: gridlines, tick marks, data labels, data markers, and more. The more non-data stuff you can reduce or remove from your graphs, the more your readers will be better able to see the data you are trying to share with them.
Integrate the Graphics and the Text. I’m a big fan of including good, useful text in my graphs. This can take the shape of active, concise titles, useful labels, and effective annotations. I just don’t agree with the whole idea that a good graph means the reader should be able to understand it at just a glance—we need text to at least describe if not explain what’s going on in the graph.
Small Multiples. If you have a lot of data in your graph, consider breaking it out into smaller, multiple chunks. We used to really worry about having too many pages in a report because printing, especially color printing, is expensive. But in a digital-first world, this isn’t so much of a concern. So, feel free to break up those dense charts into smaller multiple charts. This doesn’t mean that each smaller chart should just include one series—for example, maybe you break up that line chart of all 50 states into four graphs of regions of the country; or three graphs in which the lines are increasing, decreasing, and unchanged.
Start with Gray. This is a practical technique that I use in my creation process. Whenever I make a graph, I make everything the same weight and color (gray, obviously). Doing so, I force myself to be purposeful and strategic in my use of color, labels, and other elements.
These five guidelines are only the beginning to creating better and more effective graphs, charts, diagrams, and other visuals. They are not the only things you need to be a great data communicator but I have found that these serve as a good starting point. As you develop your own preferences and aesthetics, you may find other guidelines and practices you come back to again and again. If you’d like to learn more about these—and lots of other ways to make your data visualizations more effective—check out my book, Better Data Visualizations.
I hope you have a great holiday season and a happy new year! I’ll see you again in 2025!
Thanks and Happy Holidays,
Jon
Podcast: Unlocking Data Communication: Unleashing the Power of R with David Keyes
The PolicyViz Podcast wraps up 2024 with David Keyes, author of the new book, R for the Rest of Us: A Statistics-Free Introduction! We not only talk about how you can get started in R using David’s book, but also building data and data visualization workflows with R, RMarkdown, and Quarto. We also talk about how to create consistent visualizations through themes and functions in R to help new R users leverage its features without being intimidated by complex statistics.
I hope you enjoy this episode and have a great holiday season! See you in 2025!!
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Things I’m Reading
Books
R for the Rest of Us: A Statistics-Free Introduction by David Keyes
Thicker Than Blood: How Racial Statistics Lie by Tukufu Zuberi
Swift Sword by Doyle Glass
Just read this and can’t stop thinking about it. Glass brings history to
life with the story of the Marines fight for survival. Perfect for anyone who appreciates real-life stories of heroism.
Articles
Report on Procedures and Practices for the Equitable and Timely Provision of Data to the Public
Examining the Impact of Structural Racism on Food Insecurity by Odoms-Young and Bruce
Unconscious Bias Training That Works by Gino and Coffman (HBR)
Data physicalization in the wild by Sauvé et al.
This is the lead in a Special Issue on data physicalization in Behavior & Information Technology
TV & Movies
Awesome! So helpful. We are doing this American Inequality!