Personalities on the radar
Hi, it’s Michi from the support team. Today, I’m talking about radar charts and personality tests: two extremely fun things that should be enjoyed with caution.
At Datawrapper, we’re very intentional when it comes to deciding which features and chart types to add. Since we want to make it easy and quick for anyone, regardless of their experience, to make high-quality data vis, we tend to focus on making the bottom 80-90% of data vis possible for everyone1.
With the tool not being primarily designed for complex charts, it is inevitable that sometimes customers ask for a chart type that we do not (yet!) support out-of-the-box. That’s when we, the support team, put our heads together to answer one question: do we have a workaround or an alternative?
There are few things I enjoy more in my support role than going down workaround rabbit holes and trying to use the tool in ways it was not initially designed for.
Testing Datawrapper's limits
More often than not, this type of boundary-pushing takes place in scatter plots: my colleague Luc has built not one, but two connected scatter plots; similarly, Pascal visualised the life of a sun and Guillermina Bach's Cello Suite No. 1, both to stunning effect; and Elana may just have outdone Vermeer with her rendition of The Girl with the Pearl Earring. What makes the scatter plot particularly powerful for these less conventional visualisations is the control you have over the dots (color, size, shape, and exact placement), the powerful tooltips, and the ability to draw lines and areas.2
So when a customer recently wrote in asking about radar charts, I did some testing. I made a spreadsheet that translated data inputs into a custom lines-and-areas formula that would then draw radar-shaped polygons and gridlines in Datawrapper. And while I have a certain fondness for Excel “calculators”3, I find their interface can be a bit unintuitive and clunky.
So, inspired by Elana’s ChARTify tool, I created a radar chart generator that hopefully offers a more accessible user experience.
Radar chart generator
Radar chart generator and how it works
The tool lets you draw up to 3 polygons at once, each with 3-6 attributes. All you have to do is decide how many polygons and attributes you want to show, input your data (on a scale from 0-100), and select if you want your gridlines to be in steps of 20 or in 25; then click on the Create a Datawrapper chart with this data button, confirm the parameters on the next page, and you’ll be taken to the editor where a ready-made radar chart with all your data is waiting for you.
Of course, a Weekly Chart wouldn’t be complete without an actual chart! And apart from football-player ratings in video games, the most memorable examples of radar charts for me include personality test results, particularly this one from a FiveThirtyEight article in 2018. As a tribute of sorts, I decided to use the Big Five test, and who better to ask about their neuroticism levels than your own colleagues.
I present to you, the average Datawrapper employee:
I’ll close by saying that radar charts should be enjoyed and employed with care4. They come with a few pitfalls. The filled area of a radar polygon looks meaningful, but it often isn’t. People tend to interpret larger areas as ‘more,’ even when the shape is just a byproduct of axis arrangement. Peaks and troughs can feel significant, but they’re often artifacts of the order of the categories. In most scenarios, multiple bar and column charts will communicate differences in magnitude more clearly and more honestly.
Having said that, Mali Akmanalp wrote an interesting piece about when it’s reasonable to use a radar chart. I also got to learn about the Chernoff faces, which (1) immediately made me regret not trying to build those in Datawrapper for this Weekly Chart (maybe next time?), and (2) reminded me that sometimes, when the stakes are low and the audience is right, it is okay not to use the chart type that most effectively communicates a point: even data vis is allowed to be a little fun and quirky, after all.
Thanks to Veronika for her edits and patience; Jonathan for the incredible work on migrating our blog backend (and thus enabling this tool to live in the post); Ceren for a great chart title; Shaylee for some of the wording; and all colleagues who took the time to bare their inner lives to me with absolutely nothing in it for them.
