Junk Charts: A Tour

Kaiser Fung’s “Junk Charts” blog is full of treasures, including ones related to the COVID-19 pandemic. Evelyn wrote a post about the blog back in 2017. Please join me on a tour of a few of the posts Fung has written since then.

Pandemic-related posts

This exercise plan for your lock-down work-out is inspired by Venn”

This post includes a Venn diagram with so many compartments that it’s a bit dizzying. The chart came from a Nature article and it’s supposed to show symptoms that users of a UK app reported after testing positive for COVID-19.

It “fails the self-sufficiency test because if you remove the data from it, you end up with a data container – like a world map showing country boundaries and no data,” Fung wrote, adding “If you’re new here: if a graphic requires the entire dataset to be printed on it for comprehension, then the visual elements of the graphic are not doing any work. The graphic cannot stand on its own.”

“The numbers on this graphic add to 1,764 whereas the study population in the preprint was 1,702,” Fung noted. He then comments on the struggle of trying to interpret the information the chart is supposed to convey:

“The chart also strains the reader. Take the number 18, right in the middle. What combination of symptoms did these 18 people experience? You have to figure out the layers sitting beneath the number. You see dark blue, light blue, orange. If you blink, you might miss the gray at the bottom. Then you have to flip your eyes up to the legend to map these colors to diarrhoea, shortness of breath, anosmia, and fatigue. Oops, I missed the yellow, which is the cough. To be sure, you look at the remaining categories to see where they stand – I’ve named all of them except fever. The number 18 lies outside fever so this compartment represents everything except fever.”

 “When the visual runs away from the data” and “Make your color legend better with one simple rule” 

Both of these posts are related to the same pie chart, which is supposed to show survey respondents’ biggest worries about COVID-19. The options were “getting it,” “family getting it” and “the economy.”

In the first post, Fung removed the data from the chart in order to look at how much information the chart actually gives by itself. He calls this exercise a “self-sufficiency test.”

“The idea of self-sufficiency is to test how much work the visual elements of the graphic are doing to convey its message,” he noted. He explains that each of the slices of the pie chart don’t accurately represent the amounts they are supposed to (for instance, “the economy” slice takes up 38% of the pie, but 68% of people responded that the economy was their biggest worry). Furthermore, the data for the three categories add up to 178%, making a pie chart a confusing way of conveying this information. Fung recommends using a bar chart instead.

In the second post, Fung uses the chart to show “why we should follow a basic rule of constructing color legends: order the categories in the way you expect readers to encounter them.” He then recommends re-ordering the legend to better fit the order in which people will likely view the categories.

Other posts

“Too many colors on a chart is bad, but why?”

I usually enjoy colorful charts and graphics, but I agree with Fung here:

“The reason why the coloring scheme backfires is that readers may look for meaning in the colors. What’s common between Iceland, United States and Germany for them to be assigned green? What about Japan, New Zealand, Spain and France, all of which shown yellow? The readers’ instinct is driven by a set of unspoken rules that govern the production of data visualization.

Specifically, the rule here is: color differences reflect data differences. When such a rule is violated, the reader is misled and confused.”

“This Excel chart looks standard but gets everything wrong”

Fung dissects a chart that seems to show global car sales by region. However, he points out that there are at least four major problems with it.

Have an idea we should cover in a future post? Reach out in the comments below or on Twitter (@writesRCrowell).

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2 Responses to Junk Charts: A Tour

  1. Paul Campbell says:

    Not very educational without links to the charts themselves. And the link to Fung gives no charts, either, just a header.

    • NFQ says:

      The bold lines in quotation marks go to the various posts at Junk Charts. I was able to see all the graphs there.

      Thanks for this write-up – Junk Charts is the sort of blog I would love but had never heard of. Subscribing now!

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