Visualizing change over time: case study via Arstechnica

ArsTechnica published the following chart in its article titled "Grim new analyses spotlight just how hard the U.S. is failing in  pandemic" (link).

There are some very good things about this chart, so let me start there.

In a Trifecta Checkup, I'd give the Q corner high marks. The question is clear: how has the U.S. performed relative to other countries? In particular, the chart gives a nuanced answer to this question. The designer realizes that there are phases in the pandemic, so the same question is asked three times: how has the U.S. performed relative to other countries since June, since May, and since the start of the pandemic?

In the D corner, this chart also deserves a high score. It selects a reasonable measure of mortality, which is deaths per population. It simplifies cognition by creating three grades of mortality rates per 100,000. Grade A is below 5 deaths, Grade B, between 5 and 25, and Grade C is above 25. 

A small deduction for not including the source of the data (the article states it's from a JAMA article). If any reader notices problems with the underlying data or calculations, please leave a comment.

***

So far so good. And yet, you might feel like I'm over-praising a chart that feels distinctly average. Not terrible, not great.

The reason for our ambivalence is the V corner. This is what I call a Type V chart. The visual design isn't doing justice to the underlying question and data analysis.

The grouped bar chart isn't effective here because the orange bars dominate our vision. It's easy to see how each country performed over the course of the pandemic but it's hard to learn how countries compare to each other in different periods.

How are the countries ordered? It would seem like the orange bars may be the sorting variable but this interpretation fails in the third group of countries.

The designer apparently made the decision to place the U.S. at the bottom (i.e. the worst of the league table). As I will show later, this is justified but the argument cannot be justified by the orange bars alone. The U.S. is worse in both the blue and purple bars but not the orange.

This points out that there is interest in the change in rates (or ranks) over time. And in the following makeover, I used the Bumps chart as the basis, as its chief use is in showing how ranking changes over time.

 

Better clarity can often be gained by subtraction:

Comments

[Peter Kelley]

I was interested to see that Australia had slipped so far in the rankings in later measures which wasn't apparent in the original chart. The second wave of infections in Melbourne would have been in full swing when the data for the third period was compiled. It would be interesting to see this chart with data for later periods included. Events like the second wave in the UK, the third wave in the US and the results of Melbourne's hard lockdown in Australia would alter the picture again.

[Kaiser]

PK: One of the strengths of the Bumps chart is extensibility. So long as there is an organizing principle for the horizontal axis (here, it's recency), we can add future periods.

[Chaithanya Sai]

Hello sir,

Being a data science novice, I am not sure how you create such a visually good looking graph with all the annotations right in place. I would like to know as to how do you plot them. And maybe writing some posts for topics of how to plot better could be really helpful for many new learners.

And Thank you for putting out your expertise for new learners like me by showing some real life plots and possible corrections to enhance the the visual for better story telling and understanding.

[Kaiser]

CS: Thanks for the question. There are typically three ways: (a) post-process your chart created from Excel or some other software - using tools like Powerpoint or Illustrator; (b) use a flexible coding platform like R that allows you to place objects and text by the pixel; (c) jam it to Excel using "tricks". May make a post out of this.