Probabilities and proportions: which one is the chart showing

The New York Times showed this chart (link):

My first read: oh my gosh, 40-50% of the unvaccinated Americans are living their normal lives - dining at restaurants, assembling with more than 10 people, going to religious gatherings.

After reading the text around this chart, I realize I have misinterpreted it.

The chart should be read by columns. Each column is a "pie chart". For example, the first column shows that half the restaurant diners are not vaccinated, a third are fully vaccinated, and the remainder are partially vaccinated. The other columns have roughly the same proportions.

The author says "The rates of vaccination among people doing these activities largely reflect the rates in the population." This line is perhaps more confusing than intended. What she's saying is that in the general population, half of us are unvaccinated, a third are fully unvaccinated, and the remainder are partially vaccinated.

Here's a picture:

What this chart is saying is that the people dining out is like a random sample from all Americans. So too the other groups depicted. What Americans are choosing to do is independent of their vaccination status.

Unvaccinated people are no less likely to be doing all these activities than the fully vaccinated. This raises the question: are half of the people not wearing masks outdoors unvaccinated?

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Why did I read the chart wrongly in the first place? It has to do with expectations.

Most survey charts plot probabilities not proportions. I haphazardly grabbed the following Pew Research chart as an example:

From this chart, we learn that 30% of kids 9-11 years old uses TikTok compared to 11% of kids 5-8.  The percentages down a column do not sum to 100%.

 

Reading this chart won't take as long as withdrawing troops from Afghanistan

Art sent me the following Economist chart, noting how hard it is to understand. I took a look, and agreed. It's an example of a visual representation that takes more time to comprehend than the underlying data.

The chart presents responses to 3 questions on a survey. For each question, the choices are Approve, Disapprove, and "Neither" (just picking a word since I haven't seen the actual survey question). The overall approval/disapproval rates are presented, and then broken into two subgroups (Democrats and Republicans).

The first hurdle is reading the scale. Because the section from 75% to 100% has been removed, we are left with labels 0, 25, 50, 75, which do not say percentages unless we've consumed the title and subtitle. The Economist style guide places the units of data in the subtitle instead of on
the axis itself.

Our attention is drawn to the thick lines, which represent the differences between approval and disapproval rates. These differences are signed: it matters whether the proportion approving is higher or lower than the proportion disapproving. This means the data are encoded in the order of the dots plus the length of the line segment between them.

The two bottom rows of the Afghanistan question demonstrates this mental challenge. Our brains have to process the following visual cues:

1) the two lines are about the same lengths

2) the Republican dots are shifted to the right by a little

3) the colors of the dots are flipped

What do they all mean?

A chart runs in trouble when you need a paragraph to explain how to read it.

It's sometimes alright to make complicated data visualization that illustrates complicated concepts. What justifies it is the payoff. I wrote about the concept of return on effort in data visualization here.

The payoff for this chart escaped me. Take the Democratic response to troop withdrawal. About 3/4 of Democrats approve while 15% disapprove. The thick line says 60% more Democrats approve than disapprove.

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Here, I show the full axis, and add a 50% reference line

Small edits but they help visualize "half of", "three quarters of".

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Next, I switch to the more conventional stacked bars.

This format reveals some of the hidden data on the chart - the proportion answering neither approve/disapprove, and neither yes/no.

On the stacked bars visual, the proportions are counted from both ends while in the dot plot above, the proportions are measured from the left end only.

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Read all my posts about Economist charts here

 

Two commendable student projects, showing different standards of beauty

A few weeks ago, I did a guest lecture for Ray Vella's dataviz class at NYU, and discussed a particularly hairy dataset that he assigns to students.

I'm happy to see the work of the students, and there are two pieces in particular that show promise.

The following dot plot by Christina Barretto shows the disparities between the richest and poorest nations increasing between 2000 and 2015.

The underlying dataset has the average GDP per capita for the richest and the poor regions in each of nine countries, for two years (2000 and 2015). With each year, the data are indiced to the national average income (100). In the U.K., the gap increased from around 800 to 1,100 in the 15 years. It's evidence that the richer regions are getting richer, and the poorer regions are getting poorer.

(For those into interpreting data, you should notice that I didn't say the rich getting richer. During the lecture, I explain how to interpret regional averages.)

Christina's chart reflects the tidy, minimalist style advocated by Tufte. The countries are sorted by the 2000-to-2015 difference, with Britain showing up as an extreme outlier.

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The next chart by Adrienne Umali is more infographic than Tufte.

It's great story-telling. The top graphic explains the underlying data. It shows the four numbers and how the gap between the richest and poorest regions is computed. Then, it summarizes these four numbers into a single metric, "gap increase". She chooses to measure the change as a ratio while Christina's chart uses the difference, encoded as a vertical line.

Adrienne's chart is successful because she filters our attention to a single country - the U.S. It's much too hard to drink data from nine countries in one gulp.

This then sets her up for the second graphic. Now, she presents the other eight countries. Because of the work she did in the first graphic, the reader understands what those red and green arrows mean, without having to know the underlying index values.

Two small suggestions: a) order the countries from greatest to smallest change; b) leave off the decimals. These are minor flaws in a brilliant piece of work.

 

 

Metaphors, maps, and communicating data

There are some data visualization that are obviously bad. But what makes them bad?

Here is an example of such an effort:

This visualization of carbon emissions is not successful. There is precious little that a reader can learn from this chart without expensing a lot of effort. It's relatively easy to identify the largest emitters of carbon but since the data are not expressed per-capita, the chart mainly informs us which countries have the largest populations. 

The color of the bubbles informs readers which countries belong to which parts of the world. However, it distorts the location of countries within regions, and regions relative to regions, as the primary constraint is fitting the bubbles inside the shape of a foot.

The visualization gives a very rough estimate of the relative sizes of total emissions. The circles not being perfect circles don't help. 

It's relatively easy to list the top emitters in each region but it's hard to list the top 10 emitters in the world (try!) 

The small emitters stole all of the attention as they account for most of the labels - and they engender a huge web of guiding lines - an unsightly nuisance.

The diagram clings dearly to the "carbon footprint" metaphor. Does this metaphor help readers consume the emissions data? Conversely, does it slow them down?

A more conventional design uses a cartogram, a type of map in which the positioning of countries are roughly preserved while the geographical areas are coded to the data. Here's how it looks:

I can't seem to source this effort. If any reader can find the original source, please comment below.

This cartogram is a rearrangement of the footprint illustration. The map construct eliminates the need to include a color legend which just tells people which country is in which continent. The details of smaller countries are pushed to the bottom. 

In the footprint visualization, I'd even consider getting rid of the legend completely. This means trusting that readers know South Africa is part of Africa, and China is part of Asia.

Imagine: what if this chart comes without a color legend? Do we really need it?

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I'd like to try a word cloud visual for this dataset. Something that looks like this (obviously with the right data encoding):

(This map is by Michael Tompsett who sells it here.)