covid-19

135 posts
Pies, bars and self-sufficiency

Andy Cotgreave asked Twitter followers to pick between pie charts and bar charts: The underlying data are proportions of people who say they won't get the coronavirus vaccine. I noticed two somewhat unusual features: the use of pies to show single proportions, and the aspect ratio of the bars (taller than typical). Which version is easier to understand? To answer this question, I like to apply a self-sufficiency test. This...

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Vaccine researchers discard the start-at-zero rule

I struggled to decide on which blog to put this post. The reality is it bridges the graphical and analytical sides of me. But I ultimately placed it on the dataviz blog because that's where today's story starts. Data visualization has few set-in-stone rules. If pressed for one, I'd likely cite the "start-at-zero" rule, which has featured regularly on Junk Charts (here, here, and here, for example). This rule only...

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Same data + same chart form  = same story. Maybe.

We love charts that tell stories. Some people believe that if they situate the data in the right chart form, the stories reveal themselves. Some people believe for a given dataset, there exists a best chart form that brings out the story. An implication of these beliefs is that the story is immutable, given the dataset and the chart form. If you use the Trifecta Checkup, you already know I...

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Making graphics last over time

Yesterday, I analyzed the data visualization by the White House showing the progress of U.S. Covid-19 vaccinations. Here is the chart. John who tweeted this at me, saying "please get a better data viz". I'm happy to work with them or the CDC on better dataviz. Here's an example of what I do. Obviously, I'm using made-up data here and this is a sketch. I want to design a chart...

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Circular areas offer misleading cues of their underlying data

John M. pointed me on Twitter to this chart about the progress of U.S.'s vaccination campaign: This looks like a White House production, retweeted by WHO. John is unhappy about this nested bubble format, which I'll come back to later. Let's zoom in on what matters: An even bigger problem with this chart is the Q corner in our Trifecta Checkup. What is the question they are trying to address?...

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Illustrating differential growth rates

Reader Mirko was concerned about a video published in Germany that shows why the new coronavirus variant is dangerous. He helpfully provided a summary of the transcript: The South African and the British mutations of the SARS-COV-2 virus are spreading faster than the original virus. On average, one infected person infects more people than before. Researchers believe the new variant is 50 to 70 % more transmissible. Here are two...

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A beautiful curve and its deadly misinterpretation

When the preliminary analyses of their Phase 3 trials came out , vaccine developers pleased their audience of scientists with the following data graphic: The above was lifted out of the FDA briefing document for the Pfizer / Biontech vaccine. Some commentators have honed in on the blue line for the vaccinated arm of the Pfizer trial. Since the vertical axis shows cumulative number of cases, it is noted that...

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Handling partial data on graphics

Last week, I posted on the book blog a piece about excess deaths and accelerated deaths (link). That whole piece is about how certain types of analysis have to be executed at certain moments of time.  The same analysis done at the wrong time yields the wrong conclusions. Here is a good example of what I'm talking about. This is a graph of U.S. monthly deaths from Covid-19 during the...

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These are the top posts of 2020

It's always very interesting as a writer to look back at a year's of posts and find out which ones were most popular with my readers. Here are the top posts on Junk Charts from 2020: How to read this chart about coronavirus risk This post about a New York Times scatter plot dates from February, a time when many Americans were debating whether Covid-19 was just the flu. Proportions...

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Convincing charts showing containment measures work

The disorganized nature of U.S.'s response to the coronavirus pandemic has created a sort of natural experiment that allows data journalists to explore important scientific questions, such as the impact of containment measures on cases and hospitalizations. This New York Times article represents the best of such work. The key finding of the analysis is beautifully captured by this set of scatter plots: Each dot is a state. The cases...

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