Speedometer charts: love or hate
Here's a radar chart that works, sort of

Two uses of bumps charts

Long-time reader Antonio R. submitted the following chart, which illustrates analysis from a preprint on the effect of Covid-19 on life expectancy in the U.S. (link)


Aburto_lifeexpectancyFor this post, I want to discuss the bumps chart on the lower right corner. Bumps charts are great at showing change over time. In this case, the authors are comparing two periods "2010-2019" and "2019-2020". By glancing at the chart, one quickly divides the causes of death into three groups: (a) COVID-19 and CVD, which experienced a big decline (b) respiratory, accidents, others ("rest"), and despair, which experienced increases, and (c) cancer and infectious, which remained the same.

And yet, something doesn't seem right.

What isn't clear is the measured quantity. The chart title says "months gained or lost" but it takes a moment to realize the plotted data are not number of months but ranks of the effects of the causes of deaths on life expectancy.

Observe that the distance between each cause of death is the same. Look at the first rising line (respiratory): the actual values went from 0.8 months down to 0.2.


While the canonical bumps chart plots ranks, the same chart form can be used to show numeric data. I prefer to use the same term for both charts. In recent years, the bumps chart showing numeric data has been called "slopegraph".

Here is a side-by-side comparison of the two charts:


The one on the left is the same as the original. The one on the right plots the number of months increased or decreased.

The choice of chart form paints very different pictures. There are four blue lines on the left, indicating a relative increase in life expectancy - these causes of death contributed more to life expectancy between the two periods. Three of the four are red lines on the right chart. Cancer was shown as a flat line on the left - because it was the highest ranked item in both periods. The right chart shows that the numeric value for cancer suffered one of the largest drops.

The left chart exaggerates small numeric changes while it condenses large numeric changes.




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Cody Custis

The problem with the chart on the right is that it looks like Cancer causes a positive number of months of life.

Acquiring cancer has a negative effect on one's remaining life expectancy. An individual born into a cohort with remaining life expectancies like today's (the proper meaning of period life expectancy) who dies old is more likely to die of cancer than other causes. Long life causes cancer (both biologically and in the sense of not dying from other causes), not the other way around.

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