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The first dataset is so simple that no chart is appropriate. "Companies A and B control two-thirds of the market". Big text. Done. The second dataset could be
by Armisael16 9y ago
The first dataset is so simple that no chart is appropriate. "Companies A and B control two-thirds of the market". Big text. Done.
The second dataset could be done better with stacked bars (eg, https://i.imgur.com/oGzIKED.png https://i.imgur.com/oGzIKED.png). The author seems to think stripping temporal data is a virtue here, for some reason, when it clearly isn't.
The third dataset should be presented with a heatmap on a state (or county) level, a la https://projects.fivethirtyeight.com/mortality-rates-united-states/ https://projects.fivethirtyeight.com/mortality-rates-united-...
- LeifCarrotson 9y agoHeatmaps on state and county levels are really difficult to get right. For one thing, the dataset does not do a good job of conveying uncertainty, specifically that caused by low-population counties. The second-highest mortality rate county - Buffalo, South Dakota - has a population of 1,912 people, and the "City" which is the county seat is remarkably the least populous county seat in the US with a population of 14. The various mountainous locations (appearing to be mostly ski towns) in Colorado which top the list of lowest-mortality counties have similarly low populations. For another, size and population density are often misleading. That small, orange blotch in the lower left? That's LA County, with a population greater than the 10 lowest-population states combined (including such large areas as Montana, North and South Dakota, Alaska). Information in these low-population areas is over-represented, while the largest cities are under-represented. I suppose those caveats are just something to be aware of. Because I don't know of a good solution for to how to accurately and concisely convey this sort of information.