R

We do it because things that glow in the dark are awesome, including line charts.

Being able to work with microdata from the American Time Use Survey, via IPUMS, means you can subset, filter, and categorize how you want. This makes it easier to explore questions.

With color as the visual encoding, choose the scales that allow you to see actual patterns.

Ridgeline charts are nice to look at, and that is enough reason to make them. Use a gradient fill for extra sauce.

GeoTIFF data can provide high granularity but make it hard to see patterns when data is sparse. Contour lines provide an aggregated view that makes patterns easier to see.

There are packages to make cartograms, but in some cases you might need a more flexible solution.

There are existing functions and packages to make heatmaps in R, but when the data is irregular, it’s worth going custom.

Process the data into a usable format, which makes the visualization part more straightforward.

In a cross between unit charts and variable width bar charts, we can show total counts and relative proportions at the same time.