R

It might not be sexy, but you have to load your data and get it in the right format before you can visualize it. Here are the basics, which might be all you need.

Fill those empty polygons with color, based on shapefile or external data.

No need to settle for the mapping defaults in R. Apply map projections to show geographic data in a way most suitable for your work.

Geographic data is often available as a shapefile, and there’s plenty of heavy software to get that data in a map. R is an open source option, and as a bonus, much of the work can be done in a few lines of code.

Customizing your charts doesn’t have to be a time-intensive process. With just a teeny bit more effort, you can get something that fits your needs.

Choropleth maps are useful to show values for areas on a map, but they can be limited. In contrast, dot density maps are sometimes better for showing distributions within regions.

For when your geographic data is evenly spread rather than aggregated by government boundaries.

Change detection for a time series can be tricky, but guess what, there’s an R package for that. Then show the results in a custom plot.

Too many points to plot often means obscured patterns in the clutter. Density maps offer a smooth alternative.

The chart type often goes overlooked because people don’t understand them. Maybe this will help.

There are many exercise apps that allow you to keep track of your…