R

You get a lot of bang for the buck with R, charting-wise, but it can be confusing at first, especially if you’ve never written code. Here are some examples to get started.

You saw how to make basic heat maps a while back, but you might want more flexibility for a specific data set. Once you understand the components of a heat map, the rest is straightforward.

Sometimes these cartograms can distort areas beyond recognition, but they can also provide a better visual representation for a region with a wide range of subregions. At the least, they’re fun to look at.

From the basic area chart, to the stacked version, to the streamgraph, the geometry is similar. Once you know how to do one, you can do them all.

When base graphics and existing packages don’t do it for you, turn to low-level graphics functions to make what you want.

R, the open source statistical software environment, is powerful but can be a…

Single data points from a large dataset can make it more relatable, but those individual numbers don’t mean much without something to compare to. That’s where distributions come in.

These tend to be made ad hoc and are usually pieced together manually, which takes a lot of time. Here’s a way to lay the framework in R, so you don’t have to do all the work yourself.

The familiar but underused layout is a good way to look at patterns over time.