Learn to visualize your data like an expert with these practical how-tos for presentation, analysis, and understanding.
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.
When presented with a static graphic, it can be useful to see specific values after you see overall patterns. This tutorial shows you how to add simple interactions to a choropleth map so you can get specifics for regions.
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.
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.
Time series charts can easily turn to spaghetti when you have multiple categories. By highlighting the ones of interest, you can direct focus and allow comparisons.
The familiar but underused layout is a good way to look at patterns over time. This tutorial gives you an easy way to make them and guides you through the code so you can adapt it to your needs.
You can control graph elements with code as you output things from R, but sometimes it is easier to do it manually. Inkscape, an Open Source alternative to Adobe Illustrator, might be what you are looking for.
Filled contour plots are useful for looking at density across two dimensions and are often used to visualize geographic data. It's straightforward to make them in R — once you get your data in the right format, that is.
Color can drastically change how a chart reads and what you see in your data, so don't leave it up to chance with defaults.