Learn to visualize your data like an expert with these practical how-tos for presentation, analysis, and understanding.
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.
When you have several time series over many categories, it can be useful to show them separately rather than put it all in one graph. This is one way to do it interactively with categorical filters.
There are various ways to visualize connections, but one of the most intuitive and straightforward ways is to actually connect entities or objects with lines. And when it comes to geographic connections, great circles are a nice way to do this.
Ever since Hans Rosling presented a motion chart to tell his story of the wealth and health of nations, there has been an affinity for proportional bubbles on an x-y axis. This tutorial is for the static version of the motion chart: the bubble chart.
The goal of Chernoff faces is to show a bunch of variables at once via facial features like lips, eyes, and nose size. Most of the time there are better solutions, but the faces can be interesting to work with.