Learn to visualize your data like an expert with these practical how-tos for presentation, analysis, and understanding.
Maybe you want to make spatial comparisons over time or across categories. Organized small maps might do the trick.
Also known as specialized or custom line charts. Figure out how to draw lines with the right spacing and pointed in the right direction, and you've got your slopegraphs.
Make a lot of charts at once, line them up in a grid, and you can make quick comparisons across several categories.
Although time series plots and small multiples can go a long way, animation can make your data feel more real and relatable. Here is how to do it in R via the animated GIF route.
When you plot a lot of data at once, points and lines can obscure others and hide patterns. Transparency can help reveal what is really there.
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.
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.