In making an ever-important comparison between McDonald’s locations and golf courses in the United States, I wanted to use Dorling cartograms to show counts and which was more common in a given location. But my data wasn’t shaped quite right, so I broke it down and used parts of previous projects and tutorials.

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Heatmaps in R usually require or assume a rectangular dataset with the same sized cells all the way through. Sometimes data is unevenly spaced though, in which case you can either reformat your data to squeeze it into a function, or you can draw it ad hoc. This tutorial is for the latter.

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To show income sources for different groups and people, I wanted to show a percentage breakdown between wages, business, and investments, but also show the total amounts.

A while back I showed how people spend their money using a mosaic plot followed by unit charts, so I figured I’d just combine the two, and this is what I got, which shows average income sources by income group.

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You can use straightforward functions in R to draw certain shapes, such as circles, squares, and rectangles. However, sometimes you need to draw a more complicated shape or one that’s based on data.

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Clusters of bubbles might not be the most visually precise way to show counts, but the elements can lend weight to the individuals that aggregates represent as a whole. The one-to-one ratio between element and count feels less abstract than a bar or a line.

In this tutorial you learn how to create this one-to-one ratio.

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Maps are a good way to visualize spatial data, but to maintain geographic accuracy, there are often tradeoffs. For example, a choropleth map, which shades regions by a specified metric, maintains the same shape regardless of the data you color by. Larger regions always take up more space on the screen and smaller regions take up less space.

Cartograms are a response to this challenge by sizing regions to match the data of interest, at the sacrifice of geographic accuracy. This can get messy, as you can imagine, so you’ll have to use your judgement for when and when not to use them.

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It’s typically straightforward to make and read a line chart. The position on the line represents a value, and the slope between points represents a rate of change.

Usually a line chart that represents a single time series uses a solid color for the line. But while messing with a heatmap, which uses color as its primary visual encoding, I was curious what you could show if you introduced a color scheme to a line chart.

I ended up with this gradient line on a baseline chart that shows change in U.S. births:

A diverging color scale indicates whether births were above or below the baseline. While the line itself shows the same change, the color seems to highlight the changes.

In this tutorial, you’ll learn how to make a similar gradient line.

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