How to Make a Bump Chart in R, with ggplot
Visualize rankings over time instead of absolute values to focus on order instead of the magnitude of change.
The bump chart is a line chart variant that focuses specifically on ranks over time instead of absolute values.
The advantage of the bump chart is that it’s unaffected by large differences in magnitudes, whereas a standard line chart might find itself with a bunch of lines clustered at the bottom because of a high-value category. The bump chart instead spaces ranks evenly.
With this in mind, the process of making a bump chart is similar to making a line chart in ggplot. The difference is that you need to calculate ranks first (if they’re not available already), and because you’re looking at ranks, it is a good idea to adjust the vertical scale accordingly.
To access this full tutorial and download the source code you must be a member. (If you are already a member, log in here.)
Get instant access to this tutorial and over a hundred more, plus courses, guides, and additional resources.
You'll get unlimited access to hundreds of hours worth of step-by-step visualization courses and tutorials for insight and presentation — all while supporting an independent site. Source code and data is included so that you can more easily apply what you learn in your own work.
The tutorials are very helpful to move from "Oooo, cool!" to how to actually DO the cool.
Members also recieve a weekly newsletter, The Process. Keep up-to-date on visualization tools, the rules, and the guidelines and how they all work together in practice.
See samples of everything you gain access to:
More Tutorials See All →
How I Made That: National Dot Density Map
Mapping one dot per person, it’s all about putting the pieces together.
Calendar Heatmaps to Visualize Time Series Data
The familiar but underused layout is a good way to look at patterns over time.
How to Make Dot Density Maps in R
Choropleth maps are useful to show values for areas on a map, but they can be limited. In contrast, dot density maps are sometimes better for showing distributions within regions.