Composition – These visualizations refer to data sets that change over time or include static data (do not occur over time or are non-spacial). With static data, a pie chart can work, however there are a host of other options that can tell the same story. With data that changes over time: the number of data points is a critical asset. One should also consider that the axis needs to match the order of the data (ergo in a stacked bar chart the years 1990-1999 should be listed in order as opposed to by highest value).
Distribution – Here you are mapping a single variable versus two variables. With this data set, you don’t want to scroll bars to toggle through the data: you just want the full picture. As you can see from the choices above if you have many data points, it’s best to use a line histogram. If you are interested in mapping every point, you would want to use a bar histogram.
Relationship – The fourth and final group of visualizations ups the ante from the Distribution charts. Here you are always mapping two or three variables. The best guideline to follow in this case is rather clear cut: if you have two variables and you want to add nominal or ordinal data to categorize your data, then use color. But if you’re adding a third variable that’s interval or ratio you can see that size is better. This also ties back to my second blog post about the best way to encode data.
I hope that the process of experimenting with data visualization is now clearer to you. Hopefully you can begin to consider other factors in your charts, including the things we didn’t cover, like maps, slope graphs or box charts. Through this series, I wanted to point out that there are more ways to visualize data than a simple bar chart or pie chart: it’s more about properly representing your data and how you display it to your users.