Pie charts have a fairly narrow use-case that is encapsulated particularly well by its definition. In order to use a pie chart, you must have some kind of whole amount that is divided into a number of distinct parts. Your primary objective in a pie chart should be to compare each group’s contribution to the whole, as opposed to comparing groups to each other. If the above points are not satisfied, the pie chart is not appropriate, and a different plot type should be used instead.

The values that comprise a whole and the categories that divide the whole generally come in two major varieties. First of all, is when the ‘whole’ represents a total count. Examples of this include votes in an election divided by candidate, or number of transactions divided by user type (e.g. guest, new user, existing user).

A second type of ‘whole’ is when the total is a sum over an actual data variable. For example, we might be interested not in the number of transactions, but the monetary total from all transactions. Dividing this total by an attribute like user type, age bracket, or location might provide insights as to where the business is most successful.

North

491 064.51

East

283 445.43

South

128 753.87

West

263 391.13

Data for a pie chart can be summarized in a table like the above, where the first column indicates a category, and the second the proportion, frequency, or amount of that category. Usually, the total does not need to be specified separately unless it is to be listed somewhere else on a generated figure. Alternatively, some tools can just work with the unaggregated data as in the below table, essentially performing the aggregation into the table above at time of pie chart creation.