Pie Chart & Donut Chart

Pie Chart presents your data as a circle divided into sectors that represent proportions of the whole.

When to use Pie Chart?

Pie Chart fits into use cases where you want to attract end-users to a large difference between a few sectors, help users to have a feel of the data distribution and encourage them to do further exploration.

Pie Chart is, however, unsuitable in cases:

  • When you want to have an accurate comparison of values between categories, especially when the differences are small.
  • When there are too many categories.

For such use cases, Bar Chart and Column Chart are recommended. For a detailed explanation, please refer to The Dos and Don'ts

Creating Pie Chart

To create a Pie Chart, simply drag in the categorical variable in the Legend Field and the measure into the Y-axis field. The size of a sector will correspond to the value of the category that it represents.

The Dos and Don'ts

Do not use Pie Chart for accurate comparisons

Using angles to compare values is much less accurate than using heights or lengths. For example, the different contribution of orders from different cities are hard to see with Pie Chart when the difference is small, even when we display the underlying data:

Using Column Chart can give us a clearer picture:

Avoid dimensions with too many categories

When there are too many categories, the slices will be too small and it is hard to estimate their relative values. It is tempting to add data labels in this case, but it will only make your visualization more cluttered:

It is best to choose a low-cardinality dimension, or group small values into "Others".

Styling options

  • Legend
    • Display Label: ****Show or hide the legend
    • Alignment: ****Decide where you want to place your legend
  • Donut Chart: Toggle this on to display your pie chart as a "donut"

  • Other options:
    • Show data label: Display the raw value that the sectors represent
    • Show Total: Display the total value of the whole pie
    • Show percentage: Display the percentage that the sectors represent instead of the raw value
    • Group small values into "Others": Useful when you have too many categories