The Pie Chart

What is a Pie Chart?

A pie chart is a circular data visualization tool that shows the relative contribution of different categories to an overall total. A wedge, or slice, of the circle represents each category’s contribution to the whole, making the chart look like a pie that has been cut into different slices (hence the name). Pie charts are most effective in showing the contributions of data segments as a percentage of a whole.

When to Use a Pie Chart

As mentioned above, pie charts are most useful for comparing parts of a whole, but only when there are a minimal number of categories being compared. Using fewer categories makes it easier for your audience to quickly understand and identify how each category relates to the whole, and can also make it easier to differentiate between the sizes of each slice.

Because they don’t get into specifics, pie charts should be used when comparisons between approximations are sufficient for discussion about the data being visualized. While pie charts may just be visually appealing to some, they satisfy the ‘at a glance’ requirement of data visualization better than other charts when studying proportions.

Let’s take a look at an example where the pie chart is a great choice of visualization:

Chart made using Chartio

In this example, we’re interested in looking at a camping store’s revenue based on the seven products it sells. Each slice of the pie chart represents a different product from the store, and its size corresponds to the percent of the total revenue that product brought in. We can see the name of the product and the numerical value for the percent in the chart legend in the top right corner of the chart area.

From the pie chart, it’s easy to see which products contributed the most and the least to the camping store’s overall revenue. This type of chart can help answer specific questions like “Did the Canyon Mule Extreme Backpack makeup half of the store’s revenue?” or “Is the TrailChef Kettle the store’s best selling product?” If the point of your data analysis isn’t to know the precise value of each of the products, but instead to know how each product makes up the total revenue, the pie chart is the best visualization to use.

When NOT to Use a Pie Chart

Determining when to use a pie chart is usually simple, but there are some areas where a pie chart is not the ideal visualization choice. First, the pie chart can only be used for a part-to-whole comparison. It shouldn’t be used for part-to-part comparison or to showcase individual data points. Additionally, a single pie chart shouldn’t be used for a part-to-whole comparison for more than one area of interest; multiple different pie charts would be needed for this.

Second, pie charts should not be used for comparison with a large number of categories. When too many categories overcrowd the pie chart, it becomes very difficult for the human eye to interpret the differences between slice sizes. Increasing the number of slices decreases the size of the slices, making the visualization not very clear. This issue is often corrected by using more labels to get the measurements or percentages across, which makes the chart less concise and defeats the main purpose of a data visualization.

Let’s take a look at an example where the pie chart is NOT a great choice of visualization: 

Chart made using Chartio

This example is very similar to the previous one– we’re still interested in looking at a camping store’s revenue based on products, but this store sells fifteen products instead of only seven. Each slice of the pie chart represents a different product from the store, and its size corresponds to the percent of the total revenue that product brought in. We can see the name of the product and the numerical value for the percent in the chart legend in the top right corner of the chart area.

So why is a pie chart a poor visualization choice for this data? Well, from the pie chart we only see the revenue contribution of ten products, not the fifteen like we’re interested in; there are simply too many categories. The five missing products have been grouped into the “Other” category of the chart. Because they were grouped together and their grouped portion of the pie is so small, we know the five product’s individual contributions were not significant; nevertheless, we don’t know which products are in the group or exactly what their individual contribution is. Also because many slices of the pie have similar sizes, it’s hard to distinguish which products contributed to the overall revenue more without looking at the percentages in the chart legend.

Like the previous example, this chart can still help answer specific questions like “Did the Canyon Mule Extreme Backpack makeup half of the store’s revenue?” or “Is the TrailChef Kettle the store’s best selling product?” However, it cannot show how each product makes up the total revenue. In this example, the pie chart is not the best visualization to use.

Comparison of Composition Chart Types

Simply put, the pie chart is a data visualization that’s used to show a part-to-whole composition and how different categories contribute to an overall total. Another type of visualization that displays a composition of a whole is the stacked area chart. The table below gives the use case and pros and cons of the pie and area charts:

Pie Chart

Stacked Area Chart

Use

  • Visualize a part-to-whole relationship of multiple categories
  • Visualize a part-to-whole relationship of multiple categories while they change over time

Pros

  • Simple and quick to show proportions
  • Widely used and familiar
  • Shows trends and how component contribution changes over time
Cons
  • Can be difficult to interpret the size of and difference between angles in chart
  • Extensive labeling needed for increased understanding
  • Can become cluttered easily
  • Requires careful design to easily and accurately depict each area
  • Unfamiliarity can make interpretation difficult
  • Can become cluttered easily

References

 

About Bryn Burns

Hi! I'm Bryn Burns. I am a current senior at Virginia Tech pursuing degrees in Statistics and Mathematics. Data science and visualization are two things I'm very passionate about, as well as working with numbers and helping people learn. I'm thrilled to share my knowledge here at The Data School!