How to properly use a pie chart

It might seem odd to talk about pie charts out of the blue, but let me guarantee that I want to talk about them precisely because of the timing.

It all began with a post from the Office for National Statistics discussing guidelines on the use of the dessert chart. Then over the weekend Cole Knaflic, author of Storytelling with Data, updated her stance on pies (hint: she still dislikes them).

Whether you love them or hate them, the humble pie chart is here to stay. But if it is to stay, we should at least make sure it stays in the right context. That its used the proper way.


What’s wrong with the pie?

So you might be wondering: Is there an improper way to use the pie chart?

Yes. Lots. Pie charts are one of the most difficult charts to use because it doesn’t have a common baseline.

When comparing values, we’re used to comparing off the same baseline. See the example below where its easy to say which bar is tallest and shortest because all the bars are along the same axis.

Without having to think I know #3 is the tallest, #5 is the shortest. BBC KS3 Maths: Bar Charts.


But with pie charts, there is no single axis to baseline off. Instead we’re comparing areas of sectors or arc lengths.

Arcs and sectors

What comes more natural to you? Getting the:

  • length of  a bar?
  • Or the arc length of a circle?

If you answered the latter, then go ahead and use pie charts.

But assuming you’re like most people, the second one takes takes some extra brain power to process. In data visualization, that extra cognitive load is a sign you’re doing something wrong. The chart is meant to make people’s lives easier by visualizing the data for them, not make it even harder.

If the values can be compared in a bar chart, go with the bar chart. Don’t make your audience do the extra math that comes with using a pie chart.

This problem is further compounded when the chart is in 3D:

The third dimension skews the physical appearance of the chart. This is called forced perspective:

Forced perspective is a technique which employs optical illusion to make an object appear farther away, closer, larger or smaller than it actually is. It manipulates human visual perception through the use of scaled objects and the correlation between them and the vantage point of the spectator or camera.

When in 3D, the sectors that are closer to the eye seem larger due to forced perspective, even though in reality they may be physically smaller. Look at that Firefox sector… doesn’t it seem larger than I.E.?

This defeats the purpose of the chart, which is meant to represent the relative sizes of data.


When should I use the pie?

So, we now know the pie chart isn’t very good at comparing values. But there is one thing the pie is good at, even better than any other chart I know of.

It’s very good at representing something is part of a whole.

When we think of pie charts, what comes to mind is the slice of pie, not necessarily the whole pie itself.

Pie charts are excellent at showing the composition of a whole (i.e., that the sum of parts is 100%).

But does this mean we should always be using pie charts to show composition?

Again, no. As always it depends on context.

If we’re comparing the relative magnitudes of the parts that comprise the whole, again the bar chart wins. Its comparing values after all.

But if we just care to emphasize that yes, these parts comprise the whole, regardless of by how much, then the pie chart wins.

It all depends on where you’re putting emphasis on. Again, it depends on context.

Pie charts are especially effective for single values that are relatively small compared to the whole. See example 10% pie chart above.

Think: Into how many slices do you usually cut your pizza?

Chances are, your average slice looks like the pie chart above. It works because it matches our mental image of what a slice of pie (or pizza) is supposed to look like.

So, part of a whole, and even better if that part is small. When you need to represent your data in such a way then pie charts are the way to go.

Otherwise, stick to other charts that better suit your purpose.


Note that this is bar any aesthetic considerations, such as having lots of roundish shapes in the same page. In that case you’re going to have to consider which is more important: context or design. I’d say context, but this always leads me to arguments with my art-inclined friends 🙂


UPDATE: I found this great post which talks through use cases of when using a pie chart is okay.

GameSpace: Visualizing videogame likeness.

When I first started getting into data science, one of the projects I had set out to do was to build a visual and interactive database/recommendation engine for games.

The idea was the system would build you a car based on your preferences (games you already love), and then drop you at some random point in a data landscape visualization of thousands of games. You drive around and explore this landscape: mountains indicate games closer to your preference, while valleys are games you’re likely to hate.

Well, researchers from UC Santa Cruz beat me to it. GameSpace now exists:

GameSpace is a visualization of the videogame medium as an explorable 3D space. Each of the nearly 16,000 stars in its galaxy represents an actual game that exists in the real world, and stars are placed in the space such that more similar games are nearer to one another.

–What is this? GameSpace FAQ.

They used outer space where I had imagined roads, but the basic idea is the same.

Still, its a lovely thing to look at. Reminds me lot of the loading screen for No Man’s Sky, in itself a randomly-generated space exploration game.

How much will Valentine’s Day cost me?

J.Lo may say “Love Don’t Cost a Thing”, but Americans are planning to spend $136.57 on Valentine’s Day anyway.

How much does Valentine's Day cost?

When I chanced upon the results of NRF’s Valentine’s Day Spending Survey I knew it was the perfect material for this week’s post. Data from a reliable source that’s actually relevant and interesting? Ah, be still my geeky heart.

In today’s post, I talk through the thought process in coming up with an infographic like the one I made above.


Continue reading “How much will Valentine’s Day cost me?”

Microsoft DAT206x: Analyzing and Visualizing Data with Excel Review

You never actually analyze and visualize data, but this course is worth taking as it’s a good introduction to using Power Pivot and Power Query–both of which are useful for managing large amounts of data in Excel. Just make sure you manage your expectations.

Update: To follow my progress in this program, check the Microsoft Professional Program tag.



For those who are following this blog for my data science updates, it might be of interest to you that I am still working on Microsoft’s Professional Program for Data Science  (on beta). I have recently completed my second course, Analyzing and Visualizing Data with Excel.

This was my gateway course to the program. Excel enthusiasts at work had recommended it as a good introduction to PowerPivot, and it was only later that I found out the course was part of a larger data science program.

My primary purpose for taking the course was increasing my proficiency in Excel. I currently manage a large-scale project with an equally large-scale tracking spreadsheet. The spreadsheet easily gets out of hand due to the sheer number of assets involved and because it pulls data regularly from multiple data sources. I was hoping the course would help me clean up the data and make it sustainable to maintain in the long run.

Because of this, I’m reviewing the course from a more practical Can I use this at work? perspective rather than its relation (or lack of) to data science.

It took me about a month to complete, starting September 2016. You can follow my progress in the MS Data Science Program by using my tag Microsoft Professional Program.

Continue reading “Microsoft DAT206x: Analyzing and Visualizing Data with Excel Review”