Within the past year, I’ve led 60+ in-person and virtual workshops for 2,800+ participants. Most of these trainings have focused on data visualization best practices and how-to’s; other topics have included dashboard automation, research methods, and data analysis.

I can always tell when someone has attended a data visualization training in the past because they tell me, “Ann! I know everything there is to know about data visualization! I know that I can never use pie charts!”

That advice about never using pie charts is only half-true.

Pie Chart Guidelines

Pie charts are okay when they:

  1. are well-formatted. No 3D, exploding slices, leader lines, or legends.
  2. display nominal variables. Ordinal variables don’t belong in a pie chart.
  3. add to 100%. I’ve seen pies that only add to 90% because the designer deleted the “other” category and forgot to recalculate the new percentages.
  4. contain positive numbers. I’ve seen designers place a mix of positive and negative numbers inside the same pie chart, which doesn’t make any sense.
  5. display a single point in time. Patterns over time belong in a time series graph, like a slope chart, line chart, or dot plot.
  6. only have two or three slices. Four slices is pushing it.
  7. are displayed individually. Only show one pie chart at a time. No small multiples pies. Comparisons across multiple pies are time-consuming.

Finally, while I don’t consider this to be a strict guideline, pie charts tend to be easiest to read with common fractions, like a one-fourth vs. three-fourths pie or a one-third vs. two-thirds pie.

Given these guidelines, I use pie charts to show:

  • male/female/etc. gender categories;
  • yes/no survey responses; or
  • other binary data (e.g., students who graduated high school on time vs. didn’t graduate high school on time; adults who live in single-family homes vs. adults who live in other housing types).

Let’s tackle these pie charts! It’s not sufficient to tell you to avoid pie charts. You need to know what to do instead. Here are some pie chart makeovers that are inspired by my real projects.

If Your Pie Chart is Poorly Formatted…

The chart on the lower left is poorly formatted. This one is 3D, so the slices look larger or smaller than they really are… and, it’s exploding, which is distracting for viewers… and instead of the percentages being right on top of the pie slices, now there’s a tiny legend down below the pie, which means our viewers would have to zig-zag their eyes around the slide to tell which slice is which. The final sin in this poorly-formatted pie chart is that there are leader lines, those gray lines connecting the 25% and 75% to their corresponding slices. Plus, So much ink is on the page, yet so little is actually focused on the data.

The well-formatted pie chart on the lower right is fair game. Gender is nominal or categorical, so that works. We’re only showing a single point in time, so that works too. And we’ve only got two different slices.



If You’ve Got Ordinal Data…

Ordinal or sequential data is when the categories have a natural order, like responses to a survey that go from strongly agree to agree to disagree to strongly disagree.

In this case, you’d swap out your pie chart and use a stacked bar chart instead, so that viewers can tell which category is at which end of the spectrum – the agrees on one side and the disagrees on the other side.



If You’ve Got Negative Numbers…

I mentioned that pie charts are only for positive numbers, not negative numbers. Sometimes we have negative numbers when we’re dealing with changes over time.

In this example, we’re looking at four products and whether they increased or decreased in sales compared to the previous quarter. For example, Product A’s sales decreased 20% compared to the prior quarter while Product B’s sales improved 40% compared to the prior quarter.

Instead of a pie chart, we’d use a column chart or bar chart. In your software program, the negative numbers will automatically flip in the opposite direction of your positive numbers. The axis line runs across the middle at 0% and we can see which products went down (like Product A) and which products went up (like Products B, C, and D).



If You’ve Got Patterns Over Time…

What if you have time series data, that is, patterns over time? Maybe you’re trying to show data for each Quarter – Quarter 1, Quarter 2, Quarter 3, and Quarter 4 – or for each month, or for each year in the grant cycle.

Swap out your pie chart and use a line chart instead. You want viewers to see the beginning point – Quarter 1 – over to the end point – which is Quarter 4.



If You’ve Got More than Two or Three Categories…

Pie charts are easiest to read with only two or three slices.

What if you have lots of different slices, like favorite ice cream flavors? This pie chart has too many slices – vanilla, chocolate, strawberry, mint, and cookie dough. It’s too hard for our brains to compare the slices to each other.

Swap out the pie chart for a bar chart and order the bars from greatest to least (or least to greatest). Chocolate would be listed first because it’s the most popular, and cookie dough would be listed last because it’s the least popular.


If You’re Tempted to Display More than One Pie at a Time…

What if you want to compare several companies, organizations, outcomes, etc. all at once? Pie charts are hard enough to read. Our brains don’t do well deciphering the angles, area, or circumference of circles. Two or three or four different pie charts can be understood, but with way too much mental energy.

In this example, we’re asking our viewers to look first at the 20% angle, and then at the 40% angle, and then their eyes have to zig-zag to the 60% angle, and then their eyes have to zig-zag over to the 80% angle. So. Much. Work.

In this case, you’d swap your small multiples pie chart for a small multiples stacked bar chart. The part-to-whole pattern is still there, but now our viewers’ eyes only have to make a single, diagonal swooping motion down the page to compare all four companies at once. Less energy required for reading, more energy reserved for making decisions based on that data.



In future posts, I’ll expand on these topics with a few real-life remakes that have been submitted by workshop participants. Stay tuned.