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This is how misunderstanding pie charts led to them being hated
The importance of choosing the right chart for your goal.
The pie chart is one of the most hated charts in all of the visualization, and it became that way because it was misused. They are less accurate than other charts based on the elements of their visualization and should have only be used for niche scenarios. However, because they were used for nearly everything, they’ve garnered a reputation for being ineffective, lazy, and just plain bad.
But pie charts are still useful for certain types of goals, and it highlights the importance of choosing the right chart to answer your questions. Because if you’re not careful, your favorite chart could be next.
The 4 elements of visualizations
According to Nathan Yau’s Data Points: Visualization that means something, 4 elements make up a chart: visual cues, a coordinate system, scale, and context.
Choosing different elements, such as visual cues, is what can turn a bar chart into a column or line chart. However, these visual cues shouldn’t be chosen randomly: different cues are best suited for different types of data.
For example, lines and points can be used when we’re concerned about involves changes over time. But multiple visual cues may be suited towards addressing. For that example of changes over time, it wouldn’t be uncommon for bars to also be a viable option (i.e. creating a bar chart).
So that’s where we have to consider something else: how accurately our users can perceive these visual cues.
Accurately perceiving visual cues
In 1985, William Cleveland and Robert McGill conducted a study on graphical perception to understand how accurately people perceive visual cues. This resulted in this sort of ranking system of how accurately people not only…
This accuracy can be a big influence in determining which chart to use: for example, given that users can perceive position more accurately than length, it makes sense to use a chart that utilizes position (i.e. line charts) for changes over time unless there is a specific reason not to.
More importantly, this accuracy is a huge factor for certain questions, such as if you want to compare values across many different categories.
But while accuracy is useful for many things, it’s not the end-all metric for determining which chart type to use: if the goal of the graphic is to facilitate precise comparisons, then you should use the most accurate visual for the data.
So with that in mind, let’s look at the elements of a pie chart and how it can yield much less accurate designs.
The characteristics of pie charts
A pie chart, contrary to the more common charts, uses two odd elements: A Polar system and angles.
Polar system:
Almost every single common chart uses what’s called a Cartesian system, which is better known as the X & Y chart. From points and lines to bar lengths, all of them use this system to produce measurable visual cues. Except for pie (and donut) charts: they use a Polar system. A Polar chart is a coordinate system in which measurements are recorded in angular measurements.
And this almost immediately leads to some issues.
First and foremost is that there is always a fixed size for Polar charts. A Cartesian system graph can have scales that range from 1–5 or 1–100, which can result in more accurate measures of visual encoding. But a Polar chart must always be thought of in 360 degrees. It’s one of the cardinal design rules for pie charts that it always must add up to 100%. As a result, it must use another inaccurate measurement, angles.
Angles:
Given the accuracy chart from before, we already know that angles are less accurate than other visual cues. But to understand why to let’s take a look at how pie charts compare to bar charts.
Imagine you wanted to figure out which category was about 13 units long. If we were just to use the elements that we’ve talked about, this is how it might look in a bar chart.
It’s pretty easy to see that Category E looks closest to being 13 blocks long, even if you didn’t know the exact measurements. The length of the bars, combined with the scale, shows us which categories are between 10 and 20 units long.
But you can’t simply have something 13 units long in a pie chart: the pie chart has a fixed measurement area of 360 degrees.
So you would have to convert 13 units into 13% of the total. Given that 1% of the chart is 3.6 degrees, you would be looking for the category that is 13 * 3.6 = 46.8 degrees out of 360.
Could you tell me at a glance which category is on this chart?
Probably not. You’d probably need additional context to explain these sections, which often defeats the purpose of a visualization in the first place.
What are pie charts good for?
I’m not that much of an expert in the field to give a definitive diagnosis on the future of pie charts. But thinking about it from an audience perspective, I can say that there’s one strong but niche case for using pie charts.
And that’s to show minority or majority.
We know that the components of the chart (i.e. using angles and the Polar system) make for a very inaccurate chart for comparison and even composition. But that doesn’t matter if the goal of the visualization is to simply show that you are a small or large part of the larger picture.
It’s a much rarer use of this chart than how it’s typically been used, which has resulted in these charts becoming the bane of every designer and data visualization specialist.
And unfortunately, it’s not the only one. Recently, there’s been a trend of another chart being used, bubble charts, with an eerily similar trajectory of becoming disliked. It’s considered a junk chart by many professionals, with it running into many of the same comparison problems as pie charts.
But there’s a right and wrong reason to use this visualization, just like there’s a right and wrong reason to use the pie chart.
The key is always to first figure out the message you’re trying to convey with your visualization.
Once you do that, you can consider how important accuracy is to convey that message and select the right chart from that.
I write about UX, Healthcare, and Productivity regularly. If you would like to learn more about UX, I’ve created courses about Design Communication and UX Research on a budget.
