How to Make Better Pie Charts with On-Demand Details

by Jorge

This article goes much against conventional wisdom about pie charts (and doughnut charts) by answering these two simple questions:

  • Can we use a large number of categories in pie charts? (Yes, we can.)
  • Can we make a productive use of the apparently useless doughnut chart? (Yes, we can.)

Disclaimer (Sort of…)

Let me start by declaring this: I believe that the analysis of simple proportions is, by its very nature, very limited. It only scratches the surface of the data and it is useless for serious, decision-making processes.

A circular chart is poor because the underlying message is poor. If you can run a business using pie graphs to make sense of your data please let me know what market are you in, because I want to be there too (well, not really…).

Pie chart belong to the media and to some simple presentations. Leave them there. And don’t make the charts you see in the media your role model.

The part-of-whole issue

That said, one must recognize that proportions are so pervasive and hard-wired into our brain that escaping them is almost impossible.

A circular chart conveys perfectly the idea of part-of-whole relationship. You can’t use a bar chart to show this relationship because the whole just isn’t there! Yes, you can use percentage scales, yes you can say it in the title, but it isn’t the same thing, is it?

As I wrote in my previous post on loss aversion, each chart answers a question from a different perspective. Charts are not interchangeable.

Often pie charts are used just because they may look better (this is, of course, in the eyes of the beholder) but what the user really wants/needs to know would be better answered by a bar chart. This is a problem of graphic literacy and information management. It has nothing to do with the intrinsic qualities of pie charts.

The limit of 4 to 6 categories in pie charts

There is a widespread believe that you should not use more than four to six categories in a pie chart.

That’s is wrong or, at the very least, very incomplete.

In fact, you can use as many categories as you want, and still get meaningful insights from the chart. Problem is, you must know what to do with your data (graphic literacy and information management, again), and a large number of bad charts come from this simple fact: people don’t know what to do. Garbage in, garbage out.

“The Secret Strenght of Pies”

Here comes the fun part. In an article published back in 1991 by Ian Spence and Stephan Lewandowsky, titled “Displaying Proportions and Percentages” the authors write:

“the pie chart outperforms the bar chart for complicated comparisons, suggesting that the perceptual addition and comparison of components is inherently easier with the pie chart than the bar chart.” (emphasis added)

(By the way, the authors also say that this advantage will be lost if you “explode” the slices.)

Stephen Few, in his “Save the Pies for Dessert“, cites this article and writes about “the secret strength of pies”:

It is not difficult to believe that it is somewhat easier to sum the areas of slices in a pie than it is to imagine the combined heights of bars stacked on one another.(…) Regardless, the fact remains that a comparison of two sets of summed parts is rare in the real world. But, by all means, should you ever need to display data for this purpose, a pie chart would serve you well.

Please note that Stephen Few, in his highly regarded book “Show me the Numbers” says:

“I don’t use pie charts, and I strongly recommend that you abandon them as well.”

Few acknowledges that pie charts “could serve you well” in a very limited set of circumstances (“a comparison of two sets of summed parts is rare in the real world”).

Is it really rare? It may be, but that’s because people don’t know what to do with their data (again). Let’s see.

You have 10 or even 20 categories and you want to use them all (your loss aversion tendency?). Because 20 ungroupable categories are rare in the real world, you should be able to visually group them, using a color (hue) for each group and a different saturation for each category. By doing this, you are adding layers of detail, and the reader will be able to select the level of detail that suits his/her needs. This works best when using an interactive chart because you don’t have to label everything (just use your mouse to identify on-demand the more relevant detail categories) but even a static chart can be used (in this case, label only the relevant details).

The Consumer Expenditure Chart

I used this methodology to design the consumer expenditure chart above, with living expenditure (on the right) and discretionary expenditure (on the left).  As you can see, living expenditure accounts for almost 60% of the total. That’s something you can’t easily see with a bar chart.

Then, there is a second level of detail, where you have categories like Housing (more than half of living expenditure) or Transportation. And finally, you could use your mouse to identify those detailed categories in the outer gray ring.

I’ve added some arcs to compare the profile of total consumer units to consumer units with five or more persons. Each arc always starts at the same degree of the corresponding slice. Different proportions lead to gaps or overlaps. Please note that this is not a core feature of this chart. Just wanted to play a little with comparisons (an obvious issue: since the first arcs are closer to the center, a gap between them is different than a gap between the last arcs).

The Secret Strength of Doughnut Charts

As we saw above, pie charts are better than bar charts when comparing proportions. But, as soon as you add a second pie chart you are trying to compare proportion A1 with proportion A2, not proportions A and B of the same pie. There is a shift in the analysis and the pies become useless (use bar charts instead).

Just because you can merge both pie charts in a single doughnut chart it doesn’t mean that you gain efficiency, because the essential problems remain in place.

For many, a doughnut chart is a bad mutation of a bad chart. But if, just if, two bad’s become on good? Could a doughnut, if correctly use, become a kind of pie chart on steroids?

Let me emphasize this: never use a doughnut chart to compare series. I don’t, and I strongly recommend that you should avoid it as well… Always use a doughnut chart to add detail to a series. That’s the secret strength of doughnut charts.

And please, please, could someone write an article on doughnut charts for the English Wikipedia?

I made this chart in Excel

In case you are wondering, you can make the Consumer Expenditure chart in Excel, 2003 or 2007. Instead of the default theme colors, I used some of the colors that will be available in Chart Tamer (thanks, Andreas!).

Conclusion

Pie charts do not deserve their bad reputation. They seem to be more efficient than bar charts in some very specific tasks, like  comparing combined proportions. We should take advantage of that by adding multiple levels of detail. We shouldn’t be afraid of using a large number of categories, provided that those levels of detail are clear and meaningful.

The doughnut chart is the most misunderstood of our chart toolbox. It is seen as completely useless because two series should not be compared using circular charts, but that’s not what doughnut charts should be used for. They should be used to extend the power of pie charts, managing efficiently the level of detail that we need to add to create more insightful charts.

Is this a good way to use pie and doughnut charts? Please share your thought in the comments.

[Update: If you want to know how to create this chart (with a bonus hole-remover...) Jon has a detailed explanation here.]

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Chart-Making: Cures for Loss Aversion

by Jorge

Loss aversion – wrong chart example

Monney Income - wrong chart

JunkCharts writes an interesting post on how loss aversion can happen in chart-making. The general concept of loss aversion tells us that “people strongly prefer avoiding losses than acquiring gains”. Translated to chart-making, it means that there is a “tendency to avoid losing data at any cost”.

“To clarify, add detail” says Tufte. Corollary: you should make data-dense charts and maximize the data-ink ratio. Problem is, this fits too well into the loss aversion tendency. Take the above chart, for instance: does it make any sense to add those nine series to a single chart? What insight do you get from it? Only one: the designer don’t know how to handle a larger number of data series.

Remove irrelevant data series and you risk a mutiny on the Bounty, even if relevant trends are easier to detect. It is absurd, but very human.

So, how can you give the users all the data they expect while keeping the chart clean and readable? Well, to clarify, add detail to existing patterns (that’s what I just did to Tufte’s sentence…).

Tufte talks about “data layers”; Ben Schneiderman’s Visual Information-Seeking Mantra (“overview first, zoom and filter, then details-on-demand”); the focus+context technique. All they convey a simple idea: prioritize your data. Know what is relevant and what is nice to have. Don’t give the user a final product. Make an interactive chart and let her discover what’s inside.

I see this loss aversion tendency at work every day at the office. Do you too? How do you handle it?

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Pie Charts – A Devil's Advocate Point of View

by Jorge

In what seems to be a post-vacation syndrome, I am in the mood for pie charts. I see them everywhere, even in car logos.

Actually, I am more in the mood to defy current “crowd wisdom” about pie chats.

Search the web for “pie chart” and you’ll get more than one million results, and a depressing picture of human knowledge. Browse the first 100 and what do you get? Some educational(?) sites (poor kids), tutorials (Excel, php, java, Illustrator), humor (here, here, here), bad (here, here, here, here, here or here) or just plain stupid examples. You’ll also find them in in court or fighting government (who could ever imagine that?). I’ll leave for another post what the Wikipedia and the pie chart thread in Tufte’s Ask E.T. say about pie charts (Stephen Few’s Save the Pies for Dessert is not listed within the first 100 results).

An old litany

Some of these sites discuss the use of pie graphs, but they usually recite the same old litany: our perception is bad at judging angles, you should use no more than five or six categories, don’t use them to compare series, Cleveland’s findings, etc. (there also is at least one unfair comparison between pie and bar graphs and one very aggressive rant against them).

If there is something that I would like to have written about pie graphs it is this Expert notes at ManyEyes:

Pie charts have a mixed reputation. They are popular in business and the media but many information designers have criticized the technique. Some claim that the pie slice shape communicates numbers less exactly than other possibilities such as line length. But this remains unclear in the context of proportions: for example, we have seen no studies that looked at the task of judging whether an item is more or less than 50%. It’s also unclear whether exact communication of numeric values is the only evaluation criterion; at least one study indicates that use of a pie chart for analyzing a problem as opposed to a bar chart changes the way people think about the problem.

This is clearly more constructive than saying that “they are as professional as a pair of assless chaps” (less funny though).

Not all charts are born equal

Current wisdom presumes that bar graphs and pie graphs are equivalent. For that reason, bar graphs should be used, always. After all, they are more efficient, right? But if they are not equivalent, as the above quote suggests? Take a time series, for example. If you want to see trends, you’ll choose a line graph; if you want to compare data points you’ll use a column graph. They are very similar, but by choosing one or the other, the designer is making a choice of how he/she’ll  look at the problem. Bar graphs and pie graphs are very different, so shouldn’t we think twice before selecting a bar graph because of its presumed superior efficiency?

This disdain for pie charts has its roots in Cleveland’s work and in Tufte’s and Few’s writings. Their positivist view towards information visualization may be as relevant as the classic economic theory and its presumption that consumer always take the rational decision, but are we not all predictably irrational? I agree with Robert at EagerEyes when he says:

There is no doubt that we need to be careful about the choice of visual representation, and that we need to encourage the use of good charts and criticize the bad ones. But that doesn’t mean we can get lazy and squeeze everything into a few standard charts types we’ve been using for decades. That is especially true if we want people to actually care about what we’re trying to show – and not bore them to tears.

We should probably try to be more rational and circumspect in a decision-making environment and do not use the media as our role model, otherwise business visualization may become useless. However, ruling pie charts out is not the wisest decision.

Simple rules are made for beginners. Let’s break some. How about this one:  “you should use no more than five or six categories in a pie chart”. Are you sure?

(Before that, we must re-read what Cleveland said and what others said about Cleveland. That’s the next post.)

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The Best Pie Charts Come From Germany

by Jorge

Best Pie Chart Award
(clean and balanced. Your perception may not be great at comparing angles, but who cares?)

 

2th Place
(also nice, but too many slices, and I don’t like the title around the pie)

 

Lateral Pie-Thinking Award
(well, perhaps someone just messed up the template)

 

Designer’s Pie Charts Award
(data? what data?)

 

Seth Godin’s Pie Chart Award
(“makes an obvious point, no nuances“)

 

Consensus Pie Charts: The Venn Pie

 

Consensus Pie Charts: The Line Pie

 

Consensus Pie Charts: The Bar Pie

 

Flash Gordon Pie

 

We Try Harder Award

 

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One Year

by Jorge

And so the first year went by. I’d like to thank everyone that reads and contributes to the blog. I learned a lot with you last year (and I hopefully I gave something back).

Now, let’s see what the future brings.

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I Like Google Chrome, but…

by Jorge

I just downloaded Google Chrome, played a little and I must say I like it. It is very clean, so it goes well with my idea of information visualization. If I were a IE user I would probably make the switch, but for serious browsing you can’t beat Firefox+extensions. If a portable version is made available in the near future I’ll install it in my pen drive.

And, of course, you may want to think twice before using yet another Google property…

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Better Graphs, Faster Typing: Try Them Today

by Jorge

Do you type well? I mean, do your thoughts flow naturally through your fingers or, on the contrary, your (low) typing skills come between your thoughts and your writing?

I don’t type fast enough, I look at the keyboard from time to time and I don’t follow some simple typing rules that would save me months of my life.

My question is, if the benefits of adhering to those rules are so obvious, why don’t I spend invest some time learning how to use them properly?

The more I think about it, the more I feel that the reasons behind this are the ones that also slow down the adoption of best practices in information visualization.

Recognize the problem

The major reason is, of course, to recognize and accept that there is a problem. If you are happy with the way you do things today, why would you change it? After learning how a specific task should be performed, just close that box in your life and move on, no need to reopen it, thank you. You are a two-finger typist and a pie chart designer, and that’s more than enough.

How do you deal with this? Too often we are irrational creatures, so the benefits must be so obvious that not taking action would be almost embarrassing (remember that inertia is one of the most powerful forces in the known universe). Unfortunately, you can promise to double the typing speed, but can’t offer a similar quantitative measure to sell the benefits of better charts.

Imagine that a new company is selling a two-finger typing concept instead of all ten (“you’ll get sexier fingers!”). Too absurd? Well, I bet they would make a good living out of it. That’s exactly what Microsoft, Dundas or Business Objects do in information visualization. And unlike typing, peer pressure and marketing budgets actually work against you.

Break Old Habits. Start Today

As Stephen Few often emphasizes, the basic rules of information visualization are not difficult. Learn them and start practicing (here are a few). Just like typing.

That’s what I am doing right now. This was the first post I wrote with my eyes closed (well, the draft version, anyway). It was painfully slow. That this can help my writing or my typing remains to be seen, but it seems a good method to avoid distractions and remain focused.

So, how many fingers do you use to create a chart? :)

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Meet me Here

by Jorge

I’ll be here for the next 15 days. Great beach, air temperature and water temperature around 25ºC (77ºF), perfect for everyone that hates hot weather and cold water (that would be me). Best of all, I’ll be able to use my mobile internet connection…

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Charting around the clock

by Jorge

I’m a consensus kind of guy, I can’t help it. I always try to find the best parts of not-so-good things (a Curate’s egg syndrome?). Let me give you an example.

One of the reasons why people like pie charts is because of its strong and familiar metaphor – it is part of our daily life.

Another good metaphor is the analog clock. You don’t need a legend to know the time. So, why don’t you use it to display hourly data?

Take a look at the radar chart on the left (the roman numerals – neat, hum?). It displays pageviews per hour by hour of the day. There are two series, daytime and nighttime. As you can see, the nighttime pageviews are much lower (I wonder why…).

If you want to compare daytime and nighttime data do everyone a favor: forget about day and night. Don’t assume that those 24 data points should be split in midnight to midday and midday to midnight. Or just because you raise early, the split should be 6:00 a.m. to 6:00 p.m. and 6:00 p.m. to 6 a.m. Look at the data and do what it tells you to do.  A good split creates two series that maximizes variability between them (and each series becomes more internally consistent). In this case, the split was at 8:00 a.m/p.m.

Yes, but what about the Curate’s egg? Glad you asked.

Chandoo, over PointyHairedDilbert, had “an interesting charting idea to show the data around the clock“:

Jon Peltier doesn’t really like the idea and suggests a much more conservative aproach:

Now, shake both charts (shaken, not stirred…) and what do you get? My radar chart, of course! And what a fine mix of both it is!

Ok, where was I? Ah, yes, my soft boiled egg…

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Information Visualization Is a Jigsaw Puzzle

by Jorge

Jigsaw ChartOne of the reasons why information visualization in business is so poor is because many managers are clueless about it.

They really believe that a simple training course on Office tools is more than enough to create some nice 3D charts for a Powerpoint presentation (and they are right: that’s all they need, because that’s all they know).

Let’s see how well those courses perform, from a business visualization point of view. Do they tell you:

  • How to select the best graph to visually display a data set? No, they don’t. They just tell you how to create some very obnoxious ones;
  • What you need to know to understand a scatterplot or box-and-whiskers graph? No, they don’t. After some pie charts, there will be no time left to discuss scatter plots. Box-and-whiskers? What’s that?
  • That you must know what our working memory is and how it impacts on your slide presentations? No, they don’t. Just put a huge chart in a slide and another chart in the next one – now go back and forth between slides to compare them;
  • That your charts and graphs should answer your readers’ questions? No, they don’t. A graph is just another neat little thing you can do in Excel;
  • How to validate your data? No, they don’t. Let’s assume the data is always correct, shall we?
  • Why you should create dynamic charts whenever possible/relevant? No, they don’t. You didn’t pay for that highly advanced subject; let’s stick to sums and pies, ok?
  • How to priortize information (focus/context, Schneiderman’s mantra)? No, they don’t. Do you have twelve series? Plot them all! Trends? Patterns? What do you mean?

To create insightful graphs that will really help you making sense of the data and communicate efficiently, you don’t have to be an Excel expert, a graphic designer, a psychologist and a statistician. But information visualization is a jigsaw puzzle, and a single piece will never be enough to see the whole picture.

I saw this brilliant quote in Robert Cialdini’s Influence:

- Joe Pine (60’s talk show host who sported a wooden leg) to Frank Zappa: “So, with your long hair, I guess that makes you a woman.”
- Frank Zappa’s response: “So, with your wooden leg, I guess that makes you a table.”

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