Aug 112016
 

Overview

As readers of this blog know, I have my problems with donut charts.

That said, I acknowledge that they can be cool and, under certain circumstances, enormously useful.

On a recent flight I was struck by how much I liked the animated “estimated time to arrival” donut chart that appeared on my personal TV screen. An example of such a chart is shown in Figure 1.

Figure 1 -- Donut chart showing progress towards completion of a flight.

Figure 1 — Donut chart showing progress towards completion of a flight.

I find this image very attractive and very easy to understand — I can see that I’m almost three-quarters of the way to my destination and that there are only 49 minutes left to the flight.

So, given how clear and cool this is, why not use them on a dashboard?  And if one is good, why not use lots of them?

It’s the “more than one” situation that may lead to problems.

Trying to make comparisons with donut charts

The flight status chart works because it shows only one thing only: a single item’s progress towards a goal.

Let’s see what happens when we want to compare more than one item.

Consider the chart in Figure 2 that shows the placement rates for Fremontia Academy.

Figure 2 -- Donut chart showing placement percentage.

Figure 2 — Donut chart showing placement percentage.

A 95% placement percentage is really impressive.  Is that better than other institutions?  If so, how much better is it?

Figure 3 shows a comparison among three different institutions using three different donut charts.

03_3Donuts

Figure 3  — Three donut charts displaying placement percentages for three different institutions.

Before digging deeper let’s replace the three separate donuts with a donut-within-a-donut-within-a donut chart (Figure 4.)

Figure 4  -- A concentric donut chart (also called a “radial bar chart” or a “pie gauge.”)

Figure 4  — A concentric donut chart (also called a “radial bar chart” or a “pie gauge.”)

“What’s the problem?” you may ask, “these comparisons are easy.”

While you may be able to make the comparisons you are in fact working consierably harder than you need to be.

Really.  Let me prove it to you.

Let’s suppose you wanted to compare the heights of three famous buildings: One World Trade Center, The Empire State Building, and The Chrysler Building (Figure 5).

Figure 5  -- Comparing the size (in feet) of three large buildings.

Figure 5  — Comparing the size (in feet) of three large buildings.

Now that’s an easy comparison. With virtually no effort we can see that One World Trade Center (blue) is almost twice as tall as The Chrysler Building (red).

Now let’s see how easy the comparison is with donuts (Figure 6.)

Figure 6  -- Three large buildings twisted into semi-circles.

Figure 6  — Three large buildings twisted into semi-circles.

Here are the same buildings rendered using a concentric donut chart (Figure 7).

Figure 7  -- Three skyscrapers spooning.

Figure 7  — Three skyscrapers spooning.

Yikes.

So, with this somewhat contrived but hopefully memorable  example we took something that was simple to compare (the silhouettes of buildings) and contorted them into difficult-to-compare semi-circles.

With this in mind, let’s revisit the Placement example we saw in Figure 3.

Here is the same data rendered using a bar chart.

Figure 8 -- Placement percentage comparison using a bar chart.

Figure 8 — Placement percentage comparison using a bar chart.

The comparison is much easier with the bars than with the donuts / semi-circles. You can tell with practically no effort that the blue bar is approximately twice as long as the red bar, even without looking at the numbers.

Indeed, that’s a really good test of how clear your visualization is: can you compare magnitude if the numbers are hidden?

Pop quiz — how much larger is the orange segment compared to the red segment?

Figure 9 -- Trying to compare the length of donut segments is difficult.

Figure 9 — Trying to compare the length of donut segments is difficult.

Now try to answer the same question with a “boring” bar chart.

Figure 10 -- Comparing the length of bars is easy.

Figure 10 — Comparing the length of bars is easy.

With the circle segments you are squinting and guessing while with the bars you know immediately: the orange bar is twice as large as the red bar.

More downsides for donuts

In addition to comparisons being difficult, how would you handle a situation where you exceeded a goal?  For example, how do you show a salesperson beating his / her quota?  With a bar chart you can show the bar going beyond the goal line (Figure 11).

Figure 11 -- With a bar chart it's easy to show more than 100% of goal.

Figure 11 — With a bar chart it’s easy to show more than 100% of goal.

How do you show this with a donut chart?

Rhetorical question.  You can’t.

Conclusion

If you only have to show progress towards a single goal and don’t need to make a comparison then it’s fine to use a donut chart. If you need anything more complex you should use a bar chart as it will be much easier for you and your users to understand the data.

Special thanks to Eric Kim for creating the building images.

 

Mar 302016
 

Some thoughts on functionality, beauty, crown molding, and lollipop charts

Overview

I’ve been writing a book about business dashboards with Jeffrey Shaffer and Andy Cotgreave and we’ve conducted screen-sharing sessions with dozens of people and reviewed scores of dashboards. We had a particularly enjoyable jam session with Tableau Zen Master Mark Jackson last week. When we asked him why he had done something in particular he replied with a comment that has been haunting me (in a good way) ever since:

“I look at this dashboard first thing every morning. I want to look at something beautiful.”

This really resonated with me. Mark was not tasked with making a public-facing dashboard that had to compete with USA Today infographics. He just wanted to make something that was both functional and beautiful. It made me think of waking up in a lovely room with crown molding. You don’t need crown molding, but as long as it isn’t blocking sunlight or clashing with the decor it’s certainly delightful to have crown molding.

This got me thinking about a topic I come back to often — how to make visualizations that are both functional and beautiful.

Unfortunately, this isn’t so easy and often leads to people sacrificing clarity for the sake of coolitude (see “Balancing Accuracy, Engagement, and Tone” and “It’s Your Data, not the Viz, That’s Boring” for some more thoughts on the matter).  I did, however, want to share a case study that had a delightful outcome and that employed a chart type that combines the accuracy of a bar chart with a bit of the “oooh” from packed bubbles and “ahhh” from donut charts.

Marist Poll and Views of the 2016 Presidential Election

Marist Poll is one of my clients and they are tasked with providing nationwide survey results to The Wall Street Journal and NBC News.  In November 2015 they conducted a poll asking people to describe in one word the tone of the 2016 presidential election. Here were the results.

Figure 1 -- Marist Poll results in tabular form

Figure 1 — Marist Poll results in tabular form

Attempt One — Word Cloud

The results from the poll are very compelling but the results as depicted in the text table don’t exactly pop.

The client tried a word cloud as shown below.

Figure 2 -- Marist Poll results using a word cloud

Figure 2 — Marist Poll results using a word cloud

I’ll admit that the graphic “pops” but it’s hard to make sense of the six terms let alone discern that the results for “Crazy” were almost three times greater than the next most popular term.

Attempt Two — Packed Bubbles

People love circles and this chart certainly looks “cool” but what does it tell other than that the “Crazy” circle is larger than the other circles?

Figure 3 -- Marist Poll results with packed bubbles

Figure 3 — Marist Poll results using packed bubbles

Why not use a simple bar chart?

Attempt Three — A Simple Bar Chart

Here are the same results rendered using the chart type Tableau’s “Show Me” suggests you use when working with this type of data.

Figure 4 -- Marist Poll results using a bar chart

Figure 4 — Marist Poll results using a bar chart

This is a big improvement over the word cloud and packed bubbles with respect to clarity — you can easily sort the responses and see how much larger “Crazy” is than the other responses.

But the chart is a bit sterile. What can we do to make the “Crazy” pop out without distorting the information?

Attempt Four — A Colored Bar Chart

The major takeaway from the poll is that 40% of the respondents characterized the election as “Crazy.” We can make that easier to glean by making that bar a bold color and all the other bars muted, as shown here.

Figure 5 -- Marist Poll results using a bar chart with one bar colored differently

Figure 5 — Marist Poll results using a bar chart with one bar colored differently

I’ll confess that this does the trick for me, but the client wanted to pursue some other options so we looked into a lollipop chart.

Attempt Five — Lollipop Chart

The lollipop chart is not native to Tableau;  it’s simply a dual axis chart that superimposes a circle chart on top of a bar chart that has very thin bars.

Figure 6 -- Marist Poll results as a lollipop chart

Figure 6 — Marist Poll results using a lollipop chart

This strikes me as an excellent compromise between the analytical integrity of the bar chart and the “ooh… circles” appeal of the packed bubbles.  I have no qualms about using this chart type.

But there’s still something if we want the chart to have some impact.

Final Attempt — Adding a Compelling Title

A concise, descriptive title can make a huge difference in garnering attention and making a chart more memorable. In the example below the client added some graphic design artistry to the typography to make the title compelling.

Figure 7 -- Marist Poll results as a lollipop chart with compelling headline.  I love this.

Figure 7 — Marist Poll results using a lollipop chart with compelling headline.  I love this.

Conclusion

My bass-playing friends will probably agree that “groove” is more important than “chops.”  That is, being able to play “in the pocket” with a rock-steady beat is more important than being able to play a great solo with a flurry of notes all over the neck.

But it sure is great to be able to do both.

The same goes for data visualization. Functionality needs to come first, then beauty.

But it sure is great to have both.

And in many cases, with a little extra effort, you can have both.

So go ahead, try putting some “crown molding” into your data visualizations and delight yourself and your stakeholders.