In Ugly Real Economic Growth Chart from Singapore Statistics, an Excel training service shows an ugly chart, then proposes another equally ugly chart as an alternative. Chart Busters will describe these charts, and propose better alternatives.

Ugly Original Chart

The original chart is not too attractive, though it’s not terrible. It’s a line chart, which makes sense for time series data like this. The main problem with it is the use of several line styles to distinguish the different series. The dashed line for Financial Services isn’t too bad, but the dash-dot-dot pattern for Manufacturing is more distracting than clarifying. The effect of the patterned lines is to make the cluttered data look even more cluttered.

Ugly Alternative Chart

The alternative chart suggested in the article cited above is about twice as wide and twice as tall as the original, so data per pixel is reduced by a factor of four. This would be okay if the chart were four times as easy to read, but it isn’t: many of the chart elements are just several pixels wider than in the original. The chart has somewhat brighter colors than the original, but it’s no easier to read. It has inconsistent text: the title is huge, the Y axis labels are teeny, and at least two different font faces are mixed up within the chart (not counting the title, where a different font may be a good effect).

The data is not presented well in this alternative chart. There is still a line chart for the Total series; filling under this line doesn’t help clarify this curve as much as the author states. The Manufacturing and Financial data has changed from lines to circles with drop lines. Losing the connecting lines removes the clues that help your eyes follow each series and see the trends. The drop lines make the data points look like one-legged stick figures without any arms (actually, they are reminiscent of the denizens of Tralfamadore). The overuse of gratuitous formatting detracts from the presentation of the data.

One minor problem is removal of data labels to a legend separated from the data. Another is the Y axis lacking a marker at 0%.

Chart Busters Line Chart

The initial Chart Busters proposal is a line chart like the original, with solid lines for all data series. The chart is a little larger than the original to spread out the points, but much smaller than the alternative above. The data labels have been restored, color coded to match the associated series. The chart is still cluttered, because the data series are intertwined.

Chart Busters Panel Chart

Chart Busters decided to remove clutter in the line chart by converting it to a panel chart. The redrawn chart is taller and narrower than the first Chart Busters chart, with about the same overall area. The series do not obscure each other, yet the shared time axis allows the reader to see where the series move together and where they move separately. The darker gridline at 0% in each panel lets the reader compare positive and negative changes between the series, while the light gridlines and consistent Y axis scales help to compare the relative values of each series.

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Sounds good. How does it work?

Construct a table like the one below (the data I used is in z-chart-data.csv). This shows monthly visits for a fictitious web site, along with year to date totals, and a twelve month running total, calculated using of the previous year’s data. (The table in the original article actually computed a 13-month moving average, but I think that was an oversight.)

Here is the data in a Z-chart. Monthly along the bottom, YTD in a diagonal from lower left where it meets Monthly to upper right, and 12-Month Total above the other two until it meets YTD at the top right.

The 12-Month Total shows strong growth, but this chart only shows the second year. We don’t see that the statistics started in January of the previous year with 9 visits. Monthly looks steady, though.

Here is the next year’s data.

Here’s the corresponding Z-chart. Here we see that the chart gets its name from the shape made by the three timelines. Again, Monthly along the bottom, looking as steady as before, YTD again diagonal, and Running Total along the top.

But this Z-chart approach is not as effective as it could be. First let’s show all the data, not just a year at a time. And let’s include a 12-month moving average.

Let’s remove the cumulative (YTD) series. It really doesn’t show anything, but its upward slope, a natural consequence of an increasing value, gives the false impression that all the data is increasing. So it’s best to leave it off. If you want to show month-by-month progress towards a goal, it should be plotted in its own chart.

Now let’s replace the moving total with a moving average. This puts the calculated (smoothed) value into the same axis range as the monthly values. This prevents the large 12-month totals from dwarfing the 1-month individual values.

Here is a non-Z-chart corresponding to the first Z-chart above. Without the distraction of the YTD sloping line, and with the smoothed line and individual values on the same scale, we see that the monthly values are not steady as we first thought, but in fact they experience a bit of variation. There are two important things to note: the moving average is sloping upwards, so web site visits are increasing, and the monthly values exceed the moving average for most of the chart’s duration, also indicating increasing monthly visits.

If we adjust the second Z chart the same way, we see that the moving average is steadily decreasing, indicating fewer monthly visits. The monthly values are mixed, above and below the moving average. If there were a systematic seasonal variation we could see it here, but a cursory look does not reveal a pattern.

The adjusted charting approach doesn’t have a fancy name like “Z-chart”, but it has some advantages:

• Improved clarity: no distracting cumulative timeline makes it look like values are increasing.
• Easier understanding: smoothed and individual values on the same scale makes it easier to see trends in both, variability in the individual values, and deviation of the individual values from the moving average.

There is another advantage:

• Immediate applicability: we can show this data starting from the first month, without waiting to accumulate the first year’s data.

This chart illustrates the immediacy of the simple two-series chart: data starts at the first month.

The first year shows slow but steady growth, the second year shows faster growth, with some variability in the monthly values, and the third year shows a slow decline, with continued variability in the monthly values.

We could try to extend the Z-chart approach to the three-year period, with little success.

The YTD for the first year doesn’t show clearly, and we might notice that YTD for the second year has a slightly steeper slope than YTD for the third year. But YTD is a distraction. The 12-month moving total shows the same shape as our moving average in the previous chart, slow then fast growth, followed by a slow decline. Because of the scale, we’ve lost any insight into the variability of the monthly values.

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Recovery by Sector of S&P 500 Companies by Edouard Buning

In Bloomberg Business Week of March 8, 2010 a graph on page 17 representing a breakdown by sector of net income in 2008 and 2009 of S&P 500 companies drew my attention. At first the paired waterfall chart seemed organized, but on second thought I realized that it was in fact confusing and could tell us some more than the conclusions drawn by Business Week.

Business Week’s conclusion that financials and consumer discretionary rebounded in 2009 after the severe losses in 2008 is difficult to see in the graph although the red bars in 2008 are clearly the first thing that you see, followed by the increase in total net income from $288B in 2008 to $512B in 2009.

The data by sector and by year:

A short look at these data shows us that there are more interesting conclusions that can be drawn, but first we’ll take a look at the chart to see how we can improve it.

The problem with the published chart is that, in trying to show the addition of a sector to the net income, they placed the addition on top of the net income of the preceding sectors. As long as the addition is positive there is no interpretation problem, but in the case of the sectors financials and consumer discretionary the addition is negative. Although the addition is shown in red, it looks weird. How lucky we are they did not use a pie chart!

A vertical bar chart is already an improvement:

To improve the readability of the sector names, let’s flip the graph and reverse the order of the Y-axis:

With a little more formatting, such as adding a title and major gridlines, this panel chart is the result:

Now for the interesting conclusions:

1. It is clear that financials and consumer discretionary rebounded from a combined loss in 2008 of $147 to a combined profit of $106 in 2009.
2. But on the other hand, industrials (- $23) and energy (-$68) realized a lower net income in 2009. This is in line with the conclusion at the top of the article on page17, “But some, including Exxon Mobile, actually posted lower fourth-quarter profits than a year earlier.” In another graph (not shown in this post) we see that fourth-quarter earnings per share for Exxon Mobile dropped from $1.58 to $1.33 and for Chevron dropped from $2.58 to $ 1.58 from 2008 Q4 to 2009 Q4.
3. Telecom and utilities realized almost the same net income in both years.
4. The other sectors materials, consumer staples, technology, and health care increased their net income in 2009.

About the Author

Edouard Buning is the founder of BuningAdvies, a consulting company in the Netherlands that focuses on how to use market research results in marketing and communication actions. After a 20+ year career in market research and presenting the results of market research studies to his clients he realized that on the one hand client companies were always eager to know all ins and outs of the study, but on the other hand it often happened that the same client company did not use the recommendations nor took any actions based on the study. In many instances this was due to a lack of time, capacity, knowledge, or priority for the follow-up. BuningAdvies offers this follow-up service.

Edouard has recently started a blog on the story behind a chart, Grafiekadvies. This is the first blog in the Netherlands about charting.

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Actually, this isn’t the original chart, theirs was substantially larger. Since theirs was obviously constructed in Excel 2007, I transcribed their data and built my own. Though smaller, this chart lacks none of the features of the original.

Jimmy made some remark in his tweet about the “worst chart ever”, but I have to say, this chart is not even close to the worst ever. It may be in the bottom quartile, but we’re talking about a long, long tail.

So what’s wrong with the chart, anyway?

We’ve heard many times about how people just aren’t very good at judging angles or areas, and that makes pies ineffective for all but the simplest parts-of-a-whole displays. Donuts take this one step further, cutting out the central bit of the pie, so we’re relying on areas alone, without any help from the angles where all the wedges meet.

Of course, the donut resembles the big fat zero most viewers would give NBC as their grade.

Let’s improve this chart in steps. First, if we sort the data points, we only have to try comparing adjacent points. This also puts the most biting criticism of a sports event, “Not Enough Sports”, right up front.

Now we can put the munchkin back into the donut. We have the angles to help us judge the areas, which may or may not help.

Finally, we can convert the pie pieces into candy bars.

Now the labels are right next to the data points, not off in some distant legend, and the bars are easily ranked by length. The above chart may cause confusion with its multicolored bars, and we don’t want any viewers hunting around for the key to find meaning in the bar colors where none exists, so we use a single color for the bars. Or in this case, two colors. I’ve highlighted “Happily Watching” in a distinct color to set this category off from all the negative ones.

The above chart has two sets of labels. There is the horizontal axis at the top of the chart and the labels at the end of each bar. Is it redundant to have both in the same chart?

We can choose to leave data labels off the points and rely solely on the axis labels.

Or we can keep the data labels on the points, remove the axis, and close up the space between the chart and the title.

Which labeling option to you prefer? Axis labels, data labels, or both?

UPDATE 3 March 2010

Steve Fleming suggested in a comment below that I move all of the data labels between the category labels and the category axis. Good idea.

Similarly, the labels can be moved from the ends to the bases of the bars.

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Okay, as you move left to right, the bubbles get larger. The bubbles in the top row are larger than the rest, but except for the first bubble in the second row, the bubbles don’t change much from row to row. If not for the numerical labels, we wouldn’t notice any difference at all.

Apparently bubble area, not diameter, encodes the values; if diameters controlled size, the leftmost bubbles would practically disappear. On closer inspection, I noticed that the top row of bubbles, Income, obviously uses a different scale than the others.

Given our difficulty in judging bubble areas (or diameters), and given the unannounced scale change between Income and Family Contribution, the data may as well have been presented in a table. It has the same grid and numbers, but is smaller and lacks those distracting circles.

However, you can make much sense of the data if you make a simple line chart. Now you can see the similarities and differences in the three plans.

But I thought this was a bit simplistic, a bit incomplete. I spent ten or fifteen minutes at the Kaiser Family Foundation Health Reform Subsidy Calculator and filled out the data. When you’re making a bubble chart, you don’t need many data points, in fact, you can’t accommodate many data points. But a line chart can represent a continuum of data points.

In this chart it becomes clear that the plans are similar in the middle of the income range, but differ at the ends, not only in the costs, but also in the income levels where the plans kick in at the low end and where the plans reach maximum contribution at the high end.

If we superimpose the data from the bubble chart onto the line chart, we see that the bubble data actually shows a smaller difference between the plans at the high end than we can see in the line chart.

Nice chart and all, but I’ve neglected some details. There are no titles in the chart, so someone reading the newspaper would actually have to read the article to get the story. Also, except for USA Today, most newspapers are limited to black and white graphics. And the charts I’ve made are rather large for their data content (though smaller than the original bubble chart).

So how can we make this chart more suitable for a newspaper? Shrink it, add titles, and convert the colors to black and two shades of gray.

You could use dashed or dotted lines as well. Excel up to 2003 does poorly with thicker dashed or dotted chart lines, but Excel 2007 renders these features nicely.

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Apparently there has been much criticism of this chart. On the one hand, it is eye-catching. On the other, it does not clearly show anything without a lot of mental gymnastics. If you view it at full size, you can at least read the numeric labels in the margin, but reduced to fit into a medium sized monitor, it’s illegible.

The iGraphics Explained blog posted an attempted clarification of and justification for this beast in Data Visualisation and Quantitative Easing explained by Ciaran Hughes in the Daily Telegraph. The explanation said

On the left we can see the ‘running total’ of the value of assets purchased, against the ‘monthly total’ on the right hand side, keyed at the top of the graphic.

I think the reader is able to understand the depth of the financial stimulus package, and when.

Right. I showed this picture to an unbiased bystander (my wife, who has a PhD in the biological sciences and who now teaches math and science), and asked how well it showed monthly and cumulative spending. Her initial response: WTF!!??

As I pointed out, you can only see the running and monthly totals if the text labels are legible. A table without the pretty distraction in the middle would be an improvement, if only because the numbers are not spaced out by the nice colors.

Of course, seeing numbers in text form is not the same as truly visualizing the data. A bar chart is a major improvement over the table and over the fancy illustration at the top of this article.

If you want to see, or emphasize, the month-to-month trend, which was the intent of the original illustration (i.e., the “Easing” of government support), a line chart is the best option. You can readily see the negative slope of the Monthly data, and the decreasing slope of the Cumulative values.

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In all of its glory, here is the original 3D not-quite-exploded pie chart.

What’s Wrong?

What’s wrong with this chart? Well, it’s a pie, and we’ve covered this ineffective visualization method before. It’s rendered in 3D, which through the magic of simulated parallax distorts the apparent sizes of the data points (i.e., the wedges). Also the legend is truncated: the last three items are left off the bottom. They’re each only 0% of the total, but you can see paper-thin wedges struggling to remain upright in the pie.

In a few moments I’ll describe the larger visualization sin. But first I’ll try to duplicate this same chart.

I pasted the data into Excel, made a 3D pie, and adjusted the colors and orientation until I had gotten pretty close to the original.

I’ve managed to include all of the legend entries. But my chart doesn’t resemble the original very closely, does it? What if we explode the pie?

It still doesn’t resemble the original. When I exploded the pie, the wedges moved outwards by the same amount. The edges that were at first touching are still parallel, and the angles at the vertices of the wedges are unchanged.

The original pie, on the other hand, has its wedges still touching in the center. The gaps themselves are wedge-shaped, which decreases the angles of all of the pie’s wedges and distorts our decoding of the underlying values. I guess this is what happens when there is too much art, too much aesthetics, involved in the graphing of data.

Reconstructing the Extra Distortion

Let’s start with plain vanilla pie charts of the data, in both 2D and 3D. I’ve omitted the legend and labels for clarity.

If we do a straightforward explosion, the shapes of the wedges are unchanged. The gaps are not uniform in width, but are larger between wedges with larger values. There is also some kind of optical illusion going on in the center of at least the 2D chart.

We can recreate the appearance of the original chart by inserting an invisible wedge with value 2500 (I fiddled to get this to look about right) between every pair of adjacent wedges in the first chart, without exploding the chart.

All the gaps are the same width, and some of the real wedges are smaller than the gaps. This proves the maxim, “Less is More”. The 3D pie closely matches the appearance of the original 148Apps chart.

you can take this approach to extremes. With ultra-wide wedges, there isn’t too much differentiation between the data values.

We can take this one step further, by moving all of the gaps to the “end” of the pie.

This clearly illustrates the distortion of the fancy wedge-gapped 3D pie chart. The amount of these pies that are made up of nothing is 31%, leaving only 69% for displaying data. The largest wedge, which comprises 17% of the data, fills only 11% of the circle.

We humans are bad enough at judging areas and angles, so when the angles are distorted in this way, we’re hopeless.

Peltier Technical Services, Inc., Copyright © 2010.
 
Licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License.
 

There are a number of strange and wonderful charts on the dating site, but the one that led to this specific discussion is shown here.

The major controversy it that the horizontal X axis is not located at zero on the Y axis, but at the average effectiveness of 27%. A second issue is that vertical bars pretty much force category labels with hard-to-read orientations. If these are really a problem, then a horizontal bar chart with the bars starting at zero solves it, as Kaiser demonstrates.

If you are interested in relative effectiveness of each photo category, then the zero axis origin may not be so important. Also, the resolution of this chart is better than if the bars had to start at zero.

This is an improvement in terms of legibility of labels and judging the absolute values of the bars. Perhaps a dot plot is equally effective?

Unlike a bar chart, starting the value axis at a value greater than zero poses no cognitive issues. We can then expand the scale, to gain back the resolution of the original chart.

What do you think of these alternatives?

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Q: What’s worse than comparing categories within a pie chart?
A: Comparing categories between 2 pie charts.

I will use an article Jeff cited to show a better way to make comparisons.

The Analysis

Jeff pointed to Why are men more affected by labour market downturns?, a study by the NZ Department of Labour. The article stated that of the 34,000 jobs lost by the NZ economy in the eight quarters ending September 2009, 80% were lost by men.

This disparity in job losses can be seen through an analysis of the jobs held by men and women. Many more men hold jobs in manufacturing and construction, sectors which are hard hit during recessions. Women on the other hand are more likely to be employed in fields such as education and health, which are largely government funded and tend to be less sensitive to economic conditions.

The Visualization

This is a well thought out analysis, but as Jeff pointed out, comparing wedge sizes in two different pie charts is not an effective way to support the argument. Their two original pie charts were huge, and not even posted on the same page, though they did show barely legible thumbnails adjacent to the text of the article. The following two charts show the breakdown of jobs by men and by women. I’ve enlarged the pit thumbnails somewhat, to improve legibility and to compare with an alternate display I’ve created below using Dot Plots.

Seasonally adjusted male employment by industry September 2009 quarter

Seasonally adjusted female employment by industry September 2009 quarter

The Alternative Visualization

I’ve made two dot plots to show the same data. Within a couple pixels, the Dot Plots (including title) are the same size as these pie charts (without title), but each Dot Plot shows twice as much information, the Dot Plots have much more legible text, and the comparisons are much easier to make within a Dot Plot than between multiple pie charts.

It would be so easy to make a few simple annotations on one of these dot plots to drive home the point of the analysis.

Dot Plots

Dot Plots have been developed as a more effective tool for categorical comparisons than bar charts, pie charts, or (gasp!) donut charts. You can read about them in several places:

• Good Graphs for Better Business by William S. Cleveland and N.I. Fisher
• Dot Plots: A Useful Alternative to Bar Charts by Naomi Robbins
• Some Comments on Dot Plots by Naomi Robbins (a guest post on this blog)
• Compare Metrics by Category Using Excel Dot Plot Charts by Charley Kyd
• Excel Dot Plots by Kelly O’Day
• Dot Plots on this web site

The last three links show various techniques for creating Dot Plots in Excel. Excel doesn’t make it easy to create Dot Plots: you need to use one helper series technique or another to generate the text labels along the vertical axis. The easiest approach is probably to use a horizontal bar chart with hidden bars to supply the labels, and an XY chart to supply the data points.

To simplify the creation of Dot Plots in Microsoft Excel, I’ve developed a utility that generates a dot plot in a simple button click. The utility is a standard Excel add-in, and it adds a Dot Plot button to the Excel menu (versions 2000 through 2003) or to the Excel ribbon (versions 2007 and later). The button pops up a simple dialog for the user to select formatting options.

Excel detects a preselected data range, and remembers the Chart Lines options from the previous time you used the utility. It produces a very simple Dot Plot adjacent to the data range.

Two minutes of formatting produces charts like those I used above to support the findings of the NZ Department of Labour.

The PTS Dot Plot Utility is available from the Peltier Tech web site for a nominal fee. It can be downloaded as soon as the purchase is approved, it installs itself using the downloaded setup file, and the button appears the next time Excel is started.

Click on the icon to the left or visit PTS Dot Plot Utility to check it out.

To learn about Peltier Tech’s other utilities, visit PTS Excel Charting Utilities.

Peltier Technical Services, Inc., Copyright © 2010.
 
Licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License.
 

I missed National Pie Day, but while reading The Real Reason Outsourcing Continues To Fail, I encountered a pie chart that gave me indigestion. It purports to show problems with inter-cultural relationships that lead to outsourcing failures.

What’s wrong with the chart?

What’s wrong with this pretty chart, you may ask (or you may already have a good idea). Where to begin…

It’s a 3D pie chart, and we’re looking at it almost end on, so the sizes and angles are all distorted. The values are unsorted, so position around the pie doesn’t help determine relative percentages. The legend is ordered by row instead of by column, so you have to go back and forth between the pie and the legend, then back and forth between the two columns of legend entries, then if you remember, back to the pie.

When I visited the original report that contained this chart, What Causes Outsourcing Failures?, the reason for the unsorted values became clear. Figure 1, earlier in the report, showed its slices sorted by value. Because of the severe angle you can only tell the chart is sorted because the data labels are in numerical order.

Placing “Other” in the middle of the data is unorthodox. Generally “Other” is placed at the end, because typically it’s comprised of several very small values.

Figure 3 used the same order as Figure 1, except that “Other” was placed at the end where it belongs. This consistency is not a bad thing, but it’s dwarfed by the other problems with the charts. There is another pie chart, Figure 2, which shows Least Frequent Causes of Outsourcing Failure. I’ll spare a kitten and not repost it here.

How can we make these charts better?

Let’s dispense with the 3D distortion. The circular arrangement also does not aid in understanding, especially in the unsorted Figure 3. Let’s remove the legend and put the labels right next to the points. This way, we can reduce the number of colors (which I haven’t mentioned, since it often boils down to a matter of taste).

Well, that sounds to me like a bar chart, so here are bar charts of the two pies above, Figure 1 above and Figure 3 below.

Easy to read, easy to correlate label with data point, easy to see a strong variation in Figure 1 but not in Figure 3. But they take up a bit of room, don’t they? Let’s see how they look in a panel chart.

That’s good to compare the general shapes of the trends. If we’re concerned with the specific values of each category, we can even plot the data in the same space with a dot plot.

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