Chart Crunch Continues: say goodbye to pie charts

The Bhutanese newspaper has been presenting front-page articles on the Rupee crunch, presenting different figures to tell a story. They are to be commended for using charts to present the data. Yet, there is much to be desired in terms of the choice of charts. Yesterday's front-page article "How private consumption and credit caused the Rupee crisis" featured the following chart for showing the different sources of private consumption Rupee usage:

From www.thebhutanese.bt (May 12, 2012 issue)

In an earlier post, we discussed why pie charts are ineffective for presenting data. We also showed that a bar chart is a much better tool for presenting counts and percentages. This particular pie chart suffers from an additional ailment: it uses 3D. In other words, instead of thin pizza slices we're looking at thick cake slices! While this might build up an appetite, note that the extra dimension does not represent any information. Even worse, it distorts our perception of size. You can see this by trying to figure out which consumption item contributes the most? While the purple slice in the front looks largest, it in fact represents the second largest item! (can you find the first?). If an important decision (such as a ban) would be made based on this misperception, it would be quite tragic.

Other questions are also difficult and time-consuming to answer with a pie chart. For example:

• Which consumption item contributes the least?
• How much does Transport contribute? How does this compare to Clothing & Footwear?

Now let's look at an effective chart for conveying the same information. It is not fancy or colorful, but it doesn't require much thinking as the facts just "pop out":

Sources of private consumption and credit

We see that the first two items are similar in their contributions and high compared to the others, then a drop to 10% for Clothing & Footwear, and lastly the other items contribute between 2%-6% each, with Alcoholic Beverages, Tobacco & Narcotics contributing the least.

The bottom line: forget pie charts and forget 3D. Counts and percentages are always best to convey with simple bar charts (you can find a few more examples on this page -- click on a pie chart to see a better bar chart alternative).

Using charts in the media is very important. It catches the reader's eye and can help summarize the story in one look ("a picture is worth a thousand words"). However, it is crucial that chart creators acquire the basic knowledge in creating effective charts. It's not rocket science, yet it makes a huge difference.

Keep the pie for birthdays

Today's Kuensel's front page article reported the 2010 land cover assessment statistics. These statistics are important: according to the Kuensel article, they are "used for planning and monitoring of land based resources by agencies like the GNHC and NSB".

Charts are excellent for communicating such statistics. However, creating effective graphs is not simple. The Kuensel chart showing the land coverage by type of land looks like this:

Chart from Kuensel newspaper, June 8, 2011

What exactly can we learn from this graph? Clearly there is one big blue slice that with some effort we can map to "Forest". But what about the others? Try taking this quiz:

1. What is the green slice? (you have 3 seconds)
2. Can you quickly compare Meadows with Snow Cover?
3. What do the numbers mean?
4. What is the third largest type of land?
5. What is the smallest type of land?

Let's see a more effective plot using the same data (and the same software -- I am using Microsoft Excel, the same software used to produce the Kuensel graph):

A more effective chart of the same data (using the same software)

Now try that quiz again! Of course, there is no green slice anymore. We can easily see how "Forest" is by far the largest, we can also easily see that "Snow cover" is the third largest. And Non-built up areas is the smallest. We can also more easily compare the different types of land to each other.

Here are a few guidelines for creating an effective plot for percentages:

1. Avoid pie charts! They are known to be ineffective communicators. Bar charts almost always convey the information in a clearer and less misleading way
2. Include informative labels: the Kuensel chart does not have any title, no % sign (maybe those numbers convey squared km?)
3. Avoid 3D charts -- in this case the third dimension is only confusing.

Creating effective charts is an important skill, especially in journalism. Charts should be effective, not "artistic".

Here's a cool site by Stephen Few that shows examples of poor charts. Click on each to see a quick analysis and an example of a good chart for the same data. An excellent book by the same person is Show me the Numbers. You can view part of the book using Amazon's Look Inside, and you are welcome to come and browse my copy.

A final challenge: can you create a better chart for each of the two charts from today's article "Cash in banks come from corporations" shown below? (hint: use the 3 tips and think "effective", not "artistic").

How to convey the information from this pie chart more effectively?

How to improve this bar chart so that readers more easily grasp the story?

Workshop: Decision Making Using Excel, June 1-3

Prof. Galit Shmueli will be conducting a 3-day workshop on "Decision Making Using Excel". The workshop is intended for decision makers in government, corporate and private organisations in Bhutan, as well as for entrepreneurs and those planning to start new businesses. Attendees will gain knowledge on how Microsoft Excel can be used effectively for evaluating projects and supporting decision making. 

The three-day workshop will take place on June 1-3 at the new Rigsum Institute campus (behind Hotel Pedling).

For more information and for online registration please visit http://www.rigsum-it.com/workshops/decision-making-using-excel-may2011.

Prof Shmueli with graduates of the Oct 2010 workshop

Workshop: Decision Making Using Excel

Prof. Galit Shmueli will be conducting a 3-day workshop on "Decision Making Using Excel". The workshop is intended for decision makers in government, corporate and private organisations in Bhutan. Attendees will gain knowledge on how Excel can be used effectively for supporting decision making.

The three-day workshop will take place on October 20-22 at the new Rigsum Institute campus (behind Hotel Pedling).

For more information and for online registration please visit http://www.rigsum-it.com/workshops/decision-making-using-excel.

Prof. Shmueli and graduates of the June 2009 workshop