Starting 2011 with shark attacks courtesy of Julien D.
It seems like the good chart did not survive a shark attack if one were to judge from what's left of it.
It's a distorted pie chart with some kind of 3D hemispherical add-on, or it's a cross-sectional chart with the top of a sphere lopped off.
Charts of the USA Today variety do not usually feature here but this one has the aspiration to inform readers -- the chart appears on a web page that purports to correct some myths about shark attacks.
The biggest casualty of the shark attack is the ordering of the data labels. It is a brain teaser as to what criterion was used to order the pie slices: it's not the total number of accidents, nor the number of deaths, nor the death rate, nor alphabetical.
It's also unclear why the data labels were made vertical. The palette of colors is, however, typical of pie charts.
Since a death rate is usually defined as deaths / total accidents, not the other way round, even the numerical data labels is harder to read than necessary.
There are two primary questions this chart is intended to address: the prevalence of different types of shark attacks, and the death rate of each type of attack (as a proportion of reported accidents).
We try a scatter plot showing these two metrics, adding notes to point out where the interesting data is.
The Times Higher Education magazine fancied itself an arbiter of good universities and yet they appeared not to have heard of Tufte, or know why we should not use 3-D pie charts, ever.
Reader Cedric K. sent in this chart, with a note of dismay. Quick, which is most important: the pink, the blue or the green?
Something like the stacked bar chart shown below delivers the information more effectively. The section showing sub-categories can be omitted.
If, in fact, it is crucial for the readers to know each weight to the second decimal, then why not just print a data table? The beauty of just using a data table is that it can accommodate long text strings, which are needed in this case to explain clearly what the subcategories actually mean.
If one wants bells and whistles, one can add little bars to the right of the proportions to visualize the weights.
The Wall Street Journal reported that the Ritz-Carlton brand of hotels has been hit worse in the slump than other brands in the Marriott family, and has recently launched a loyalty program as a result after holding out for a long time.
The following serving of pie charts shows the occupancy rates in the past three years. In the second layer of charts, I removed the data from the chart in order to show why this chart is not self-sufficient. Without the data printed directly on the chart, it is difficult to read the individual occupancy rates; and it is even harder to figure out that the decline was worse on the Ritz-Carlton brand.
A line chart brings out the message clearly and directly.
A good example showed up in the New York Times recently of a chart that fails the self-sufficiency test that I often speak about here. First, the doctored chart (with the data removed):
And for comparison, the chart as originally printed (the chart was found only on the paper edition but not on line):
There is little doubt that the second version, with the data -- all four numbers -- printed on the chart, is much more effective, and that is why the designer thought to include them.
This shows that readers are gravitating to the data rather than the graphical constructs, and thus I consider these types of charts not self-sufficient. The graphical constructs can't stand on their own.
The choice of pie charts in a small-multiples arrangement is a mistake for this data set. While indeed in theory the winning percentage could range from 0 to 100%, in practice the winning percentages are rather narrowly dispersed (with the exception of the NFL which has a 16-game regular season).
Just quickly looking up the 2009 regular seasons: MLB teams ranged from 36% (Nationals) to 65% (Yankees); NHL ranged from 32% (Islanders) to 65% (Bruins); NBA from 21% (Sacramento) to 81% (Cleveland).
In order to judge whether 60% or 52% is a large or small number, readers need to have a sense of how teams are dispersed around those averages. A side-by-side boxplot brings this out pretty well (the data is for 2009 seasons).
The "box" in a boxplot contains the middle 50% of the teams in each league while the line inside the box depicts the median team (in terms of winning percentage).
The NBA teams showed much higher variability in winning percentages than the NHL or the MLB. The difference in average winning percentage of say, 2% or 5%, from one league to the next is not remarkable, given this fact.
(The original article did not really pertain to such a comparison so the reason for this chart is not clear.)
The Facebook privacy chart that's been circulating widely (thanks to Eronarn): a FAQ on how to read the chart sorely needed.
BBC to beam general election results on to Big Ben (thanks to Julien D.): London readers, did this happen?
Another example of an infographics poster (thanks to Daniel L.), this one concerning the use of cell phones by teenagers. Daniel said:
Check out the pie graphs under the sexting category. He's showing his
percentage in color, but leaving the rest of the pie white. Awesome!
Surefire way to get the data-ink ratio right where you want it to be.
I agree with reader Craig N.'s assessment that the "visualization" applied to this data made it even more difficult to understand than just the data table. Much of this is due to failing the self-sufficiency test. There is no graphical element on this chart that can stand on its own without the support of the data itself.
The chart depicts royalty payments to music artists after the Digital Economy Act passed in the UK. There are a few even larger bubbles at the bottom of the chart including one that is so large as to overstep the borders of the chart.
While the total responses were almost evenly split between the three choices, the bar chart drew our attention to the first bar, which is inapt.
If plotted as a pie chart, I thought, the reader would see three almost equal slices. This effect occurs because we are much less precise at determining the areas of slices than the areas of bars. Wouldn't that turn our usual advice on its head?
How the Bar Chart is Saved
The one thing that the pie chart has as a default that this bar chart doesn't is the upper bound. Everything must add up to 100% in a circle but nothing forces the lengths of the bars to add up to anything.
We save the bar chart by making the horizontal axis stretch to 100% for each bar. This new scaling makes the three bars appear almost equal in length, which is as it should be.
Another Unforgivable Pie Chart
On the very next page, Luntz threw this pie at our faces:
Make sure you read the sentence at the bottom.
It appears that he removed the largest group of responses, and then reweighted the CEO and Companies responses to add to 100%.
This procedure is always ill-advised - responders responded to the full set of choices, and if they were only given these two responses, they very well might have answered differently.
It also elevated secondary responses while dispensing with the primary response.