Junk Charts

Progress and retrogress

Joran E. pointed to this "icky" chart he found on Clive Crooks' blog at the Atlantic. 


He ordered a "junkchart treatment", so here it comes.

First we wanted to process the triangles, dots and squares to make sense of this data.  We noted that the data came from a single year (2005) so the chart did not trace the development of the education sector over time.  But wait, it used a different route to get at the same idea.  The author compared different generations within each country to see if more and more citizens took university degrees.  So each vertical "arrow" was kind of a historical record of different generations within a country.  Under this criterion, Korea and Japan had come a long way while the US and China stagnated.

The chart is quite impossible to read as designed.  There is little reason to sort by 25-34-year-old proportion when the message concerns improvement over generations.  Besides, what about countries that apparently retrogressed?  (like Russia and Germany)

For this data, I returned to my favored bumps chart.  Here is version one.  There are two ways to read this chart: across countries, we note that most of the European states (blue) had similar profiles showing roughly a constant rate of growth.  The Asian duo of Japan and Korea (brown) had the most marked growth.  Of North America (black), Canada diverged from the US since the 35-44 generation.

Alternatively, we can focus on the change generation-over-generation.  From 55-64 to 45-54, almost all countries in this sample (except Japan) grew at the same rate.  Then between 45-54 and 35-44, the two Asian countries clearly set the pace.  The generation between 35-44 and 25-34 is most interesting: Korea has not slowed, Japan has slowed a little but still grew as fast as Canada.  A trio of European countries (Spain, Ireland, France) outpaced their neighbors.

Below I show version two.  This one combines bumps chart with small multiples.  North America, Europe and Asia/Australia are now in separate charts.  This removes clutter.

 

Small multiples re-imagineered

This chart gave me trouble.  I kept staring at it, staring.  Searching for the legend.  What could the several lines, in different colors, represent?  Take a look yourself.

Well, it turns out all three graphs were duplicates.  A different line was given dark blue to highlight a particular amusement park.

I have not seen this tactic used before.  This is like a small multiples concept except that every chart contains the same data.  Is it better than having just one chart?

Reference: "Will Disney Keep Us Amused?", New York Times, Feb 10 2008.

PS. [4/6/2008]  Here are two alternative charts contributed by our readers.  See comments below.

Derek suggested using sparklines:

Zuil reverted to basics:

Football rankings 1

The Times' sports pages made wise use of graphics in a series of NFL articles recently.  Here is a rank plot (below left) comparing Jaguars quarterback David Garrard to seven other quarterbacks who started the weekend of January 5.

Simple and effective, this chart does not fuss around in showing us where Garrard ranks relative to the others. 

The junkart revision (below right) plays with a different scale: the spacing between the tick marks represent proportional differences in the underlying metric.  This gives us a little more: for example, Garrard's second rank in completion percentage is less remarkable than first thought as he essentially tied with the 3rd and 4th best while the top six were bunched between 60 and 65 percent.

But Garrard's touchdown to interception ratio stands out as the next best quarterback attained only about half his ratio.  (Todd Collins who had not thrown an interception until that time was omitted; he also had only started four games.)

References: "Two Dreams (One Big, One Tiny) Come True", New York Times, Jan 4 2008; ESPN statistics.

Maps and dots

Happy New Year

The cosmos of university ranking got more interesting recently with the advent of the "brain map" by Wired magazine.  This new league table counts the total number of winners of five prestigious international prizes (Nobel, Fields, Lasker, Turing, Gairdner) in the past 20 years (up to 2007); and the researcher found that almost all winners were affiliated with American institutions.

As discussed before, the map is a difficult graphical object; it acts like a controlling boss.  In this brain map, the concentration of institutions in the North American land mass causes over-crowding, forcing the designer to insert guiding lines drawing our attention in myriad directions.  These lines scatter the data asunder, interfering with the primary activity of comparing universities.

The chain of dots object cannot stand by itself without an implicit structure (e.g. rows of 10).  This limitation was apparent in the hits and misses chart as well.  Sticking fat fingers on paper to count dots is frustrating.  Simple bars allow readers to compare relative strength with less effort.

In the junkart version, we ditched the map construct completely,  retaining only the east-west axis.  [For lack of space (and time), I omitted the US East Coast and Washington-St. Louis.]  With this small multiples presentation, one can better contrast institutions.

To help comprehend the row structure, I inserted thin strikes to indicate zero awards. A limitation of the ranking method is also exposed: UC-SF has a strong medical school and not surprisingly, it has received a fair share of Nobel (medicine), Lasker and Gairdner prizes; but zero Lasker and Gairdner could be due to less competitive medical schools or none at all!

Reference: "Mapping Who's Winning the Most Prestigious Prizes in Science and Technology", Wired magazine, Nov 2007.

Structuring a chart

This chart from the NYT was intended to show how the EPA has moved the bar on vehicle mileage ratings: 2008 estimates were lower than 2007 estimates across the board, regardless of manufacturer, model and city/highway.

The chart was built from one basic component, repeated for each model.  I like the discreet gridlines (the white ticks) which enable readers to count off the mileage ratings.

The data is rich: ratings were given along three dimensions (model, year of estimate and city/highway).  Readers can benefit from a stronger guidance in where to look for the most pertinent information.  As the chart stands, it is merely a container for the data.  It fails our self-sufficiency test: all the data were printed on the chart, and the bars add little.

In the junkart version, I use knowledge of the data to structure the chart. First, noting that sedans, hybrids and trucks/SUVs/minvans have different levels of mileage ratings, I clustered the models into three groups.  Secondly, the city and highway ratings were separated into two columns as I consider the between-model comparisons more important than city-highway comparisons.  The chart is a dot plot, with a vertical tick for 2007 estimates and a dot for 2008 estimates.  It's easy to see that all dots sit to the left of vertical ticks.

More subtly, we can also see that the hybrids appeared to have been penalized more.  Or perhaps, the higher the rating, the larger the downward adjustment...

Source: "Mileage Ratings Are Still Estimates, Though Closer to Reality", New York Times, Sept 16 2007.

Noisy subways

This NYC subway report is impossible to read.

However, it is very difficult to find a good way to show the information.  In fact, the data contained very little of that.  Curiously, the ratings are very dispersed so that each line is graded high on some category and low on others.  Here's one view of it:

I have grouped the subway lines together (A/C/E, 4/5/6, etc.).  The metrics are plotted left to right in the same order as in the original.  Is it all noise and no signal?

(I just realized the vertical axis is reversed: best ratings are at the bottom, worst ratings at the top.  Doesn't matter anyway since I can't see any patterns.)

Source: "No. 1 Train is Rated Highest by Commuter Advocates", New York Times, July 24 2007.

PS. Two contributions from readers.  Still looking for insight from this data...

More prevalent versus more likely

Aleks pointed to an interesting Business Week chart used to explain what people in different age groups are doing on-line.  This is a pretty chart that does an admirable job with a difficult data set.

The key to this chart, unfortunately missing, is that the percentages must be read as vertical columns to make sense.  So the top left square says 34% of "Young Teens" who answered the survey said they create web pages on-line.  In addition, the total of each column can be much more than 100% because multiple responses were allowed.

Realizing the above, we should interpret the bottom (grey) row as saying: "Older boomers" and "seniors" are more likely to be "Inactives" than younger people.  A tempting interpretation is: "Inactives" are more likely to be "seniors" and "older boomers".  But this is wrong because the chart hides the age distribution.  While 70% of "Seniors" are inactive, "Seniors" may represent a small proportion of the population, and thus they may not account for a large proportion of "Inactives".  This is the difference between prevalence and incidence rate.  (Another way to grasp this is to add the percentages across a row and try and fail to understand what the row sum could mean.)

The construct of the square grids is less damaging than it seems.  In effect, the data has been rescaled by dividing by 10.  The reader is then forced to apply "rounding".  If you are someone who sees $19.95 as $19, then you'd round down the partial rows.  If you see $19.95 as $20, you'd round up the partial rows.  So the designer has pushed you to think in terms of whole numbers between 0 and 10, in other words, in units of 10%, rather than units of 1% or, horror of horrors, 0.1% or at some other unrealistic precision.

Here's another example where the profile chart shines.  Because the percentages don't sum up to 100%, the other alternatives like stacked bar charts and "Merrimeckos"/mosaic charts don't work.  (Prior discussion of this issue here.)

This version gives a column view of the data, the lines linking percentages of each age group performing on-line activities.  The profiles nicely cluster into three groups: the younger people are more likely to say they are "joiners", "spectators" or "creators" but less likely to be "inactives".  We also see that the likelihood of being "Collectors" has little to do with age.

Source: "Inside Innovation -- In Data", Business Week, June 11 2007.

Dizzy display

 Xan G. tells us that these "inconsistent pie charts ... make [his] head hurt".  The dizzy array of colors is unfortunate, especially when "Application" gets a medium blue in three of four pies but an orange-red in one of them.  Just like the baby names charts, it's important to keep the background constant when constructing small multiples.

We cite from the horse's mouth:

The goal of this section was to uncover any [software development] task that might be overlooked [by these startup companies]. When writing a software product, the tendency is to focus 100% on the application. Items like support, marketing, and especially billing never cross your mind.

The junkart version below is designed to bring out this one message: that Blinksale has distinguished itself from the rest by having spent more time developing code for purposes other than the application itself.  

I removed the raw counts of lines of code and focused only on the relative proportions.  The former does nothing to argue the author's case.

The pie charts fail our self-sufficiency test.  The reader must rely on the data table and data labels to understand the chart.  If removed, the key message is obscured.

Source: "Web App Autopsy", ParticleTree, June 2007.

Baby names and success

While we speak of baby names, David F. nominates this set of 6 charts from WSJ.  Compare this with Wattenberg's names voyager, and the benefit of interactive graphics is immediately evident.

In David's words:

They show graphs of six different names, but the two on the bottom use a dramatically different scale (from 1st to ~20th, instead of from 1st to 1000th). The introductory text notes the difference, but it is still a shock.

We like the use of "small multiples" but their impact is compromised if we don't keep the background material constant so that readers can compare between charts.  By having  different scales, the message was distorted: Mary has had a much larger drop than David, and it's easily missed in these charts.

Lines should take the place of areas which carry scant meaning in this context.

The use of blue and red is a nice touch but dovetailing the male and female charts strikes us as excessive fun.  It would have been clearer to give the sons and the daughters their own columns.

The article itself relates the anguish of modern parents in naming their babies.  Much of this angst can be traced to serious econometric studies that claim to have found cause-and-effect relationships between someone's name and their eventual success in life.  Some of this research was highlighted in Freakonomics, for example.  My stance is that all such studies are dubious, there being innumerable confounding factors (socio-economic, genetic, cultural, luck, etc. etc.).  In addition, the measured response can range from "happiness" to income to many other metrics.  The danger of finding something because one looks hard enough is very real.  We don't currently have tools powerful enough to substantiate this sort of studies.

Source: "The Baby-Name Business", Wall Street Journal, June 22, 2007

Less is more

Derek pointed me to the style.org site which also parses political speeches.  Their preferred graphic is not the tag cloud but a labeled bar chart.

From top to bottom, each bar represents a sentence; the length of each bar is the length of each sentence.  Further, the user can specify word pairs for comparison.  Here the red bars are sentences containing the word "freedom"; the blue bars, "security".

It's a good illustration of the "small multiples" principle in constructing comparative graphics.

However, the choice of dimensions is perplexing.  I'd be much more interested in the timing of mentions of those words, rather than which sentence they appeared in.  I also find the length of each sentence to be irrelevant.

Here's one concept that brings out the point better.  It uses less space and voluntarily gives up some of the data (the sentence structure).