Junk Charts

Getting the Nobel wrong

Why do we pay so much attention to seemingly inconsequential details of a chart's scale, colors, labels, etc.?

Here's why. (Reader Omegatron pointed us to the FAIL blog that captured this beauty.)

Notice the messed-up horizontal scale, in particular, the failure to start the axis at 0.  The result: the tiniest difference presented as a wide gulf.

The graph, published by the Washington Post, has since been fixed.  See here.  Nevertheless, the comments left by readers lent witness to the confusion.  I copied the first bunch that mentioned the graphical display - there were plenty of for-and-against-the-prize comments, many assuming that the poll result was as lop-sided as the chart seemed to indicate.

By starting at a base of 45% (as of this reading), your graphic grossly misrepresents the results of your poll. The "no" bar is four times as big as the "yes" bar, giving the visual impression that the vote must have gone 80-20 against Obama's Nobel. On the contrary, as of now the vote is 53-47 against. Whoever produced this graphic should re-read Edward Tufte's "The Visual Display of Quantitative Information."

Posted by: threefab | October 9, 2009 8:33 AM

Someone made comment that the graph is misleading, but it really isn't if you know how to read a graph.

This type of graph highlights the difference, not the complete number of votes and is appropriate when viewing percentages.

Posted by: gconrads | October 9, 2009 8:39 AM

why is graph not drawn to scale? The vote is 51 - 49 no and the graph looks like an overwhelming number of "voters' said no

Posted by: spitts1 | October 9, 2009 8:41 AM

The graph is purposely designed to make it appear that there are a huge number of no votes and demonstrates obvious bias.

Posted by: fingersfly | October 9, 2009 8:41 AM

I'm looking at the graphic here and cannot figure out what you guys are trying to show?
The tiny slice of blue is supposed to be 50% and the huge slice of red is supposed to be 50% and the scale at the bottom is......
WHAT?

Posted by: Tomcat3 | October 9, 2009 8:53 AM

It is my understanding that the President did not "win", but was "awarded" the prize...much like a bonus given to you from your employer...deserving or not. In addition, I could not help but notice that after casting my vote, the graphic scale indicated 49% yes and 51% no; however, the graph lines seemed much more disproportionate for only a 2% spread with emphasis on “no”…

Posted by: jonbwnfd | October 9, 2009 9:40 AM

Why does the graph make the vote look so lopsided?

Posted by: subwayguy | October 9, 2009 9:48 AM

The graph is not lopsided .. the numbers are. Read the posts and you will see the posts are against the idea not for.

Posted by: T-Tom | October 9, 2009 9:53 AM 

Who designed the bar graph? The difference between the "yes" votes and the "no" votes is two percentage points, yet the "no" bar is three times as long as the "yes" bar.

In fact, "yes" is 49% and "no" is 51%: the graph is supposed to illustrate reality, not. . . well, just what DOES it illustrate?

Posted by: cmcintyr | October 9, 2009 10:02 AM

Inconsequential detail?  Big fail?  You decide.

Lining them up

Consider this simple and effective display accompanying the NYT article titled "Recession Drives Women Back to the Work Force".  This is somewhat surprising because jobs are harder to come by during a recession. 

The journalist also told us that the interpretation of this data is controversial among economists.  The trend seemed to have been a decline in participation till 2004 and then a rise since then for 25-to-34-year-old women.  If there were an up-trend, it appeared before the start of this recession.  And participation rate dropped after the 2001 recession.  So much is still under study.

The use of small multiples is a good idea, and the color coordination between the two charts is well thought out.  A minor miss is the vertical scale: for any small multiples chart, one should always use the same scale.  In this case, using the same scale will allow readers to compare the levels of participation between men and women.

The following chart plots a related data set, focusing on the difference between men and women, among those married.  As before, the participation rates are of very different levels.  The male rate has steadily declined in recent decades while the female rate rose from the 30s in the 60s to about 70% in the 90s. 

Reference: "Recession Drives Women Back to the Work Force", New York Times, September 19, 2009; Bureau of Labor Statistics.

Extra reading

The New York Fed has put up some impressive infographics, depicting the credit conditions around the country.

(I was going to praise the producers for this effort - a rarity among government sites but when I went back to the site today, I find that the graphic is loading only occasionally, and try as I might, I could not get rid of the big brown patch that obscures the West coast.  Hope you have better luck!)

As with similar maps, the unresolved problem is that the pattern is strongly dominated by the relative density of people around the country.  As we click through different maps, they all look similar.

But this map -- as do most infographics -- does well to put structure onto unwieldy, large data sets.  It acts as a table of contents to the data set.

Nick Rapp's team at AP produced this great chart on US life expectancy:

Very sensitive axis labels, putting the line labels into the chart (rather than in a legend box far away), showing only the current data, not every data point, subtle coloring to bring out the decades, etc.

I'm not sure about varying the thickness and tinge of the lines.  Is it necessary?  It hinders a bit as we try to compare the slopes of the lines.  Adding the initial data labels (for 1975) would also help us judge the net change over the period depicted.  If it is the change in life expectancy over time that is the main story, consider indicing all lines to the respective 1975 value.

Reference: "CDC says life expectancy in the US is up, deaths not", Miami Herald, Aug 19 2009. 

PS. This is really an awful title for the article.  It reads as if something bad has happened to the death rate while something good happened to the life expectancy, as if there is a paradox but the article makes clear that the death rate declined, which would make the point redundant.

Pie Cubed

Omegatron also did away with a set of cascading pie charts on Wikipedia, a particularly ineffective use of this chart type.  Whenever there are more than two or three categories, the necessary use of many colors can really make one's head spin.

Here, the cascade is being used like a log scale, to artificially elevate the small pieces, which unfortunately are also the least significant pieces of the energy pie.  There is no reason for nuclear, bio-mass, hydro and "others" to add to 100% except that the author decides to group them together.  The 41% nuclear or the 41% solar heating in the second and third charts, respectively, have no meaning in the larger context.

In deference to the original author, Omegatron's new version preserves the arbitrary three-level cascade.  He converts to stacked bar charts, which brings out the differences better.


He also sensibly exposes the original proportions rather than the arbitrary relative proportions.  For example, nuclear energy accounts for 6% of the total, not 41% of the arbitrary "others" bucket which in turn contains 14% of the total.

I'd prefer an even cleaner presentation with unstacked bar charts.  This can be done in either one chart with all eleven categories, or in two charts, as shown below.  The two-chart version assumes that the reader have two key questions: alternative energy sources as a proportion of the total, and the mix of different sources within the alternative category.

With the ordinary bar chart, many fewer colors are needed, and there is no need to print out each data point, nor a need to use guides to point to labels and data.  The trouble of the latter is its tendency to draw attention to the least important aspects of the data.

With this further example, I continue to find the Wikipedia rule to discourage text annotations on graphs bewildering.   Such a rule apparently does not apply to data labels, as can be seen here.  Of course, a graph without any labeling of categories is robbed of meaning but if labels can be saved, so should annotations!

Community outreach

Omegatron re-cycled some Wiki charts and we are happy to report that they are great improvements over the originals.  I welcome other readers to alert us when you have done your bit of community outreach, by ridding the world of chartjunk.  The email address is the name of the blog at gmail for any submissions.

Not surprisingly, the originals were pie charts.

The recycled art: (can be seen here and here)

Why are pie charts such poor tools for communications?  Think about where we can place the message, and you'll find that this chart type is far too rigid.  In a pie chart, the key resource is the relative size of the sectors, followed by the number of sectors, and sometimes the size of the total pie.  Other than those, there are little else useful in a pie chart.

The histograms used by Omegatron are much more flexible.  There can be information encoded in the height of the bars, the width of the bars, the total area, the relative distribution of bar areas, the existence and location of peaks and troughs, etc. etc.

For comparing data collected in slightly different formats, the pie charts are hopeless.  Notice that the lowest category on the left (pink) corresponds to 8 weeks or less, which would include two and a half sectors on the right plus potentially a missing sector for 4 weeks or less.  The histograms below handle this easily.

Omegatron asked for some feedback.  I think the new ones are significantly better.  A few minor points:

• Instead of coloring the background to the chart, I'd color the bars themselves into green/yellow/orange according to the trimester
• I'd put the trimester labels under the horizontal axis, close to the "week" labels 
• The charts obviously need to identify the country and year of the data (which I added).  Omegatron pointed me to an inexplicable Wiki convention of not putting text inside charts (see here).  I must disagree with this convention.  Annotations on charts are some of the most useful things.
• If these two charts are to be placed side by side for comparison, then we need to sort out the vertical scale.  It cannot be the absolute number of abortions but some kind of relative scale in proportion to the population size, or some similar metric. 
• In addition, if comparison is the point, I'd suggest an overlapping histogram with bars having no fill.  

Great work!  And I love to see more of it! 

Space and time

When it comes to space or time in graphics, old habits die hard.   When we have spatial data, the default is to put it on a map.  When we have a time series, the default is to plot time along the horizontal axis.  Sometimes, these defaults work; other times, breaking up the map or straight-time-line works better.

Thanks to a reader, I noticed that Google put up a "Flu Trends" website to help us track the flu season.  They use two main charts to plot the data, as shown below.




On the right side is the time series, showing the severity of flu cases from month to month.  There are many great things about this chart and one serious flaw.  I love the fact that they did not plot time on the horizontal axis; they realize the seasonality and they create overlapping lines.  They make good use of foreground and background; it's easy for us to compare year to year differences.

The serious flaw: no vertical scale.  This was a problem with Google Trends from day one (see my post here).  They still haven't fixed it.  Because of this, we don't know if the peak shown was 5 cases or 5000 cases.  While for Google key word searches, one can excuse them for trying to protect commercial secrets.  I would imagine that this public health data is, well, public.  Since the apparent purpose of this chart is to allow citizens to declare a flu epidemic (say, when they see the current trend depart from the historical norm), not having the scale is a huge problem.

I also disagree with shifting the months around for the Northern Hemisphere so that the peaks of the graphs are aligned towards the middle.  It is better for the peaks to appear on the left and let the order of the months conform to our expectation.  (The "peak" would be split on the sides and the chart would look like a valley, which presumably is why they did it this way.)

The charts on the left side plot the spatial data, not surprisingly on maps.  Sadly, the standard exhibited on the time-series charts is nowhere found on these maps.

First, the legend is seriously deficient.

Second, the gradation of the colors is not fine enough, or put differently, the aggregation to the state/province level is much too coarse for any interesting pattern to be seen.

This poorly aggregated map becomes a farce when applied to the U.S.  There is not much left to be said, is there? 

Reading comprehension

Note: I am in the middle of a holiday and so posting will be limited.

Andrew posted a pretty chart that caught my attention.  This is the sort of sophisticated chart that rewards careful reading. 

Below is a guide to reading the chart:

• It is a small multiples chart with the components arranged in two dimensions (income levels, and a race-religion hybrid category).  The top row is a summary of voters of all race-religion grouped by income.  Note that there is no corresponding summary column for voters of all incomes grouped by race-religion.
• Source of data: 2000 poll but applied to 2008 demographic patterns.  In other words, there is an underlying assumption that opinions have stayed stable within the demographic groups.
  
• The chart is in fact three dimensional because each map gives us the geographical (state by state) breakdown.
• It is useful to figure out the smallest unit of data: in this case, this is the percentage support of federal school vouchers by voters of a given race-religion-income-and-state category.
• The color scheme is such that red represents highest support and blue lowest support, with pink and purple in the middle
• It's almost always better to start from the aggregate (that is, the average) and then study variations along different dimensions, and this is how the chart is arranged from top to bottom
  
• On the top row, the higher income groups tended to favor vouchers more than lower income groups, with a break point around $75k; even here, the regional differences are significant, with northeast and southwest hotter for vouchers at all income levels
• As we move from row to row, we realize that the aggregate data hides many disparities.  For example, white Catholics (second row) are more likely to support vouchers regardless of income level while white non-evangelical Protestants (fourth row) are much less likely than average to support vouchers at all income levels.
• Notice that the statistician (Andrew) has carefully defined the race-religion categories to balance between collapsing subgroups that are distinct and showing too many subgroups so as to cloud the patterns.  That is why there are many more race-religion subgroups that are not shown.  The ones shown are of special interest.  Consider the white protestants, evangelical vs. non-evangelical (third and fourth rows).  If one were to fix the race, geography and income dimensions, and even fix half of the religion dimension, we still find the two subgroups to be on different ends of the spectrum relating to the voucher issue.  This is why the evangelical or not dimension has been included.
  
• The white space is interesting.  Here, the issue faced by the statistician is sparse data when one gets down to multi-dimensional subgroups.  Andrew chose to ignore all the data, which is the wise thing to do.  With so few samples, it is particularly easy to draw bad conclusions.   
• Because of the white space, we get additional information on the spatial distribution of the demographic subgroups.  The black population (at least the voters) are predominantly found in the southeast while Hispanics are in the southwest.  The subgroup of income higher than $150k is essentially all white.  Admittedly, this is a very crude read because we only have two levels (below 2% of state population and above).  Of the colored states, we cannot differentiate between densely populated and not.

  

Such rich graphics deserve careful reading.  Enjoy!
  

Spinning multi-color 2

Here are two more versions of the greenhouse gas chart.

The first one is a Marimekko which many would consider to be appropriate for this type of data.  It is essentially a stacked bar chart where the width of the bar is scaled to the proportion of the type of gas.  Here's what one would be looking at:

Merimekkos (also called Mosaic charts) share many of the problems of pie charts.  Note the need to use multi-color, the difficulty in comparing the areas of the pieces (even worse than looking at sectors), and the difficulty in comparing across categories since the pieces float in irregular space (take for example the three pink pieces).  My rule is: avoid at all costs. (Well, like the pie chart, when the data is sufficiently simple, with very few pieces and with some outliers, these could be acceptable.)


Secondly, here is a recycled junkart chart, with all white space removed from the interior.  (Thanks to Derek for the suggestion.)

Depending on what the purpose of the chart is, one can decide what is the base for the proportions.  My version preserves equity between the two dimensions.  Anything else will require the designer to make a choice.  If, for example, the base is 100% for each type of gas emitted, then the reader could not derive from the same chart the proportion of each source of emission (across all types of gases).

Spinning multi-color

New York Times has a great pointer to the Global Warming Art website.  The author Robert Rohde wants to popularize environmental science by visualization of the data.  There are many interesting charts and well worth repeated visits.

These pie charts cry out for some re-dressing:

The pie charts, the colors, the whole works.  Most troubling is that each pie has its own sorting scheme, and because the text labels were not reproduced in the smaller pies, the reader is sent scrambling around to find the right labels.

In addition, these pie charts, as with almost every other pie chart, fail the self-sufficiency test.  Without all the data printed next to each sector, the reader is simply unable to judge the size of each sector.

Further, the aggregate data (larger pie) may not be as relevant after realizing that the smaller pies show very different patterns.  The following junkart version tries to bring out this fact by treating both dimensions (type of greenhouse gas; source of emission) equitably.

While I picked on this particular chart, I must say I support Robert's effort and wish him luck in this very well-intentioned project.

Some things never fall

It's New York City.  House prices do not fall.  Enjoy the graphic!

Click here for the interactive version

Reference: "For Housing Crisis, the End Probably Isn't Near", New York Times, April 21 2009.

PS. Memo to grammar teachers: When did they start using contractions in titles?