Junk Charts

Reading

Here are some of my favorite links from other places:

A spatial journey illustrating a very long scale, created by the Genetic Science Learning Center (here)

Long scales are very difficult to deal with in charts; I have never been satisfied with log scales since it addresses the designer's challenge of trying to fit everything onto one page, bu does not deal with the reader's need to compare the elements accurately

Not sure how this helps but perhaps some of you will figure it out

Tommi left a comment about this conceptual chart by xkcd, which has been making the rounds.  Fits into our Light Entertainment category.

Says there is no optimal chart type.  A type that works very well for one data set may get hopelessly cluttered for another, similar data set.

From fellow bloggers (especially Jorge), a whole series of views of the U.S. unemployment figures by state over time.  Alternatives that are much more interesting to look at than the typically line chart. Jorge even found something in Excel that looks good.

Life-enabling charts

In response to my call for positive examples, reader Merle H. sent in an example of how good charts can make our lives simpler and easier.

All of us have seen the following presentation of air travel data.

Not trying to pick on Travelocity - it's the same format whether you use Expedia or any of the airline sites.  For those customers who are looking to decide what dates to travel so as to minimize their air fare, this format is very cumbersome to use.

What about this fare chart at FuncTravel.com?

As you mouse along the line chart, the average fare for each day is visible.  Clicking on a particular day will fix the departure or return dates.

So much easier, isn't it?

A few caveats, though:

• Instead of just providing the historical averages, they should consider including information on variability, such as bars that indicate the middle 50% or 75% of prices.  Also, what about a sliding control for customers to decide which period of past history the averages should use?  More recent data may be more representative.
• This particular feature appeals to the price-sensitive, date-flexible customer segment.  Not everyone will pick itineraries based on those criteria.  There is an easy fix. If some controls are available for customers to indicate other preferences, e.g. exclude all British Airways flights, include only evening flights, etc., and the chart can update itself based on such selections, then the chart becomes a lot more flexible, and useful to many more customers.
• As with many automatically generated charts, the chosen labels on the vertical axis are laughable.  That should be relatively easy to fix, you'd think.
  

A great start.  I happen to notice that Travelocity has a beta feature that shows a similar chart.  A revolution in how travel sites present data to us is long overdue. 

Following one's nose 1

Andrew Gelman has a great post about a so-called Immigrant paradox here, which should be interesting to our readers too.

He posed a set of sharp questions.  My read, in reverse order:

6. The graph is pretty effective, I agree.  This is known as an "interaction plot".  The message the authors were trying to send was that the gap between immigrants and U.S. born in terms of prevalence of mental illness is not constant across sub-groups of Latinos.  For example, the gap for Mexicans (light blue) is larger than the gap for Puerto Ricans (pink).  Thus, the authors concluded that one should be careful about speaking of an aggregate (average) gap.

The graph lays this out clearly.  The steeper the line, the bigger the gap between the  immigrants and non-immigrants.

When Andrew showed this, I knew for sure someone will cry foul that a line is drawn between unrelated, discrete things.  Indeed, the very first commenter weighed in with this complaint.  In fact, whenever I show such charts to non-statisticians, a lot of people have this reaction.

So I'll take this as another chance to convince you to release interaction plots from jail.

Typically, a dissenter will offer up a dot plot as an alternative.  So let's look at the same chart without the lines.  Since the reader is supposed to figure out how the gap between U.S. born and immigrant groups across different subgroups of Latinos, the proverbial nose is tracing a line from a left dot to a right dot.  Thus, to follow one's nose is to mentally draw the lines I just removed.  The chart designer has done us a favor by making the lines explicit.

In addition, as Andrew pointed out, it is always better to try to get rid of the legend and put the line labels directly onto the chart.

One shortcoming of the interaction plot is that it does not disclose the relative importance of the different lines, which correspond to the relative proportions of people in these subgroups.  Without this information, the reader will likely assume the lines have equal weight.  This assumption, as I will explain in a future post, may be a problem.

This post dealt with the graphical aspect.  I will have more to say about Andrew's other points on the statistics in a future post.

A tale of four charts

Speaking of rules for making charts, I think the most important is "if at first you don't succeed, try, try and try again."  It's absolutely essential to produce multiple looks before settling on the one that helps tell the story.

While researching U.S. consumer credit this week, I came across these four views of presumably the same data set.

The chart on the top left (via Business Insider) shows a downward sloping line, with a steep decline on the right edge of the chart.  The authors clearly wanted to show us that consumer credit in the U.S. was collapsing.  The bottom was falling out.  To aid in this effort, they chose to:

• start the plot at the peak of the time series (2000);
• set the minimum value of the vertical scale to coincide with the lowest value attained thus far (2009) -- we have reached rock bottom, ladies and gentlemen; and
• pay no particular attention to the 0% line, which is placed in the lower half of the chart, rather than the middle, thus obscuring the fact that credit grew at faster rates during the height of the boom than it has been declining during the recession.

(Thanks to Excel's default setting, we are treated to a centipede effect on the horizontal axis.  Also thick lines and line shadows.)

Readers who previously complained about my willingness to draw a line through things that shouldn't be connected will be none too amused by the use of a line chart to plot year-on-year changes.  Thus, the zig-zagging of the line represented the change of change, which has been dubbed the "second derivative" during a period when optimists used technical wizardry to show us "green shoots".  To read this chart properly, one should focus on the actual annual decline (the dots) rather than the line.

If one takes a longer-term view, as in the chart on the top right (via Rolfe Winkler), the recent drop in consumer credit can be put in perspective.  Since the 1960s, consumer credit has been positively exploding with few years of decline.  Note, again, that the falling line between 2000 and 2008 represented a slowing rate of growth, not a decline.  The question to ask is: after so many years of almost continuous growth, is the current correction such a big cause for alarm?

This situation of exploding growth followed by a slight correction is better visualized in the third chart (lower left, via chartingtheeconomy.com)  The author also answered a question asked by Rolfe, which is to compare the total consumer credit to the population, as he plotted the per-capita consumer credit.  An eyeball estimate tells us that consumer credit jumped by more than 800 percent since the 1970s, and the current retrenchment is a blip if we take a long view.  The explosion in consumer credit has no doubt enhanced wellbeing in the U.S. for decades; even though credit might well have been over-extended in the recent past, it is far from something evil.

The final chart (bottom right, via The Big Picture) should carry a health warning.  It really should not be shown by itself, or without comment.  Both charts on the right, in fact, came from the same presentation, by David Rosenberg.  This chart, not surprisingly, is the most dramatic -- the chart designer is going for the jugular.  The decline on the right side is much more exaggerated than in any of the other three.  The trick here is:

• to plot the dollar value of credit change, rather than percentage value, and thus ensuring that the further back the time, the more insignificant the change;
• to connect dots which bring out the steep decline when in fact, the steep drop reflected the second derivative; and
  
• to put the 0% line below the middle of the chart, which causes the bottom "half" of the chart to be smaller than the top "half", which plays with our perception so that we may not realize that there were multiple years of growth above the absolute level of decline recently experienced.
  

If this chart were to be believed, our focus should not be on the cliff-diving at the far right -- instead, the chart has the hallmark of a system getting completely out of control, and oscillating to oblivion.  One hopes that is not the message intended by its creator.

For anyone creating charts, it would be a great idea to have attempted all of these versions, and more.

Seth's Rules

(Via Gelman blog)

Prominent marketer Seth Godin came up with some sensible rules for making "graphs that work".  We pretty much agree with most of what he says here, unlike the last time he talked about charting.

One must recognize that he has a very specific type of chart in mind, the purpose of which is to facilitate business decisions.  And not surprisingly, he advocates simple, predictable story-telling.

His first rule: dispense with Excel and Powerpoint.  Agreed but to our dismay, there are not many alternatives out there that sit on corporate computers.  So we need a corollary: assume that Excel will unerringly pick the wrong option, whether it is the gridlines, axis labels, font sizes, colors, etc.  Spend the time to edit every single aspect of the chart!

His second rule: never show a chart for exploration or one that says nothing.  I used to call these charts that murmur but do not opine.  (See here, for example.)  This pretty much condemns the entire class of infographics as graphs that don't work.   This statement will surely drive some mad.  One of the challenges that face infographics is to bridge the gap between exploration and enlightenment, between research and insight.  As I said repeatedly, I value the immense amount of effort taken to impose structure and clarity on massive volumes of data -- but more is needed for these to jump out of the research lab.

In rules 3 and 4, Seth apparently makes a distinction between rules made to be followed and rules made to be broken.  In his view, time going left to right belongs to the former while not using circles belongs to the latter.  He gave a good example of why pictures of white teeth are preferred to pie charts, bravo.  I hope all those marketers are listening.

As readers know, I cannot agree with "don't connect unrelated events".  He's talking about using line charts only for continuous data.  This rule condemns the whole class of profile plots, including interaction charts in which statisticians routinely connect average values across discrete groupings.  The same rule has created the menace of grouped bar charts used almost exclusively to illustrate market research results (dozens to hundreds of pages of these for each study).  I'd file this under rules made to be broken!

What menace?

What menace?

What menace?

What menace?

Alright, I made my point.  If you don't work in market research, the mother lode of cross-tabs and grouped bars, consider yourself lucky.  If you do, will you start making line charts please?

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.

A second lease on life

I hinted at it in the last post, and some readers also made similar suggestions.  What happens if we plot the U.S. life expectancy data in relative terms (indiced) rather than in absolute terms?

The result is highly revealing, and that is why we should always look at the data many ways.  While in the original chart, the differences in the race/gender segments were essentially obscured by the overall slowly-growing trend, in our new chart, we took out the trend, isolating the growth rates.



The reconstructed chart showed that:

• Between 1970 and roughly 1990, blacks of both genders gained in life expectancy at a rate higher than the national average, while white females lagged behind white males
• However, almost all of the gain by blacks were attained between 1970 and 1984, and in the 10-15 years following, this excess gain was wiped out so that by 1992 or so, the black male, black female and white male lines again converged.
• Starting in 1995, black males again achieved significant improvement in life expectancy.  This time, black females did not follow their male counterparts.  Meanwhile, white females continue to lag behind.

Not being a health care specialist, I can't say what happened to the cohorts of the 1970s, the 1980s and 1995.  One thing is for sure: these insights are hard to glean from the original.

 

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

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.

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? 

Round up

Here are some interesting reading from other places:

Tag clouds have caught on since we approved them a while ago.  One interesting use was at the Life Vicarious blog.  They use it to compare the inclinations of three New York-based restaurant reviewers.  What they should have done is to remove irrelevant words like  "one", "also", "many", "make"/"made", etc.  In statistics, this is called removing "noise" which helps bring out the "signal".

Andrew Gelman discussed the NYT article that reported the finding of unexpected male bias in the children of Asian American families.  He can be counted on to make useful comments on any accompanying graphics.  He rightly pointed out that this is one example of not starting at zero: the relevant baseline is 100 since the metric is essentially the over-age of males relative to females.  I also agree that a line chart with a longer time series plotting percentages rather than over-age would work better.

The racetrack chart made an appearance at Flowing Data.  This one is even more busy and just as impossible to decipher.