Junk Charts

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.

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.

Food art

Adam, who is the designer behind the Wired graphics special on "The Future of Food", asked about the rest of the series.  We previously made some comments on a set of mini donut charts.

The first thought that came to mind after browsing through all the charts was: what a great job they have done to generate interest in food data, which has no right to be entertaining.  Specifically, this is a list of things I appreciated:

• An obvious effort was undertaken to extract the most thought provoking data out of a massive amount of statistics collected by various international agencies.  There weren't any chart that is overstuffed, which is a common problem.
• It would be somewhat inappropriate to use our standard tools to critique these charts.  Clearly, the purpose of the designer was to draw readers into statistics that they might otherwise not care for.   Moreover, the Wired culture has long traded off efficiency for aesthetics, and this showed in a graph such as this, which is basically a line chart with two lines, and a lot of mysterious meaningless ornaments:
• 
  
• A nice use of a dual line chart, though.  It works because both data series share the same scale and only one vertical axis is necessary, which is very subtly annotated here.
• The maintenance of the same motifs across several charts is well done.  (See the pages on corn, beef, catfish)
  

Further suggestions:

• It would be nice if Wired would be brave enough to adopt the self-sufficiency principle, i.e. graphs should not contain a copy of the entire data set being depicted.  Otherwise, a data table would suffice.  The graphical construct should be self-sufficient.  This rule is not often followed because of "loss aversion"; there is the fear that a graph without all the data is like an orphan separated from the parents.  Since, as I noted, these graphs are mostly made for awe, there is really no need to print all the underlying data.  For instance, these "column"-type charts can stand on their own without the data (adding a scale would help).
• Not sure if sorting the categories alphabetically in the column chart is preferred to sorting by size of the category.  The side effect of sorting alphabetically is that it spreads out the long and the short chunks, which simplifies labelling and thus reading.
• Not a fan of area charts (see below).  Although it is labelled properly, it is easy at first glance to focus on the orange line rather than the orange area.  That would be a grave mistake.  The orange line actually plots the total of the two types of fish rearing, not the aquaculture component.  The chart is somewhat misleading because it is difficult to assess the growth rate of aquaculture.  Much better to plot the size of both markets as two lines (either indiced or not).
  
•  

Reference: "The Future of Food", Wired, Oct 20 2008.

Mini donuts

As a reader noted, this chart is essentially unreadable.  It contains data for the composition of diets in four countries during two time periods.

What might we want to learn from this data?

Are there major differences in diet between countries?

Within each country, are there changes in diet composition over the thirty years?

If there were changes in diet inside a country over time, did those reflect a worldwide trend or a trend specific to that country?

Unfortunately, the use of donut charts, albeit in small multiples, does not help the cause.  The added dimension of the size of the pies, used to display the total calories per person per day, serves little purpose.  Seriously, who out there is comparing the pie sizes rather than reading off the numbers in the donut holes if she wants to compare total calories?

This data set has much potential, and allows me to show, yet again, why I love "bumps charts".

Here is one take on it.  (Note that the closest data I found was for six different countries - China, Egypt, Mexico, South Africa, Philippines, India - and for different periods.)

The set of small multiples recognizes that the comparison between 1970 and 2000 is paramount to the exercise.  There is a wealth of trends that can be pulled out of these charts.  For example, the Chinese and Egyptians take in much more vegetables than the people of the other countries; in particular, the Chinese increased the consumption of vegetables drastically in those 30 years. (top row, second from left)

Or perhaps, for sugars and sweetners, consumption has increased everywhere except for South Africa.  In addition, the Chinese eat a lot less sugars than the other peoples. (top row, right)

Egg consumption also shows an interesting pattern.  In 1970, the countries had similar levels but by 2000, Mexicans and the Chinese have outpaced the other countries. (bottom row, right)

These charts are very versatile.  The example shown above is not yet ready for publication.  The designer must now decide what are the key messages, and then can use color judiciously to draw the reader's attention to the relevant parts.

Also, some may not like the default scaling of the vertical axes. That can be easily fixed.

Finally, here is another take which focuses on countries rather than food groups.  We note that too many categories of foods make it hard to separate them.

References: "Who's Eating What?", Wired, Oct 2008; "The Double burden of malnutrition", FAO, 2006.

An embarrassment

I find it embarrassing for the Economist to print an article like this one.  (Do they have a statistics editor?)

The subtitle asserting "causality" is offensive.  It is alleged that smoking bans in bars have "caused" more road accidents because people are forced to drive longer distances to find those bars that still allow smoking.

To assert causality so starkly for an undesigned observational study is unprofessional.  I doubt that the authors of the study they cited even went so far.  At best, they probably found a correlation.

Another problem is the practical significance of the finding.  There is a 13% increase in fatal accident rate in a "typical county containing 680,000 people".  There are two problems with this statement:

• When I check the Census data, there are only about 85 counties in the entire U.S. with at least 680,000 people.  What do they mean by "typical"?
• 13% is said to be an increment of 2.5 fatal accidents, presumably per year.  The crane accident in Manhattan a few weeks ago killed at least five people.  I just don't believe that one can prove definitively that such a tiny difference is not due to chance so even the correlation, let alone the causality, is suspect.

It appears that the paper is locked up in pre-publication.  If you have seen it, let us know if the authors actually asserted causality.

Reference: "Unlucky Strikes", The Economist, April 3 2008.

Mid-week entertainment: Pity grapefruit

Courtesy of Derek.  Hope for the scatter plot?

Original link here. 

Read fast, pay the price

At first, this looks like a decent chart despite the donut construct, which I cannot stand (but the Economist loves).

The accompanying text proclaimed: "Rock stars are famous for excess, and some pay the price".  The rest of the paragraph points out drug- and alcohol-related deaths, plus deaths due to "unhealthy lifestyles", which apparently include cancer and cardiovascular disease.

There is a gaping hole between what's on the chart and what's in the text.  They just talk past each other.

• The chart invites us to compare the European experience to the American experience. Each donut presents the proportion of total deaths by causes of death. The top donut presents American rock-star deaths, the bottom European ones. But this comparison has zilch to do with the key point, which is how rock stars are different from the rest of us.  The chart tells us nothing about the rest of us.  The 20% death by cancer would be entirely unremarkable if 20% of non-rock-star deaths also were attributed to cancer!

• We must also bear in mind that the base populations are rock stars who died young. This is a very specific demographic segment, and so the only valid point of reference are people who died young.  If we think along those lines, then among unmusical people, if they died young, what might have been the causes of death?  Drugs? Alcohol?  Accidents?  Suicide?  You bet.  I am not sure who is the authoritative source of such data but the CDC reported that among Americans aged 15-34 who died, the leading causes were "unintentional injury", suicides, homicides, cancer and heart disease.  Not much different from the above list...

• The deaths depicted in the two donuts totaled fewer than 100, and yet percentages are given to one decimal place.  This creates a false sense of precision not justified by the sample size.
• The deaths occurred over about 50 years.  It is very likely that the causes of premature death have shifted during this time span, making an aggregate analysis questionable.

Charting is much more than just aesthetics.  Some basic statistical common sense goes a long way.  This was observed long ago by Huff.

Source: "Rock stars: live fast, die young", Economist, Sept 4 2007.

Gauging the water level

This set of charts covered the back page of one of New York Times' sections this weekend.

Regular readers will share my enthusiasm for the top chart.  It makes a clear, cogent case to support the article's thesis concerning the rise of bottled water.  Various renditions of this type of chart have appeared here, for example.

Specifically, the smart use of color to cluster the line objects helps interpret the trends.  Blue sets out the two primary interests.  (It's a mystery to me why the gray lines were separated into darker and lighter hues.)

The twenty-year horizon used is another nice touch. I'd remove the gridlines although they aren't too distracting here.

Sadly, the second graphic does not meet the high standard of the first.  The biggest problem concerns the red rectangle, purportedly showing how much of the bottled water was imported.  The choice of differently-sized bottles as objects makes it impossible to gauge what proportion of the total was imported.  If the rectangle was placed over 1-litre bottles instead, it would look smaller.

Source: "A Battle Between the Bottle and the Faucet", New York Times, July 15, 2007.

Mid-week entertainment: creme fraiche

From Forsooth! on RSS News, June 2007

Dropped, just like that

Frank W. sent in a timely reminder of the start-at-zero rule.  This ad from Quaker Oats pitches the impossible: the smart consumer will never believe that cholesterol levels can be dropped just like that!  According to Frank's measurement, the column heights plunged 77% from Week 1 to Week 4 in this chart.

In fact, if the vertical axis had started from 0, then the drop would more appropriately appear to be 5%.  Now, even that would have been a miracle, in my opinion.

Thus, I would like to know what is a "point" of cholesterol, and what do they mean by "representative" drop.  I suppose they are asking me to call that number.

My previous posts (with commentary from readers) about starting at zero can be found here and here.