Statistical Graphics and more

That’s it for this year. Au revoir 2007!.

Stage Results	cumulative Time	Ranks
(click on the images to enlarge)

Prolog: Did anyone have Thor HUSHOVD on his list?
Stage 01: Except for 5, all arrived in the peleton
Stage 02: MC EWEN already on 3.
Stage 03: First drop outs
Stage 04: BOONEN still keeps the yellow jersey
Stage 05: Only O’GRADY can’t keep the pace of the top 10 from the Prolog
Stage 06: First 37 still in a window of one minute
Stage 07: This was the day of team T-Mobile!
Stage 08: No stage to remember …
Stage 09: Still waiting for the mountains, so we look at Sebastian JOLY …
Stage 10: MERCADO and DESSEL out of the blue?
Stage 11: LANDIS now in yellow
Stage 12: POPOVYCH’s day, now on 10. 5 withdrawls after the mountains.
Stage 13: PEREIRO SIO’s second place awards him the yellow jersey.
Stage 14: No changes within the top 17.
Stage 15: LANDIS back in yellow; two more mountain stages; down to 152
Stage 16: LANDIS passes yellow to PEREIRO, KLÖDEN the real winner in the end?
Stage 17: LANDIS back after great ride; top 3 within 30”
Stage 18: No changes, we are all waiting for the show down tomorrow
Stage 19: Everything as expected, LANDIS too strong for PEREIRO
Stage 20: Profile of a winner …

To play with the data yourself, get the Mondrian software and the dataset. Thanks goes to Sergej Potapov, who wrote the script to manage the data!

Here is an example of the so called “Sectioned Density Plot”, which was recently published in “The American Statistician” (Vol 60, No. 2, 167-174).

Using a simple histogram, maybe with an added density estimator, and/or a simple standard boxplot for group comparison does the job here. No need to “invent” a new plot, which introduces more problems as it solves any.
Actually this plot makes a good case against the use of graphics …

Haven’t we been preaching against 3-d barcharts and the like for a long time? Here is what we didn’t think of in our wildest dreams: an animated 3-d barchart!

As you might guess, this is the usage statistics from this blog over the last week. It is not hard to draw “The Good” (far less sensational)

(This statistics also tells me why my quota is used up, so I moved most of the recent stuff to another server!)

Well, the Tour de France 2006 is only a few weeks ahead, probably still time to give an update on the 2005 data.
We now have the data recompiled with drop outs (thanks to Sergej).

Interesting to see that almost half of the cyclists categorized as “sprinter” don’t make it to the Champs-Elysees.

Looking at the ranks is quite funny as well. Here’s what happens when you start as first and last …

(Certainly, David Zabriskie would have probably looked better when not having the crash in the team time trial)

For those of you who do not stop by at JunkCharts regularly, here is what John S. found:

Probably one of the best example of “featurism”; computer scientists set free with no idea of application …

We got this from Friedrich who is just finishing a sabbatical in Paris:


Michael came up with the idea of a challenge. Here’s what he wrote:

Here’s a graph you may find interesting, published yesterday
in Le Monde.
The manifest goal is to compare estimates of les manifestants at les manifestations according to the unions vs. the police throughout France. An admirable goal for a newspaper, and done with some sensitivity to graphic principles, in a style reminiscent of les Albums: color-coding key (but no scale), time-series sub-graph, numbers included for those who want/need them.

But what do we see, and is there anything we could see better by some other display or encoding of these numbers? Obviously, either the police in Paris can’t count or deliberately misrepresent things, or the unions there have enormously dilated pupils. Is the over-count ratio greater in Paris than in Marseilles? Were the police in Mont de Marsan all en cong’e? Is Dijon the place with the closest correspondence between the estimates? Presumably, they used count ~ area (or sqrt radius); should they have used a different power for better perceptual comparison? What is missing from this display? e.g, how did the turnout in Toulouse compare to that in Nante or Rennes, taking population into account?
best,
-Michael

You can download the data here. Any comments are welcome and of course, any attempt of a better visualization will be posted here!

Have fun!

---

Here are two posts:

Michael sent this graphics:

He wrote:

“Here is a simple log-log scatterplot that answers some of the questions posed
originally. On a log scale, the relation is fairly linear, with a slope of 1.19, indicating that union estimates are
on average about 154% of those by the police. I’ve labelled only those cities that have a log-residual greater than
0.15, and scaled the axes so that the line of equality is approx. 45 deg.”

Romain’s (updated) suggestion is:


(See also his comment!)

There are many stories and myths about the sinking of the Titanic.

We recently compiled the data on the lifeboats (721 passengers who entered a lifeboat) and a nice pattern came out in the fluctuation diagram of “launch sequence no.” against “class”.

(female passengers highlighted)

The “fate” of the lifeboats is listed under http://www.encyclopedia-titanica.org/lifeboats/

What can be learned from the fluctuation diagram?

• 1st class was served with the first 6 boats
• The next 4 boats were mainly 2nd class
• Boats 11-14 had many 3rd class passengers
• Boats 3 and 12 were less filled and almost no men entered these boats
• Boats 18-20 are not well filled
• …

There is more to see, when reading the story and the graphics side by side.

This time no fancy graph, but a nice example of “less can be more”. I took this month’s example from the R Graph Gallery.
Here’s “The Bad”:

Above graphics tries to depict a r by s table in a 3-d view.
Uwe’s example has several problems

• The 3-d view make a judgement of the heights almost impossible
• The chosen view point (seems to be the default) puts 2 to 3 bars into a row, which makes it even harder to read the plot
• What does the gray shading of the bars mean?
• Why use meaningless random data for the example … (if there is nothing to interpret in the data, it is hard to prove that the plot has problems in interpreting the data displayed)

“The Good” uses Bertin’s “Accident” data. It is a simple fluctuation diagram of Age vs. Vehicle which performs very well to display simple tables:

Sizes of tiles are simply proportional to the counts in the category. Patterns and trends are easy to depict, though it lacks the fancy 3-d property …

(There will be a “Statgraphics 101” on Mosaic plots and alike soon …)

There is a gallery of R graphics at http://addictedtor.free.fr/graphiques/index.php.

The nice thing of this gallery is that you can get the sources of all examples, so this can be a good starting point for your own custom graphics in R.

There is a similar page – not so sleek – at http://zoonek2.free.fr/UNIX/48_R/04.html which is a bit more instructive.

None of the pages raise the question whether the presented graphics are useful though …

Yes, it has been a while since I posted a “Good & Bad” …

But as I saw this “novel decision tree plot” on an advertisement by C&H for Paul’s R Graphics book, I got inspired again …

Now here is “The Bad”:

Let me explain, what went wrong with the R graphics:

1. A tree, which is just a special graph, consists of nodes and edges
   A full featured barchart in a leaf is certainly a doubtfull glyph for a leaf/node!
2. The size of the nodes must be read as text …
3. Side by side barcharts are among the weaker representations to display a proportion
4. The numbering of the nodes is non-standard and does not help reading the information
5. Now think of a tree with, say, 10 inner nodes and 11 leaves! How big must the plotting device be to display so much (overhead) information?

… and here is “The Good” (from Simon Urbanek’s KLIMT)

This representation is much clearer. Not to overstress Tufte, but the data-ink-ratio in the KLIMT plot is hard to beat!

I personally would prefer the next display (which only takes two key-strokes to change from the first plot!), which puts leaves on the “correct” level and has proportionally sized nodes.