Defending boxplots

Andrew Gelman thinks boxplots are horrible (he prefers horizontally-jittered dot plots). Some of his commenters leap to their defense, leading Andrew to issue this challenge:

I have never ever seen an example where I've felt a boxplot was appropriate. I'm open to being convinced, but I don't think you'll be able to convince me. Bring on the examples!

Personally, I think boxplots are especially useful when you need to compare multiple distributions: looking at changes in trend or variability over time, or between many samples. (Note the plural though: you'd probably prefer a histogram to a single boxplot.) It's a lot easier to compare the five statistics and look at the outliers than it is to try and see the differences in dozens of histograms lined up side-by-side -- if you can even fit them all on the page.

I think this example of microarray data from one of the commenters (which, I'd add, was created in R) is a great use of boxplots. Anyone got any other great examples in defense of boxplots?

Comments

[Paolo]

David, I agree with you. Recently I found that the beanplot (implemented in the beanplot package) is a good alternative to the traditional boxplot in comparing univariate data. Take a look here:
http://cran.r-project.org/web/packages/beanplot/vignettes/beanplot.pdf

[Loyal]

I was going to suggest the violinplot (from vioplot or UsingR packages). But the beanplot looks like an even better implementation. Boxplots are useful, but if you can have the boxplot layout with more appropriate information about the distributions, why not?