Best way to visualize attrition using R? Thru this site I've recently discovered Sankey Diagrams, a great way to visualize what is happening in a traditional flow chart.
Here is a good example of a Sankey Diagram by George M. Whitesides and George W. Crabtree,

Source; Don't Forget Long-Term Fundamental Research in Energy, Science 9 February 2007:Vol. 315. no. 5813, pp. 796 - 798.
After I realized that there was no Sankey R-package I found an R script online, unfortunately this script is quite raw and somewhat limited. With high hopes I asked for a Sankey R-package or a more mature function at stackoverflow, but to my surprise it seems as we do not have a mature function for building Sankey Diagrams in R.
After I posted a bounty Geek On Acid was kind enough to suggest a small hack on the existing script which made it work more or less for my specific purpose.
The improved R-script produced this diagram,
 
Source; stackoverflow.com.
But, does the lack of a R package indicate that Sankey Diagrams isn't such an amazing way to visualize attrition using R in a data flow à la the one presented in the diagram above (see initial stackoverflow question for data and R code. Maybe there's a better way to visualize attrition.
What do you think is the best way to visualize attrition in a data flow using R?
 A: I agree with @gung. The Sankey diagram you posted is, I think, a pretty good example of where the technique can help. While it is complicated, the context (energy input and output) is complex too and it is hard to think of a nicer way of visualizing the paths of inputs-to-outputs-acting-as-new-inputs across multiple categories of usage. 
Now then, for the attrition example you posted, as others have noted it is not helpful to use a Sankey diagram. I think you need to post your full set of variables if you want a good recommendation on alternative visualizations though. If you simply want to show differences in attrition sources between sites and clinicians, a small-multiples series of dot plots may be the easiest for your audience to understand and for you to implement (see this example, where in your case the groups could be the sites, the elements within the groups would be the causes of attrition, and the horizontal axis would be 0-100%). 
If the Sankey diagram is something you want to use, and you are willing to dabble in another high level language, there is a nice example (with code) on the gallery for the Python plotting package, matplotlib.
A: I wouldn't necessarily assume the lack of a method implies that method is unimportant or not useful.  After all, for all the methods that currently exist in R, there was a time (quite possibly recent--R is only ~10 years old) when there was no package for it.  
However, I should think there are any number of ways to visualize data such as attrition.  My first thought looking at your chart, is that it could be represented with a dot plot.  Other possibilities exist as well.  The extra functionality of the Sankey Diagram is going to come into play when you have some attrition due to a particular cause at one point, and then more due to the same cause later with other inputs and outputs in between.  That would be more complicated to represent by standard plots (it's also harder to follow even with a Sankey diagram--for example, the one at the top of the page takes quite a bit of work to read).  Since you don't seem to have that, the Sankey diagram seems to be pretty, but overkill.
A: How about using R code to write an SVG file with the arrow widths set according to your data, and a simple layout. Then load into Inkscape and bend the arrows around, add labels etc etc to your heart's content to make something pretty.
Obvious problem: you need to redo all your prettification in Inkscape if your data changes (although you might be able to use your pretty SVG from Inkscape as a template and just substitute new arrow widths in).
But honestly, if that multi-coloured mess of straggling squiggles at the top is a good Sankey diagram, I'd hate to see a bad one on a full stomach [although staring at it for a few more minutes has given me a clue about what it's about, a good graphic shouldn't need that].
