I agree that Tableau was more impressive than I expected, however I also think mere pivot tables in Excel are under appreciated for perhaps their biggest strength—many mathematically-minded and analytical in starting positions who do not work in the IT department supporting desktop workstations. For people in these positions even Python may be considered too low level, yet, they already have Excel, and if they have the budge for Tableau they can get that too without threatening any sys admin types.

Anyway, on the topic of R, I highly suggest the Johns Hopkins data related courses on Coursera. Many of them use R as the central tool, and the three I've taken really stood out for how much the instructors reminded me of getting a private tutorial from a bright colleague on an area of their own expertise.

Based on your description, you'd probably already know most of what would be covered, but the Roger Peng class on "Computing for Data Analysis"[1] is starting again soon, and it includes an overview of R that might have some gems for you. I liked that the lectures were relatively succinct, and the assignments put it in practice.

[1] https://www.coursera.org/course/compdata

>Eventually, I hit a point with R where I was trivially doing pretty complicated things that would have been... ugly in Python.

I'm curious; can you give any examples?

There have been others, but this was a pretty good example of 1.) munging data easily 2.) having libraries available that did what I was looking for

https://gist.github.com/pschmied/5903410

I make no guarantees about the quality of my hackish code :-)