I never cease to be amazed at how people who work with data large enough to make tools like plyr slow assume that everyone works with data like that.
I have been using R daily for 7-8 years now and have only occasionally turned to somethig like data.table for performance reasons. "Big data" receives waaay more attention and hype than there are actual human beings working on data of that scale. I can assure you that for the vast majority of R users world wide plyr is plenty fast enough for their needs.
This pretty much perfectly illustrates my comment below, pointing out that these sorts of recommendations are entirely subjective and useless.
Many of your points are quite subjective. I could do the same thing with Matlab. For instance, I find it mind boggling that anyone could get anything done when you have to devote a separate file to every single functions. That seems incomprehensible to me. And yet, I realize that that's probably a mostly subjective thing that you get used to.
Personally, I find R's documentation excellent. When people complain about it, it's usually because they have mistaken it for a tutorial. It's not. It's documentation.
Without any data, I seriously doubt your claim that Matlab has a much larger user base. (There is considerably more activity in R on StackOverflow than in Matlab.)
Your complaint about matrices, lists and data frames is similar. Data frames exist for the same reason that there's a mean() function: a columnar data structure that holds differently data types in each column comes up so often and is considered so useful that it is built in.
pandas in Python was developed in a way that went out of its way to specifically _mimic_ these data structures because data frames are considered such a vital aspect of R.
And keep in mind that these criticisms are all coming from someone who _also_ recommended against switching...!
I use R almost exclusively and I absolutely love it. But people can get a little carried away with picking and recommending the "best" language for particular task.
If you feel very comfortable and skilled in Matlab, and you aren't finding that there are regular situations where Matlab can't do what you want, I wouldn't really advocate switching. Same goes for someone working primarily in Python.
The biggest reason I would recommend switching away from a language you're already very comfortable in would be the availability of statistical methods that aren't present in your language of choice. Statisticians tend to work in R, so much of the cutting edge work ends up in packages on CRAN.
So I wouldn't dump Matlab if you're happy with it, unless you're just looking to learn something new, out of intellectual curiosity, which is always fun.
The two methodologies can give somewhat different results, but not as often as you might think, and the differences aren't as large as you might think. In my experience, the instances where you get radically different answers from Bayesian/Frequentist methods are quite rare, and tend toward the pathological example invented purely to demonstrate the "superiority" of one over the other.
That said, sometimes one or the other are significantly more convenient or easy to apply for a given model or type of data.
The point of Gelman's reply is that the comic is actually comparing a Bayesian to an absurdly incompetent Frequentist, so there' really no conflict. No (modestly intelligent) Frequentist would mis-apply this methodology in this circumstance.
I don't see how, really. Gelman himself is a (rather famous) Bayesian, and if you read the comments, you'll see that Randall himself pops up and basically cedes Gelman's point.
I have been using R daily for 7-8 years now and have only occasionally turned to somethig like data.table for performance reasons. "Big data" receives waaay more attention and hype than there are actual human beings working on data of that scale. I can assure you that for the vast majority of R users world wide plyr is plenty fast enough for their needs.