That's the thing that gets under my skin about R. For example, why do I need to use
do.call on a list but
apply for data frame? Not only that, the two functions have entirely different names and signatures (edit: this is a bad example, see Joshua's explanation in the comment). I find that a lot of R is just a matter of memorising what to do under which condition. You can't simply guess or deduce what to do like a sane programming language would enable. I know that many solutions are just a Google search away, but I am not comfortable with the awkwardness of the language.
Having said that, I've been using R reluctantly for a few years. I try to avoid it as much as possible. But there's just no other statistical platform that's so easy to be productive. I tried Incanter for a while but it doesn't seem that well suited for exploratory analysis as I usually end up writing functions up from scratch. More recently I played with Julia briefly. Although it is too bleeding edge as there isn't even a release version yet.
As much as I don't like R the language, the R platform and its package repertoire are incomparable at the moment. We did a bit of ggplot today at work. With the help of my coworker, it only took a few minutes to hook R with our Hadoop cluster to pull some data and produce the graphs that we wanted. In comparison, Incanter's charts are pretty too but not very customisable. D3.js is very customisable but not quick to use at all. Then there's Julia, which can't do more than a bar or line chart for now.
I haven't mentioned the other big contender, Python + Numpy + Scipy + Panda + PyPy + Matplotlib. I tried some of that too some time ago, but didn't get far with it. Come to think of it, I wrote a similar babble like this a year ago ...