R annoyances

I guess it depends on what kind of data analysis one does. R is designed
and best suited for the analysis that starts with a data frame which
fits in 1/10th of your computer RAM. R programming is then mostly
limited to writing small convenience functions for better presentation,
visualization, etc. Or alternatively one implements a new fitting
procedure/algorithm and applies it to the data.

Now things begin to look harder when you have 200G of data and 8G of RAM
and still need to find "structure" in the data. You need to pre-process
the data, recover from *unexpected* failures, store and retrieve
intermediate data sets, etc. This requires qualities of a good
general-purpose programming language. Note, we do not use R to program a
system, we do data analysis so we should be considered R *users*.
In my view, and the experience of the colleague of my confirms it, R has
a long way to go to become a wrinkle-free general purpose language.

To your specific question, why good (C++) programmers should not
struggle with R? Because they have the skills to plan sizeable programs
in any wrinkle-free language.

Hope this makes my earier comments more clear,
Vadim