If you are responding to my comment, I agree with you. I like the R language very much and use it. I may not have said what I meant well enough. What I mean is that there is a tendency with R programmers and much more so with SAS or Stata not to think about the guts of the program and what is really happening. The data frame in R is a nice framework for thinking and programming, but it does not promote thinking about whether i/o is to disk or memory, or whether iterations or loops or recursion is efficient, or overall program efficiency, reuse etc. Programmers tend to rely on the underlying system to worry about memory allocation disk i/o etc. There are many problems where these details matter. So much time is spent on the statistical problem and what procedure or function to call, rather than the algorithm at a deeper level. These details can be handled, but are not so "front and center". Matlab and others similarly. I have seen enterprise-class servers brought to their knees by a poorly written R program... ( you can say lots of things about how that should not be possible, or how that can happen with other languages, and I'd agree, but I still think it is more likely in a context that does not encourage programmers to think "closer to the metal" about where data is and what is happening to it. )

I've seen some great R code too... so this is a complicated topic and has to do with who is writing the code as much as R itself.