One of R's big benefits is the huge amount of statistical functionality, for example numerous different quantilization algorithms. If you are working with a lo

One of R's big benefits is the huge amount of statistical functionality, for example numerous different quantilization algorithms. If you are working with a lot of heterogeneous data in R it becomes a real headache. Merging data frames seems like it should work like you think it should but if one of your sets of keys (strings) are 'factors' (what I am calling 'legacy S+ functionality', I'm sure they're useful for many algorithms), you'll end up with garbage. There's a hack you can put in your code ('options(stringsAsFactors=FALSE)') which alleviates some of this but in general aligning data I found to be a huge pain. If you're running regressions this is pretty important haven't tried sage but have heard good things. NumPy is a good alternative because it's extremely well implemented and has consistent behavior across the board. Extensibility (with Fortran, Cython/Pyrex, C/C++) is clean and easy. Never thought I'd write Fortran 77 code being born quite a few years after '77 but it's an easy way to speed up simple procedural algorithms 50x or more.