Writing Vectorized Code In R

John Mount helps us understand writing R code like a native:

This sort of difference, scalar oriented C++ being so much faster than scalar oriented R, is often distorted into “R is slow.”
This is just not the case. If we adapt the algorithm to be vectorized we get an R algorithm with performance comparable to the C++implementation!
Not all algorithms can be vectorized, but this one can, and in an incredibly simple way. The original algorithm itself (xlin_fits_R()) is a bit complicated, but the vectorized version (xlin_fits_V()) is literally derived from the earlier one by crossing out the indices. That is: in this case we can move from working over very many scalars (slow in R) to working over a small number of vectors (fast in R).

This is akin to writing set-based SQL instead of cursor-based SQL: you’re thinking in terms which make it easier for the interpreter (or optimizer, in the case of a database engine) to operate quickly over your inputs. It’s also one of a few reasons why I think learning R makes a lot of sense when you have a SQL background.

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