As a language for statistical computing, R has always had a bias towards linear algebra, and is optimized for operations dealing in complete vectors and matrixes. This can be surprising to programmers coming to R from lower-level languages, where iterative programming (looping over the elements of a vector or matrix) is more natural and often more efficient. That's not the case with R, though: Noam Ross explains why vectorized programming in R is a good idea:

If you can express what you want to do in R in a line or two, with just a few function calls that are actually calling compiled code, it’ll be more efficient than if you write long program, with the added overhead of many function calls. This is not the case in all other languages. Often, in compiled languages, you want to stick with lots of very simple statements, because it allows the compiler to figure out the most efficient translation of the code.

Read Noam's complete article at the link below for a bunch of useful tips and tricks for writing efficient and clear code in the R langauge using vectorized programming.

Noam Ross: Vectorization in R: Why?

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