A guide to speeding up R code

May 10, 2013

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

Noam Ross recently shared a very useful guide to speeding up your R code

  • Get a bigger computer (for example, renting an instance on the Amazon cloud for a few cents an hour)
  • Use parallel programming techniques
  • Using the R byte-compiler
  • Profiling and benchmarking your code
  • Using high-performance packages (like xts, for time series)
  • And lastly, rewriting your code to use more efficient constructs

One other tip that can have some great performance benefits is linking R to parallel BLAS libraries (Revolution R does this by default). For more details on how to speed up your R code read Noam's excellent guide, linked below.

Noam Ross: FasteR! HigheR! StrongeR! – A Guide to Speeding Up R Code for Busy People

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