Christian Gunning has a great example of using Rcpp to speed up a for loop in R. For his agent-based simulation, Christian needed to repeatedly call the rbinom function in a loop. (Unfortunately, you can't pass a vector to the size argument, which would have solved the problem.) Using the aaply function (from the plyr package) took about 38 sections for 10,000 simulations: aaply gives you nice concise code, but it's bit like using a hammer to crack a walnut in this case. An explicit for loop took just over a second. But rewriting the body of the loop in C++ (but still calling R's native binomial RNG, via the standard R API) reduced the time by a factor of over **50** compared to the for loop: down to 0.021 seconds. cxxfunction in the inline package makes it super simple to incorporate C++ code into your R loops (provided you know C++ of course) — see Christian's full post at the link below to see how it's done.

Life in Code: Efficient loops in R — the complexity versus speed trade-off

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