This is from a session I did with the UBC R Study Group. Loops can be convenient for applying the same steps to big/distributed datasets, running simulations, and writing your own resampling/bootstrapping analyses. Here are some ways to make them faster.
1. Don’t grow things in your loops.
2. Vectorize where possible. i.e. pull things out of the loop.
3. Do less work in the loop if you can.
My learning curve with the statistical software R has been a long one, but one of the steepest and most exciting times was learning how to write functions and loops. Suddenly I could do all kinds of things that used to seem impossible. Since then, I’ve learned to avoid for loops whenever possible. Why? Because doing things serially is slow. With R, you can almost always reduce a big loop to just few lines of vectorized code.
But there’s one situation where I can’t avoid the dreaded for loop. Recently, I learned how to make for loops run 100s of times faster in these situations.