How to speed up R code: an introduction

TL;DR

This paper discusses techniques to optimize R code performance, identify bottlenecks, and leverage cluster computing to handle heavy calculations efficiently, especially in statistical applications.

Contribution

It provides practical methods for speeding up R code and introduces strategies for utilizing cluster computing resources for large-scale computations.

Findings

Identification of common bottlenecks in R code

Effective code optimization techniques demonstrated

Guidance on using clusters for intensive computations

Abstract

Most calculations performed by the average R user are unremarkable in the sense that nowadays, any computer can crush the related code in a matter of seconds. But more and more often, heavy calculations are also performed using R, something especially true in some fields such as statistics. The user then faces total execution times of his codes that are hard to work with: hours, days, even weeks. In this paper, how to reduce the total execution time of various codes will be shown and typical bottlenecks will be discussed. As a last resort, how to run your code on a cluster of computers (most workplaces have one) in order to make use of a larger processing power than the one available on an average computer will also be discussed through two examples.