Improving the performance of R

R has a reputation for being slow and memory inefficient, a reputation that is at least somewhat earned. These faults are largely unnoticed on a modern PC for datasets of many thousands of records, but datasets with a million records or more can push the limits of what is currently possible with consumer-grade hardware. The problem is worsened if the data have many features or if complex learning algorithms are being used.

Note

CRAN has a high performance computing task view that lists packages pushing the boundaries on what is possible in R: http://cran.r-project.org/web/views/HighPerformanceComputing.html.

Packages that extend R past the capabilities of the base package are being developed rapidly. This work comes primarily on two fronts: some packages add the capability to manage extremely large datasets by making data operations faster or by allowing the size of data to exceed the amount of available system memory; others allow R to work faster, perhaps by...