There are many machine learning algorithms, such as principal component analysis, that will refuse to run when faced with columns of data that have zero variance. There are multiple ways to remove these in R, some much faster than others. In this post, I introduce some such methods and demonstrate how to use the `rbenchmark` package to evaluate their performance.