By default, R uses only one core for its computation. This can slow things down, when you have to run extensive analyses (e.g. involving bootstrapping over many groups).
However, you can set up R in parallel computing mode and boost speed considerably. There are several R-packages that enable R to use multiple cores. But if you are new to the field, it can be painful to choose the right package. Here, I show how a parallel R-analysis can be set up in five easy steps.