During the last few decades, demand for computing power has steadily increased as the data volume has become larger and models have become more complex. It is obvious that minimizing the time needed for these calculations has become an important task and that there are obvious performance problems that need to be tackled. These performance problems arise from a mismatch between data volume and existing analytical methods. Eventually, a fundamental shift in data analysis techniques will be required, but for now, we must settle with improving the efficiency of our implementations.

R was designed as an interpreted language with a high-level expressiveness, and that's one of the reasons why it lacks much of the fine-grained control and basic constructs to support highly-performant code. As Arora nails it in the book, she edited...