Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
The beauty of R is that it is built for performing data analysis. The downside is that sometimes R can be slow, thereby obstructing our analysis. For this reason, it is essential to become familiar with the main techniques for speeding up your analysis, so you can reduce computational time and get insights as quickly as possible.
The Art of Benchmarking
-In order to make your code go faster, you need to know how long it takes to run. This chapter introduces the idea of benchmarking your code.
Fine Tuning: Efficient Base R
-R is flexible because you can often solve a single problem in many different ways. Some ways can be several orders of magnitude faster than the others. This chapter teaches you how to write fast base R code.
Diagnosing Problems: Code Profiling
-Profiling helps you locate the bottlenecks in your code. This chapter teaches you how to visualize the bottlenecks using the `profvis` package.
Turbo Charged Code: Parallel Programming
-Some problems can be solved faster using multiple cores on your machine. This chapter shows you how to write R code that runs in parallel.