Writing Efficient R Code
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Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
Course Description
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.
What You’ll Learn
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.
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.
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.
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.
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