Subscribe now. Save 33% on DataCamp and make data science your superpower.

Offer ends in  days  hrs  mins  secs

This course is part of these tracks:

Colin Gillespie
Colin Gillespie

Assoc Prof at Newcastle University, Consultant at Jumping Rivers

Colin is the author of Efficient R Programming, published by O'Reilly media. He is an Associate Professor of Statistics at Newcastle University, UK and regularly works with Jumping Rivers to provide data science training and consultancy. He is the only person in history to move to Newcastle for better weather.

See More
  • Tom Jeon

    Tom Jeon

  • Richie Cotton

    Richie Cotton

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.

  1. 1

    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.

  2. 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.

  3. 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.

  4. 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.