Writing Efficient R Code

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
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4 Hours14 Videos43 Exercises29,818 Learners
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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

    Free
    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.
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  2. 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.
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  3. 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.
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  4. 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.
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In the following tracks
Big DataR ProgrammerR Programming
Collaborators
Tom JeonRichie Cotton
Prerequisites
Intermediate R
Colin Gillespie Headshot

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.
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