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Writing Efficient R Code

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4 hours
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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.
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  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.

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    50 xp
    R version
    100 xp
    50 xp
    Comparing read times of CSV and RDS files
    100 xp
    Operational differences: "<-" and "="
    50 xp
    Elapsed time
    100 xp
    Relative time
    50 xp
    How good is your machine?
    50 xp
    DataCamp hardware
    100 xp
    Benchmark DataCamp's machine
    100 xp
  2. 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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In the following tracks

Big DataR DeveloperR Programming


Collaborator's avatar
Tom Jeon
Collaborator's avatar
Richie Cotton


Intermediate R
Colin Gillespie HeadshotColin 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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