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Writing Efficient R Code

4.3+
23 reviews
Intermediate

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

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4 Hours14 Videos43 Exercises
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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.

    Play Chapter Now
    Welcome!
    50 xp
    R version
    100 xp
    Benchmarking
    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. 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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    Memory allocation
    50 xp
    Why is this piece of code slow?
    50 xp
    Timings - growing a vector
    100 xp
    Timings - pre-allocation
    100 xp
    Importance of vectorizing your code
    50 xp
    Vectorized code: multiplication
    100 xp
    Vectorized code: calculating a log-sum
    100 xp
    Data frames and matrices
    50 xp
    Data frames vs. matrices
    50 xp
    Data frames and matrices - column selection
    100 xp
    Selecting a row in a data frame
    50 xp
    Row timings
    100 xp
  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.

    Play Chapter Now
    What is code profiling
    50 xp
    Profiling a function
    50 xp
    Where's the hold-up?
    50 xp
    Profvis in action
    100 xp
    Profvis: Larger example
    50 xp
    Change the data frame to a matrix
    100 xp
    Calculating row sums
    100 xp
    Use && instead of &
    100 xp
    Monopoly overview
    50 xp
  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.

    Play Chapter Now
    CPUs - why do we have more than one
    50 xp
    How many cores does this machine have?
    100 xp
    What sort of problems benefit from parallel computing?
    50 xp
    Can this loop run in parallel (1)?
    50 xp
    Can this loop run in parallel (2)?
    50 xp
    The parallel package - parApply
    50 xp
    Moving to parallel programming
    50 xp
    Moving to parApply
    100 xp
    The parallel package - parSapply
    50 xp
    Using parSapply()
    100 xp
    Timings parSapply()
    100 xp
    You can write efficient R code!
    50 xp

In the following tracks

Big Data with RR ProgrammerR Programming

Datasets

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Collaborators

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

Prerequisites

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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*4.3
from 23 reviews
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  • Susan L.
    3 months

    this is an unusual type of course and very helpful, even for a data analyst sort

  • Evgeniia A.
    3 months

    A useful course for those who have ever thought about the speed of execution of their programmes

  • Ákos Á.
    8 months

    Great course with useful best practices. Learned a lot from this

  • Dimitris L.
    10 months

    cool instructor, excellent material, clear messages. Thanks for the experience

  • Henry G.
    10 months

    Gran curso, lo disfruté mucho y aprendí un monton.

"this is an unusual type of course and very helpful, even for a data analyst sort"

Susan L.

"A useful course for those who have ever thought about the speed of execution of their programmes"

Evgeniia A.

"Great course with useful best practices. Learned a lot from this"

Ákos Á.

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