Efficient R Programming: A Practical Guide to Smarter Programming

by Colin Gillespie, Robin Lovelace

There are many excellent R resources for visualization, data science, and package development. Hundreds of scattered vignettes, web pages, and forums explain how to use R in particular domains. But little has been written on how to simply make R work effectively--until now. This hands-on book teaches novices and experienced R users how to write efficient R code.

Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics--from optimizing the set-up of RStudio to leveraging C++--that make this book a useful addition to any R user's bookshelf. Academics, business users, and programmers from a wide range of backgrounds stand to benefit from the guidance in Efficient R Programming.


Get advice for setting up an R programming environment
Explore general programming concepts and R coding techniques
Understand the ingredients of an efficient R workflow
Learn how to efficiently read and write data in R
Dive into data carpentry--the vital skill for cleaning raw data
Optimize your code with profiling, standard tricks, and other methods
Determine your hardware capabilities for handling R computation
Maximize the benefits of collaborative R programming
Accelerate your transition from R hacker to R programmer

Genres: Programming

356 pages, Kindle Edition

First published December 8, 2016

Reviews

[Vysloczil]

Somewhere between Advanced R, R4DS, and the Art of R Programming (did I forget another?) I believe this deserves a spot on the bookshelf of a regular R user. Or, as the price tag is quite heavy, at least a bookmark of the free ebook version, which is regularly updated it seems: https://csgillespie.github.io/efficie....
Outside of R the book gives structural tips on programming in general (structure, style, version control, etc.) and also gives advice and hardware and the respective bottlenecks to keep in mind.

[Lilli]

Really useful and important read for R programmers.

[Jiaxing Li]

Great book, very helpful!
Makes me thinking more not only the code to run, but how to make it efficiently running.

[José]

Saving time is very important in (big) data science and Colin Gillespie makes a fair point in refering it throughout the book.

Luckily, the book is not only the representation of the author to identify this recurring problem, it is also filled with suggestions on how to speed up your coming across different steps - from the actual typing of the keys to the optimization, featuring two very interesting chapters on sharing your code and making it available and on how to correctly (and effectively) learn how to use different and search for new packages and functions.

[Emil O. W. Kirkegaard]

Basically a worse version of content covered in Advanced R, and R for data science (Wickham's books).

[James Igoe]

Simply a great book, chock full of tips and techniques for improving one's work with R.