EFFICIENT R PROGRAMMING A PRACTICAL GUIDE TO SMARTER PROGRAMMING

by Colin Gillespie

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.

Genres: Programming, Nonfiction, Computer Science

220 pages, Paperback

First published December 8, 2016

Reviews

[Vysloczil · Rating 3 out of 5]

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 · Rating 4 out of 5]

Really useful and important read for R programmers.

[Jiaxing Li · Rating 5 out of 5]

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

[José · Rating 4 out of 5]

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 · Rating 3 out of 5]

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

[James Igoe · Rating 5 out of 5]

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