Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency. There are many excellent R resources about topics such as visualization (e.g., Chang 2012), data science (e.g., Grolemund and Wickham 2016), and package development (e.g., Wickham 2015). There are even more resources on how to use R in particular domains, including Bayesian statistics, machine learning, and geographic information systems. However, there are very few unified resources on how to simply make R work effectively. Hints, tips, and decades of community knowledge on the subject are scattered across hundreds of internet pages, email threads, and discussion forums, making it challenging for R users to understand how to write efficient code.
In our teaching we have found that this issue applies to beginners and experienced users alike. Whether it’s a question of understanding how to use R’s vector objects to avoid for loops, knowing how to set up your .Rprofile and .Renviron files, or the ability to harness R’s excellent C++ interface to do the heavy lifting, the concept of efficiency is key. The book aims to distill tips, warnings, and tricks of the trade into a single, cohesive whole that provides a useful resource to R programmers of all stripes for years to come.
The content of the book reflects the questions that our students from a range of disciplines, skill levels, and industries have asked over the years to make their R work faster. How to set up my system optimally for R programming work? How can one apply general principles from computer science (such as do not repeat yourself, aka DRY) to the specifics of an R script? How can R code be incorporated into an efficient workflow, including project inception, collaboration, and write-up? And how can one quickly learn how to use new packages and functions?
The book answers these questions and more in 10 self-contained chapters. Each chapter starts with the basics and gets progressively more advanced, so there is something for everyone in each one. While more advanced topics such as parallel programming and C++ may not be immediately relevant to R beginners, the book helps to navigate R’s infamously steep learning curve with a commitment to starting slow and building on strong foundations. Thus even experienced R users are likely to find previously hidden gems of advice. While teaching this material, we commonly hear “Why didn’t anyone tell me that before?”
Efficient programming should not be seen as an optional extra, and the importance of efficiency grows with the size of projects and datasets. In fact, this book was devised while teaching a course called R for Big Data, when it quickly became apparent that if you want to work with large datasets, your code must work efficiently. Even with small datasets, efficient code that is both fast to write and fast to run is a vital component of successful R projects. We found that the concept of efficient programming is important in all branches of the R community. Whether you are a sporadic user of R (e.g., for its unbeatable range of statistical packages), looking to develop a package, or working on a large collaborative project in which efficiency is mission-critical, code efficiency will have a major impact on your productivity.
Ultimately, efficiency is about getting more output for less work input. To take the analogy of a car, would you rather drive 1,000 km on a single tank (or a single charge of batteries) or refuel a heavy, clunky, ugly car every 50 km? Or would you prefer to choose an altogether more efficient vehicle and cycle? In the same way, efficient R code is better than inefficient R code in almost every way: it is easier to read, write, run, share, and maintain. This book cannot provide all the answers about how to produce such code, but it certainly can provide ideas, example code, and tips to make a start in the right direction of travel.
Conventions Used in This Book
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Using Code Examples
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This book was written in the open, and many people contributed pull requests to fix minor problems. Special thanks goes to O’Reilly who allowed this process and everyone who contributed via GitHub: @Delvis, @richelbilderbeek, @adamryczkowski, @CSJCampbell, @tktan, @nachti, Conor Lawless, @timcdlucas, Dirk Eddelbuettel, @wolfganglederer, @HenrikBengtsson, @giocomai, and @daattali.
Many thanks also to the detailed feedback from the technical reviewers, Richard Cotton and Garrett Grolemund.
To Esther, Nathan, and Niamh. Thanks for your patience.
Thanks to my housemates in Cornerstone Housing Cooperative for putting up with me being antisocial while in book mode. To everyone at the University of Leeds for encouraging me to pursue projects outside the usual academic pursuits of journal articles and conferences. And thanks to everyone involved in the community of open source developers, users, and communicators who made all this possible.