Efficient R Programming: A Practical Guide to Smarter Programming Paperback – 6 Jan 2017
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About the Author
Colin Gillespie is Senior lecturer (Associate professor) at Newcastle University, UK. He has been running R courses for over five years at a variety of levels, ranging from beginners to advanced programming. During his academic career, he has also been employed as an external consultant at Shell, Burberry, Yorkshire Bank, KPMG, and Tesco Bank. His research interests are high performance statistical computing and Bayesian statistics. Robin Lovelace is a Research Fellow in the Leeds Institute for Data Analytics, which specializes in the handling of large data sets. Robin has five years using R for academic research and three years teaching R at all levels. Robin developed the popular tutorial "Introduction to visualizing spatial data in R" and is working to publish "Spatial microsimulation with R" (CRC Press). Robin has used R on mission-critical contracts, including the creation of a nationally scalable interactive online mapping tool for the UK's Department for Transport (DfT).
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Most helpful customer reviews on Amazon.com
The first edition also reads like a hastily prepared draft. Numerous errors, incomplete descriptions, and redundant prose abound. If you can get past them, you will find excellent recommendations of sources that can be used to teach yourself a new topic or two. This book is freely available online at the first author's webpage, so skim this manuscript there and save your dollars for more effective material.
From that perspective, this book assumes at least a basic familiarity with R, although it touches on many of the intro concepts. It's not a good place to start if the reader is wanting to *learn* R. The concepts in it are also applicable to other languages, so it doesn't have to be R specific, but the code snippets are all designed to be executed in R. It links to other resources to learn R (including a personal favorite, the R Inferno), which is a nice touch. There is some discussion on the differences in R set up between OS (Windows, Linux, MacOS, Ubuntu).
The tips are sometimes interesting - e.g. vectorize data whenever possible. Eh, there are some data types where that's not possible and perhaps not indicated for the types of analysis that need to be performed. Are there other approaches that could work? Those are not to be found in this book.
Overall, moderate beginners and some intermediate R programmers will find this useful. Self-taught R programmers will also find some nuggets. Worth a read.
The book is extremely concise. We get an example or two and a short explanation of why we're doing things and then it's on the next topic. If you're just learning R, you'll need another resource or two. Though it may be a little short sometimes, it covers a lot of material in only 200 pages. I found myself going back to sections of the book several times to better understand the points being made and because there were so many points in a short space.
Overall, I liked the book and the writing style. I learned a ton about better ways to write R code. I'm going to use the information in this book. That's the highest praise I can give it.
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