- Paperback: 222 pages
- Publisher: O'Reilly Media; 1 edition (January 2, 2017)
- Language: English
- ISBN-10: 1491950781
- ISBN-13: 978-1491950784
- Product Dimensions: 7 x 0.5 x 9.2 inches
- Shipping Weight: 0.6 ounces (View shipping rates and policies)
- Average Customer Review: 10 customer reviews
- Amazon Best Sellers Rank: #912,499 in Books (See Top 100 in Books)
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Efficient R Programming: A Practical Guide to Smarter Programming 1st Edition
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About the Author
Colin Gillespie is Senior lecturer (Associate professor) at Newcastle University, UK. His research interests are high-performance computing and Bayesian statistics. He is regularly employed as a consultant by Jumping Rivers and has been teaching R since 2005.
Robin Lovelace is a researcher at the Leeds Institute for Transport Studies (ITS) and the Leeds Institute for Data Analytics (LIDA). Robin has many years using R for academic research and has taught numerous R courses at all levels. He has developed a number of popular R resources, including Introduction to Visualising Spatial Data in R and Spatial Microsimulation with R (Lovelace and Dumont 2016). These skills have been applied on a number of projects with real-world applications, including the Propensity to Cycle Tool, a nationally scalable interactive online mapping application and the stplanr package.
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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.
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.
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.
That said, I gave it a 3.5 star score because this did have a few portions that were helpful and that I learned from. After running across one of these, I actually stopped and looked the book over again, and I found it a bit better were I looking at it as a beginner or as someone with a specific need set. There was also discussion on items that helped because of their approach, but this is pretty much specific to how you use R and what you want/need to learn.