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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

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Book ID Asin: 1491950781
Book Title: Efficient R Programming: A Practical Guide to Smarter Programming
Book Author: Colin Gillespie,Robin Lovelace
Book Format: unknownBook Price: unknown
Book Category: Books, Computers & Technology, Programming and unknown
Book Rating: 30 ratings

Efficient R Programming: A Practical Guide to Smarter Programming by Colin Gillespie,Robin Lovelace Book Review

Name: Kindle Customer
Rating: 3.0 out of 5 stars
Title: Parts of the examples work
Date: Reviewed in the United States on September 26, 2022
Review: Other parts don't.

The examples using datasets sometimes are complete, sometimes the required text is written later. And some point to outdated links

The optimization chapter fails because the source code cannot be retrieved

Name: Matthew J. Brauer
Rating: 3.0 out of 5 stars
Title: Reasonable content with uncharacteristicly poor production quality
Date: Reviewed in the United States on March 31, 2018
Review: The content is reasonable, but the book is very short and wow is the editing poorly done! Typos and other errors on every couple of pages, and the layout of figure legends seems to to have been considered at all. The amateurish production undermines the authority of the book’s tone, and it comes across in the end as a self-published pamphlet that someone decided to quickly turn into a book. I’m very disappointed with O’Reilly’s lack of attention to the book’s production. The material, the author and the purchaser deserve better.

Name: R. McDonald
Rating: 2.0 out of 5 stars
Title: Useful in parts, but mostly quite basic
Date: Reviewed in the United States on May 30, 2017
Review: On page 40 you will learn about keyboard shortcuts in RStudio. On page 50 there are a few pages about vectorization. On p. 154 a byte is defined. Too much of the book is at this level. There are bits and pieces that I find useful (I didn't know about the feather file format, for example), but on the whole this is much more basic than the title would indicate.

Name: James Igoe
Rating: 5.0 out of 5 stars
Title: Simply a great book...
Date: Reviewed in the United States on August 18, 2017
Review: Simply a great book, chock full of tips and techniques for improving one's work with R.

Name: Epilady
Rating: 4.0 out of 5 stars
Title: Definitely worth a read for beginner/novice R programmers
Date: Reviewed in the United States on August 1, 2017
Review: Writing beautiful concise code is definitely an art, and one that can be difficult to master as so many of us end up pulling together code to perform analytics in a very quick and dirty manner. This reviewer has been guilty of not commenting code and writing redundant routines because it was easier than figuring out the error in a macro or loop. In this book, Gillespie covers the concept of efficiency both through the amount of computational time and the programming time. For large datasets with a "standard" compute, in particular, R can be a little feisty, so computational tips are always useful.

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.

Name: TBS
Rating: 4.0 out of 5 stars
Title: Some useful items, but lots of introductory material
Date: Reviewed in the United States on August 27, 2017
Review: When looking at this, I expected it to include more for folks beyond the basics, which this seemed to cater to. For example, as another reviewer noted, a "byte" is defined on pg. 154, which is pretty simplistic. There are other examples of that, too, and they happen with a bit too much frequency. Also, this relates to large data sets, so a lot of folks will simply take a few extra minutes and approach these subjects differently.

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.

Name: Two kids mom
Rating: 4.0 out of 5 stars
Title: Get this book if you are dealing with massive data sets. Otherwise, its just an elegant manner in which to program.
Date: Reviewed in the United States on May 24, 2017
Review: Probably not necessary unless you are running REALLY large data sets. On the other hand, programming elegantly is useful when you need to make changes later as it is easier to figure out what you did in the past. You do not need this book right away if you are just learning R, but if you decide to do massive data mining it could certainly be worth the effort to master. This book would be helpful in that case, it is well written and clear if you have a medium level of knowledge in using R and statistics.

Name: C. Ang
Rating: 4.0 out of 5 stars
Title: Short book that hits on the broad topics
Date: Reviewed in the United States on May 12, 2017
Review: I use R mainly for financial analysis. Unless you are working with big data, a fairly new computer will be sufficient for you to not be an efficient programmer. A matter of even a few seconds is relatively inconsequential in the grand scheme of things (it may actually help give you a breather). However, this book is pretty useful for those instances in which you need to be more efficient. For example, since R uses the computer's RAM, reading in large files can quickly eat up the RAM. The book has a helpful discussion of how to break up large data so that R can read it in more efficiently. Overall, good and helpful book, but not necessary depending on what you do in R.