R in a Nutshell: A Desktop Quick Reference (In a Nutshell

In a Nutshell

by Joseph Adler

If you’re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You’ll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports. Updated for R 2.14 and 2.15, this second edition includes new and expanded chapters on R performance, the ggplot2 data visualization package, and parallel R computing with Hadoop.

Genres: Programming, Reference, Nonfiction, Technology, Technical, Computers, Computer Science

721 pages, Paperback

First published January 1, 2009

Reviews

[Alex Ott · Rating 4 out of 5]

Good introduction to R + reference...

[Maria Beatriz · Rating 5 out of 5]

This is a great book for anyone interested in R. It is very complete, has a strong introductory part and also more advanced content. It is still modern for 2019, with trendy issues, like machine learning and visualization.

I have worked with this book having a strong background in programming, but I don't think this is a requirement. I would recommend it for anyone who wants to learn R as a first or second language.

If you work hard using this book, you could achieve a somewhat fluent level in R in a reasonably short time. Read the book (skipping the in-depth parts), write and run the scripts in your computer, and you will learn R.

[Maaly · Rating 4 out of 5]

The book is useful as a reference for R and it has advanced content. but the only disturbing thing is that nutshell package is now archived and the examples was just read not practiced

[Michael Scott · Rating 5 out of 5]

I picked up this book because (i) I wanted to learn about R for a long time, (ii) I had a visualization project for which I needed a good automated graphing tool, and (iii) I was too sick to do anything else that day. But how to start quickly with addressing goals (i) and (ii)? Luckily for me, after about an hour of Quora- and Google-ing, I bumped into Joseph Adler's book, R in a Nutshell. Overall, and perhaps also due to (iii), the experience with this book was truly excellent, and I would recommend to starters to read this book over any other R book.

First, R in a Nutshell is a technical introduction to R, a programming environment for building data processing and visualization pipelines, especially of the type I needed. This is a mouthful, so let me explain:

technical, because the author knows the technology behind and in R, and knows how to explain it to a professional with prior technical knowledge. (A bit of background: I've done programming since I was 10-12, and hold a software engineering degree (and more).)
programming environment, meaning a programming language, but also an IDE (better, as I learned from the book, to use RStudio [1]), and many, many useful libraries [2]. High productivity indeed.
data processing and visualization is in my view a well-understood process in information systems, which consists of every stage after collecting data from one or more sources and until but including presenting data to your customers. In other words, R does not collect data for you, but everything else (ingestion, preparation, visualization, and saving) is managed well by R and its many libraries. (Caveat: R is not yet good to process big data, that is, high-volume, or high-velocity, or low-veracity, or any other V* issue. This book introduces the reader to Hadoop-based data processing, but Hadoop is already so 2010s... not the book's fault though.)
pipeline is actually a misnomer; in my world, people use workflows, which are simply sets of (data processing and visualization) tasks that have inter-dependencies (that is, a task cannot complete and likely not even start until all the tasks on which it depends have completed -- think about dressing up in the morning, and having to complete the task of putting your coat, which can only happen after the task of putting on your shirt has completed, which can only happen after... etc.) Pipelines are simply degenerate workflows, in which tasks chain linearly, so each task depends on only one previous task.
the type I needed--my project was simply to create fivethirtyeight.com-like graphs [3]. Starting from zero-level knowledge of R, I got to learn about: installing R, using an excellent IDE I knew nothing about before, installing libraries and their dependencies with ease, using basic R concepts up to data frames, importing (my Python-pre-processed) data and images, and visualizing data using ggplot2 and grid.


[1] RStudio, https://www.rstudio.com/
[2] For the most popular 100 packages, by downloads, see http://www.r-statistics.com/2013/06/top-100-r-packages-for-2013-jan-may/ or simply check the latest raw data from the CRAN log files, at http://cran-logs.rstudio.com/.
[3] fivethirtyeight.com was made famous by its lead statistician, Nate Silver's, appearance in TV shows related to political and sports analysis. For a typical graph in their style, see http://fivethirtyeight.com/features/everyone-files-their-taxes-at-the-last-minute/ or the interactives in http://projects.fivethirtyeight.com/election-2016/delegate-targets/democrats/.

[Rob · Rating 3 out of 5]

A good reference for the R language. Adler takes a nuts-and-bolts approach, starting with R's fundamental classes and working up through it's higher level stuff, and wrapping up with how to actually use it (R) to do statistical analysis. (Remember: BYOSK! [1]) I took a cover-to-cover approach with this book at first but found that it served me better when I needed to look up the answer to a "How do I...?"

Side note: I co-read this while I was taking Jeff Leek's Coursera course on Data Analysis. It was a good companion.

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[1] "Bring your own statistical knowledge."

[Karl · Rating 2 out of 5]

Not the clearest O'Reilly book I've read, but it's hard to know if that's the author or R, which is a weird piece of software. Assumes you know a lot of stats/data science already, and uses a lot of real-world data sets/examples. Not designed as a gentle intro.

[Mike S · Rating 5 out of 5]

This is a great introduction to R, but as he says in the book, it is no substitute for a solid foundation in statistics. It inspired me to purchase Statistics Unplugged, which I also highly recommend.

[Alan Parker]

It's all in the title. One of the first books that I bought on R. My copy is well worn. I use the list of all the commands in the back as a reference frequently. A nice relaxed style with lots of examples.

[Tathagata · Rating 5 out of 5]

Brilliant ...

[Max Nova · Rating 4 out of 5]

It's a good intro to R and some basic statistical analysis techniques. No complaints here.

[Delhi Irc]

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[Bill Rand · Rating 3 out of 5]

Really well done reference book, and even reading it over quickly provided me some new insights into R.

[David · Rating 4 out of 5]

After you've done a few free online tutorials, this is a great desktop reference book.

[Marijka · Rating 4 out of 5]

Great reference book for learning and using R.