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In a Nutshell
R in a Nutshell
Why learn R? Because it's rapidly becoming the standard for developing statistical software. R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics. The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems. "I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians."
640 pages, Paperback
First published January 1, 2009
31 people are currently reading
270 people want to read
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Displaying 1 - 14 of 14 reviews
February 16, 2012
Good introduction to R + reference...
December 30, 2019
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.
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.
March 7, 2024
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
April 23, 2016
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 's book, . 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, 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,
[2] For the most popular 100 packages, by downloads, see or simply check the latest raw data from the CRAN log files, at .
[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 or the interactives in .
First, 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,
[2] For the most popular 100 packages, by downloads, see or simply check the latest raw data from the CRAN log files, at .
[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 or the interactives in .
March 10, 2013
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."
Side note: I co-read this while I was taking Jeff Leek's Coursera course on Data Analysis. It was a good companion.
----
[1] "Bring your own statistical knowledge."
October 26, 2012
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.
May 12, 2015
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.
Read
September 18, 2018It'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.
March 18, 2011
Brilliant ...
May 17, 2015
It's a good intro to R and some basic statistical analysis techniques. No complaints here.
Read
January 21, 2016DL027828 ND6
February 27, 2021
Really well done reference book, and even reading it over quickly provided me some new insights into R.
January 22, 2011
After you've done a few free online tutorials, this is a great desktop reference book.
December 18, 2014
Great reference book for learning and using R.
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