May the source be with you, but remember the KISS principle ;-)
Home Switchboard Unix Administration Red Hat TCP/IP Networks Neoliberalism Toxic Managers
(slightly skeptical) Educational society promoting "Back to basics" movement against IT overcomplexity and  bastardization of classic Unix

R Bookshelf

News R programming language Classic Books Recommended Links Software Engineering Programming style Rstudio R-Studio Keyboard Shortcuts
Debugging CRAN package repository R help system R Debugging A Slightly Skeptical View on Scripting Languages
Software Fashion Conway Law KISS Principle Tips Quotes R history Humor Etc


Note: An excellent resource as for books and websites related to R is Computerworld article  60+ R resources to improve your data skills . Please read it first.

Authors of books on R use multiple "book writing" strategies. Some authors cover way too many packages. Some limit themselves to a few. Same have many different examples Others use the same example in many chapters.  Much of the value of the book for particular reader comes from matching background and topics covered. As such there is not and can't be the "best" book for all readers. You need to chose the book hat suit your needs best.

Also most of the books on R have recipe flavor -- they show how something is done in R and that's it. So if a particular book is close to the area where you plan to use R (for example, technical analysis) it is a distinct plus as most probably it will cover those things that are necessary for programming in this domain in more details then other R books.

R books

Still there are several good "general" R books

  1. The R book by Michael J. Crawley. One of the few attempt to cover R as the language, not as the set of recipes. Chapter 2 contains one of the best overview of the language.
  2. R Cookbook  by Paul Teetor If you want set of recipes here you can found them ;-)
  3. R for everyone by Jared P. Lander
  4. The Art of R Programming by Norman Matloff

The R book by Michael J. Crawley

The R book. by Michael J. Crawley is one of the few attempt to cover R as the language, not as the set of recipes. This is a big book, over 1K pages, and I think 1K pages is the minimal amount in which you can cover the R language in some detail. 

Hardcover: 1076 pages
Publisher: Wiley; 2 edition (December 26, 2012)
Language: English
ISBN-10: 0470973927

Writing a book on a complex and powerful scripting language such as R is a daunting task and requires several iteration. That's what we have in this book. first edition was actionlly the second as the content of this book was similar to an earlier book by the same author (Statistical Computing: An Introduction to Data Analysis using S-Plus). So the second edition can be viewed as the third edition.

Writers of the "language book" need to carefully consider their audience (which now for the most part knows some Java and possibly Python) and clearly state how features of the language really work and not to fall in a typical for books about R recipe approach. This is a very difficult task and the author did a good job in this area which makes the book outstanding. Chapter 2 contains one of the best overview of the language.

See selected Amazon reviews for the book that might help you to decide whether this is a book for you or not.

R Cookbook by Paul Teetor

R Cookbook  by Paul Teetor If you want set of recipes here you can found them ;-)

Here is one review from

Dimitri Shvorob on July 31, 2011

Great for beginners

You will be disappointed if you are a competent R programmer looking for "hacks". Note that the only negative review so far mentions its author's four-year experience with R. Mine is much shorter, yet I too find the book too "junior" for my needs. (With "R in Nutshell" and Google at my disposal, I can send "R Cookbook" back after making several notes to record what I learnt from it. There are several nuggets that you will not find in "R in Nutshell", or will not think to google). However, the book is not advertised as an "R in Depth", so no complaints.

"R Cookbook" is a friendly and highly informative introduction to "general-purpose" R (one half of the book) and doing basic statistics with R (the other half). A chapter on time series, with a look at "zoo" package, is a bonus; a somewhat light (but does-the-job) take on R graphics may be viewed as a downside, but I see the benefit of getting the basics right, and letting the reader explore other resources - I would recommend "R Graphs Cookbook" and Quick-R Web site - when he/she is ready.

Yes, there are free R tutorials out on the Web - but given that this one is widely praised and inexpensive (even if you are never going to resell it - and at some point you probably should, and move to "R in Nutshell" - $25 is not too much. How much saved time is worth $25 for you?), why not take a look?

PS. "R in Action" by Robert Kabacoff is another option, and one that I actually like better.

PPS. The second edition of Michael Crawley's "R Book" is a large improvement over the first, and is a stronger competitor to both "R Cookbook" and "R in Action".

R for everyone by Jared P. Lander

R for Everyone  by By Jared P. Lander  is a good introductory book for non-programmers. Has little  value for programmers proficient in Perl or other scripting languages.  It is almost 500 pages long, half-the length of R book and this shows.

Again this book is not for everyone. It's only good for absolute beginners. This book is basically 2-distinct books: The first 13-chapters are the basics of R. They are OK only if you are new to R.

The book "R for everyone" has many positive reviews on Amazon. 

Among advantages of the book that are sited in reviews are 

The author used to teach a course at Columbia, which is a plus, but the standard of coverage of programming language in this book is very low as this is book mainly directed toward non professionals.   Unfortunately the author provides only minimal online resources at (no code examples was posted by the author which is "big no" and signifies the lack of respect for readers ):

  1. There is one video available.  
  2. The lesson on building scatterplots with ggplot2 is available on YouTube.
  3. Table of Contents (also available as a pdf)
  4. Data used
  5. Packages used or mentioned
  6. People mentioned
  7. Errata

Pearson has posted Chapter 12 “Data Reshaping”, online for free.

The idea to provide 80% of functionality by explaining only 20% of the language is great, but it's implementation if far from perfect. The book feels rushed. Examples can definitely be polished more and made more useful by using them in different chapters. Currently chapters look isolated.  Still it is OK for beginners who does not have a programming experience in any language, but even in this case there might be better books. For an introductory book the author introduces way too many packages. Used examples that are way too diverse and generally do not care about consistency from one chapter to another. I do not recommend this book for people who already know other scripting language such as Perl. The R book by Michael J. Crawley is a better deal. 

See selected Amazon reviews for the book that might help you to decide whether this is a book for you or not.

The Art of R Programming by Norman Matloff

The Art of R Programming is a pretty short book (400 pages only) oriented on people who already know programming in some other language.

This book concentrates on essentials of the R language itself and contains well-chosen examples that illustrate the concepts involved (as opposed to focusing on statistics or data analysis). If covers such rarely addressed in R books concepts as

(Older free version is available as PDF, so you can get pretty accurate impression about the book by reading the PDF). This is more systematic approach to the language that in other books. As you can read older version online you can get  solid understanding of what book is about and the style of the author.

See selected Amazon reviews for the book that might help you to decide whether this is a book for you or not.

Free books

There is one "official" free book from R Group: An Introduction to R: A Programming Environment for Data Analysis and Graphics ©2009-2012 (William N Venables, David M Smith). It has reference style and covers version 3.2.0.

Like in case of Perl there are several free books (adapted from The R Programming Language - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials)

Quirks of the language are discussed in

Web courses


Slightly updated version of 60+ R resources by Sharon Machlis

Usually such lists published in Computerworls are of below average quality.

[Jun 13, 2015] 60+ R resources to improve your data skills By Sharon Machlis

Published in 2014 it now look slightly outdate but still extremly valuable resource. I put some minor corrections and updates for 2015 in the text 
Jan 15, 2014 | Computerworld

This list was originally published as part of the Computerworld Beginner's Guide to R but has since been expanded to also include resources for advanced beginner and intermediate users. If you're just starting out with R, I recommend first heading to the Beginner's Guide.

These websites, videos, blogs, social media/communities, software and books/ebooks can help you do more with R; my favorites are listed in bold.

Want to see a sortable list of resources by subject and type? Expand the chart below. You can also search for key terms within the chart by using the search box below.

R Resources
Resource Topic Type
R book Recommended textbook  
R Cookbook Recipes; recommended book or ebook
R Graphics Cookbook Best coverage of R graphics capabilities; recommended book or ebook
R In Action not recommended book or ebook
The Art of R Programming Recommended for people with programming experince book or ebook
R for Everyone (not recommended for programmer; OK for novices) Introductory book. Very basic. book or ebook
R in a Nutshell general R book or ebook
R For Dummies general R book or ebook
Statistical Analysis with R general R book or ebook
Introduction to Data Science data analysis ebook
Reproducible Research with R and RStudio Reports in R book or ebook
Exploring Everyday Things with R and Ruby general R book or ebook
Visualize This graphics book or ebook


Statistics and R on Google+ general R community
#rstats hashtag general R community
Stackoverflow general R community
R User Meetups general R community
RStudio Documentation R programming documentation
CRAN general R official R site
Try R general R online interactive class
4 data wrangling tasks in R for advanced beginners general R online reference
Data manipulation tricks: Even better in R general R online reference & PDF
Cookbook for R general R online reference
Quick-R general R online reference
Short List of R Commands general R online reference
FAQ About R general R online reference
Chart Chooser in R graphics-ggplot2 online reference
R Graphic Catalog graphics-ggplot2 online reference
ggplot2 Cheat Sheet graphics-ggplot2 online reference
Ten Things You Can Do in R That You Wouldve Done in Microsoft Excel for Excel users online reference
R Reference Card for Data Mining data mining PDF
Spatial Cheat Sheet geospatial online reference
Web interface for ggplot2 graphics-ggplot2 online tool
R Tutorial general R online tutorials general R online tutorials
How to Visualize and Compare Distributions graphics online tutorials
Getting Started with Charts in R graphics online tutorials
Producing Simple Graphs with R graphics online tutorials
Quick Intro to ggplot2 graphics-ggplot2 online tutorials
Introducing R general R online tutorials
Using R general R online tutorials
Aggregating and restructuring data data reshaping online tutorials
Higher Order Functions in R R programming online tutorials
Introductory Econometrics Using Quandl and R statistics online tutorials
ggplot2 Guide graphics-ggplot2 online tutorials general R online tutorials
Introduction to dplyr general R online tutorials
Applied Time Series Analysis time series online tutorials
13 resources for time series analysis time series online tutorials
knitr in a knutshell reproducible research online tutorials
The Undergraduate Guide to R general R PDF or Google Doc
Little Book of R for Time Series time series online tutorials
ggplot2 workshop presentation graphics-ggplot2 online tutorials
ggplot2_tutorial.R graphics-ggplot2 online tutorials
More and Fancier Graphics graphics online tutorials
How to turn CSV data into interactive visualizations with R and rCharts graphics online tutorials
R Reference Card general R PDF
Introduction to R general R PDF
Handling and Processing Strings in R text in R PDF
Learning Statistics with R statistics PDF
R: A Self-learn Tutorial general R PDF
Introduction to ggplot2 graphics-ggplot2 PDF
Statistics with R Computing and Graphics general R PDF
Using R for your Basic Statistical Needs general R R code
Short Courses by Hadley Wickham general R, graphics R code and slides
Introducing R general R R code and slides
Rseek general R search
R site search general R search
R mailing list search general R search, community
RStudio IDE R programming software
Revolution R R programming software
Enterprise Runtime for R R programming software
Shiny for interactive Web apps interactive graphics software
Swirl statistics software
R Style Guides R programming style guide
Up and Running with R general R video class
Computing for Data Analysis general R video class
Data Analysis data analysis video class
Statistics One statistics video class
Twotorials general R video tutorials
Google Developers' Intro to R general R video tutorials
Introduction to Data Science with R general R, ggplot2 video tutorials
Data Analysis and Visualization Using R general R, statistics video tutorials
Programming in R at general R website
R-bloggers general R blog
Revolutions general R blog
10 R Packages I Wish I Knew About Earlier R packages blog post
R programming for those coming from other languages R programming blog post
A brief introduction to 'apply' in R general R blog post
History of R Financial Time Series Plotting graphics blog post
Translating between R and SQL general R blog post
Graphs & Charts in base R, ggplot2 and rCharts graphics blog post
When to use Excel, when to use R? for Excel users blog post
A First Step Towards R From Spreadsheets for Excel users blog post
Using dates and times in R R programming blog post
Scraping Pro-Football Data and Interactive Charts using rCharts, ggplot2, and shiny graphics blog post
Grouping & Summarizing Data in R general R slide presentation
R Instructor general R app
Click to show/hide rest of table

Books and e-books
  1. R Cookbook. Like the rest of the O'Reilly Cookbook series, this one offers how-to "recipes" for doing lots of different tasks, from the basics of R installation and creating simple data objects to generating probabilities, graphics and linear regressions. It has the added bonus of being well written. If you like learning by example or are seeking a good R reference book, this is well worth adding to your reference library. By Paul Teetor, a quantitative developer working in the financial sector.
  2. R Graphics Cookbook. If you want to do beyond-the-basics graphics in R, this is a useful resource both for its graphics recipes and brief introduction to ggplot2. While this goes way beyond the graphics capabilities that I need in R, I'd recommend this if you're looking to move beyond advanced-beginner plotting. By Winston Chang, a software engineer at RStudio.
  3. R in Action: Data analysis and graphics with R. This book aims at all levels of users, with sections for beginning, intermediate and advanced R ranging from "Exploring R data structures" to running regressions and conducting factor analyses. The beginner's section may be a bit tough to follow if you haven't had any exposure to R, but it offers a good foundation in data types, imports and reshaping once you've had a bit of experience. There are some particularly useful explanations and examples for aggregating, restructuring and subsetting data, as well as a lot of applied statistics. Note that if your interest in graphics is learning ggplot2, there's relatively little on that here compared with base R graphics and the lattice package. You can see an excerpt from the book online: Aggregation and restructuring data. By Robert I. Kabacoff.
  4. The Art of R Programming. For those who want to move beyond using R "in an ad hoc way ... to develop[ing] software in R." This is best if you're already at least moderately proficient in another programming language. It's a good resource for systematically learning fundamentals such as types of objects, control statements (unlike many R purists, the author doesn't actively discourage for loops), variable scope, classes and debugging -- in fact, there's nearly as large a chapter on debugging as there is on graphics. With some robust examples of solving real-world statistical problems in R. By Norman Matloff.
  5. R in a Nutshell. A reasonably readable guide to R that teaches the language's fundamentals -- syntax, functions, data structures and so on -- as well as how-to statistical and graphics tasks. Useful if you want to start writing robust R programs, as it includes sections on functions, object-oriented programming and high-performance R. By Joseph Adler, a senior data scientist at LinkedIn.
  6. Visualize This. Note; Most of this book is not about R, but there are several examples of visualizing data with R. And there's so much other interesting info here about how to tell stories with data that it's worth a read. By Nathan Yau, who runs the popular Flowing Data blog and whose doctoral dissertation was on "personal data collection and how we can use visualization to learn about ourselves."
  7. R For Dummies. I haven't had a chance to read this one, but it's garnered some good reviews on If you're familiar with the Dummies series and have found them helpful in the past, you might want to check this one out. You can get a taste of the authors' style in the Programming in R section of, which has more than a 100 short sections such as How to construct vectors in R and How to use the apply family of functions in R. By Joris Meys and Andrie de Vries.
  8. Introduction to Data Science. It's highly readable, packed with useful examples and free -- what more could you want? This e-book isn't technically an "R book," but it uses R for all of its examples as it teaches concepts of data analysis. If you're familiar with that topic you may find some of the explanations rather basic, but there's still a lot of R code for things like analyzing tweet rates (including a helpful section on how to get Twitter OAuth authorization working in R), simple map mashups and basic linear regression. Although Stanton calls this an "electronic textbook," Introduction to Data Science has a conversational style that's pleasantly non-textbook like. There used to be a downloadable PDF...
  9. R for Everyone. Author Jared P. Lander promises to go over "20% of the functionality needed to accomplish 80% of the work." And in fact, topics that are actually covered, are covered pretty well; but be warned that some items appearing in the table of contents can be a little thin. This is still a well-organized reference, though, with information that beginning and intermediate users might want to know: importing data, generating graphs, grouping and reshaping data, working with basic stats and more.
  10. Statistical Analysis With R: Beginner's Guide. This book has you "pretend" you're a strategist for an ancient Chinese kingdom analyzing military strategies with R. If you find that idea hokey, move along to see another resource; if not, you'll get a beginner-level introduction to various tasks in R, including tasks you don't always see in an intro text, such as multiple linear regressions and forecasting. Note: My early e-version had a considerable amount of bad spaces in my Kindle app, but it was still certainly readable and usable.
  11. Reproducible Research with R and RStudio. Although categorized as a "bioinformatics" textbook (and priced that way - even the Kindle edition is more than $50), this is more general advice on steps to make sure you can document and present your work. This includes numerous sections on creating report documents using the knitr package, LaTeX and Markdown -- tasks not often covered in-depth in general R books. The author has posted source code for generating the book on GitHub, though, if you want to create an electronic version of it yourself.
  12. Exploring Everyday Things with R and Ruby. This book oddly goes from a couple of basic introductory chapters to some fairly robust, beyond-beginner programming examples; for those who are just starting to code, much of the book may be tough to follow at the outset. However, the intro to R is one of the better ones I've read, including lot of language fundamentals and basics of graphing with ggplot2. Plus experienced programmers can see how author Sau Sheong Chang splits up tasks between a general language like Ruby and the statistics-focused R.
Online references
  1. 4 data wrangling tasks in R for advanced beginners. This follow-up to our Beginner's Guide outlines how to do several specific data tasks in R: add columns to an existing data frame, get summaries, sort results and reshape data. With sample code and explanations.
  2. Data manipulation tricks: Even better in R. From working with dates to reshaping data to if-then-else statements, see how to perform common data munging tasks. You can also download these R tips & tricks as a PDF (free Insider registration required).
  3. Cookbook for R. Not to be confused with the R Cookbook book mentioned above, this website by software engineer Winston Chang (author of the R Graphics Cookbook) offers how-to's for tasks such as data input and output, statistical analysis and creating graphs. It's got a similar format to an O'Reilly Cookbook; and while not as complete, can be helpful for answering some "How do I do that?" questions.
  4. Quick-R. This site has a fair amount of samples and brief explanations grouped by major category and then specific items. For example, you'd head to "Stats" and then "Frequencies and crosstabs" to get an explainer of the table() function. This ranges from basics (including useful how-to's for customizing R startup) through beyond-beginner statistics (matrix algebra, anyone?) and graphics. By Robert I. Kabacoff, author of R in Action.
  5. R Reference Card. If you want help remembering function names and formats for various tasks, this 4-page PDF is quite useful despite its age (2004) and the fact that a link to what's supposed to be the latest version no longer works. By Tom Short, an engineer at the Electric Power Research Institute.
  6. A short list of R the most useful commands. Commands grouped by function such as input, "moving around" and "statistics and transformations." This offers minimal explanations, but there's also a link to a longer guide to Using R for psychological research. HTML format makes it easy to cut and paste commands. Also somewhat old, from 2005. By William Revelle, psychology professor at Northwestern University.
  7. Chart Chooser in R. This has numerous examples of R visualizations and sample code to go with them, including bar, column, stacked bar & column, bubble charts and more. It also breaks down the visualizations by categories like comparison, distribution and trend. By Greg Lamp, based on Juice Labs' Chart Chooser for Excel and PowerPoint.
  8. R Graph Catalog. Lots of graph and other plot examples, easily searchable and each with downloadable code. All are made with ggplot2 based on visualization ideas in Creating More Effective Graphs. Maintained by Joanna Zhao and Jennifer Bryan.
  9. Beautiful Plotting in R: A ggplot2 Cheatsheet. Easy to read with a lot of useful information, from starting with default plots to customizing title, axes, legends; creating multi-panel plots and more. By Zev Ross.
  10. Frequently Asked Questions about R. Some basics about reading, writing, sorting and shaping data as well as a lineup of how to do various statistical operations and a few specialized graphics such as spaghetti plots. From UCLA's Institute for Digital Research and Education.
  11. R Reference Card for Data Mining. This is a task-oriented compilation of useful R packages and functions for things ranging from text mining and time series analysis to more general subjects like graphics and data manipulation. Since decriptions are somewhat bare-boned, this will likely be more useful to either remind you of functions you've seen before or give you suggestions for things to try. For much more on the subject, head to the author's R and Data Mining website, which includes examples and other documentation. including a substantial portion of his book R and Data Mining published by Elsevier in 2012. By Yanchang Zhao.
  12. Spatial Cheat Sheet. For those doing GIS and spatial analysis work, this list offers some key functions and packages for working with spatial vector and raster data. By Barry Stephen Rowlingson at Lancaster University in the U.K.
Online tools

Web interface for ggplot2. This online tool by UCLA Ph.D. candidate Jeroen Ooms creates an interactive front end for ggplot2, allowing users to input tasks they want to do and get a plot plus R code in return. Useful for those who want to learn more about using ggplot2 for graphics without having to read through lengthy documentation.

Ten Things You Can Do in R That You Wouldve Done in Microsoft Excel. From the R for Dummies Web site, these code samples aim to help Excel users feel more comfortable with R.


Twotorials. You'll either enjoy these snappy 2-minute "twotorial" videos or find them, oh, corny or over the top. I think they're both informative and fun, a welcome antidote to the typically dry how-to's you often find in statistical programming. Analyst Anthony Damico takes on R in 2-minute chunks, from "how to create a variable with R" to "how to plot residuals from a regression in R;" he also tackles an occasional problem such as "how to calculate your ten, fifteen, or twenty thousandth day on earth with R." I'd strongly recommend giving this a look if textbook-style instruction leaves you cold.

Sample "Twotorial" video.

Google Developers' Intro to R. This series of 21 short YouTube videos includes some basic R concepts, a few lessons on reshaping data and some info on loops. In addition, six videos focus on a topic that's often missing in R intros: working with and writing your own functions. This YouTube playlist offers a good programmer's introduction to the language -- just note that if you're looking to learn more about visualizations with R, that's not one of the topics covered.

This video in the Google Developers' R series introduces functions in R.

Up and Running with R. This video class covers the basics of topics such as using the R environment, reading in data, creating charts and calculating statistics. The curriculum is limited, but presenter Barton Poulson tries to explain what he's doing and why, not simply run commands. He also has a more in-depth 6-hour class, R Statistics Essential Training. is a subscription service that starts at $25/month, but several of the videos are available free for you to view and see if you like the instruction style, and there's a 7-day free trial available.

Coursera: Computing for Data Analysis. Coursera's free online classes are time-sensitive: You've got to enroll while they're taking place or you're out of luck. However, if there's no session starting soon, instructor Roger Peng, associate professor of biostatistics at Johns Hopkins University, posted his lectures on YouTube; Revolution Analytics then collected them on a handy single page. While I found some of these a bit difficult to follow at times, they are packed with information, and you may find them useful.

Intro video for the Coursera Computing for Data Analysis course

Coursera: Data Analysis. This was more of an applied statistics class that uses R as opposed to one that teaches R; but if you've got the R basics down and want to see it in action, this might be a good choice. There are no upcoming scheduled sessions for this at Coursera, but instructor Jeff Leek -- an assistant professor of biostatistics at Johns Hopkins, posted his lecture videos on YouTube, and Revolution Analytics collected links to them all by week.

Intro video for Coursera Data Analysis online class

Coursera: Statistics One If you don't mind going through a full, 12-week stats course along with learning R, Princeton University senior lecturer Andrew Conway's class includes an introduction to R. "All the examples and assignments will involve writing code in R and interpreting R output," says the course description. You can check the Coursera link to see if and when future sessions are scheduled.

Introduction to Data Science with R. At $160 this O'Reilly training course is somewhat pricey considering how many free and lower-cost video classes there are out there. However, if you're looking for a step-by-step intro to R, this is a useful course, starting with language and ggplot2 visualization basics through modeling. It's taught by RStudio Master Instructor Garrett Grolemund, who focuses on hands-on learning as well as explaining a few of the language's quirks. If cost is an issue and you're not in a hurry, sign up for O'Reilly's deal emails and you may eventually find a 50% off sale. Also availble from O'Reilly Safary unlimited. --[NNB]

Data Analysis and Visualization Using R. Free course that uses both video and interactive R to teach language basics, ggplot2 visualization basics, some statistical tests and exploratory data analysis including data.table. Videos by Princeton Ph.D. student David Robinson and Neo Christopher Chung, Ph.D, filmed and edited at the Princeton Broadcast Center.

Other online introductions and tutorials

Try R This beginner-level interactive online course will probably seem somewhat basic for anyone who has experience in another programming language. However, even if the focus on pirates and plunder doesn't appeal to you, it may be a good way to get some practice and get more comfortable using R syntax.

An Introduction to R. Let's not forget the R Project site itself, which has numerous resources on the language including this intro. The style here is somewhat dry, but you'll know you're getting accurate, up-to-date information from the R Core Team.

How to Visualize and Compare Distributions. This short and highly readable Flowing Data tutorial goes over traditional visualizations such as histograms and box plots. With downloadable code.

Handling and Processing Strings in R. This PDF download covers many things you're want to do with text, from string lengths and formatting to search and replace with regular expressions to basic text analysis. By statistician Gaston Sanchez.

Learning statistics with R: A tutorial for psychology students and other beginners by Daniel Navarro at the University of Adelaide (PDF). 500+ pages that go from "Why do we learn statistics" and "Statistics in every day life" to linear regression and ANOVA (ANalysis Of VAriance). If you don't need/want a primer in statistics, there are still many sections that focus specifically on R.

R Tutorial. A reasonably robust beginning guide that includes sections on data types, probability and plots as well as sections focused on statistical topics such as linear regression, confidence intervals and p-values. By Kelly Black, associate professor at Clarkson University. This site is probably best known in the R community for author Bob Muenchen's tracking of R's popularity vs. other statistical software. However, in the Examples section, he's got some R tutorials such as basic graphics and graphics with ggplots. He's also posted code for tasks such as data import and extracting portions of your data comparing R with alternatives such as SAS and SPSS.

Aggregating and restructuring data. This excerpt from R in Action goes over one of the most important subjects in using R: reshaping your data so it's in the format needed for analysis and then grouping and summarizing that data by factors. In addition to touching on base-R functions like the useful-but-not-always-well-known aggregate(), it also covers melt() and cast() with the reshape package. By Robert I. Kabacoff.

Getting started with charts in R. From the popular FlowingData visualization website run by Nathan Yau, this tutorial offers examples of basic plotting in R. Includes downloadable source code. (While many FlowingData tutorials now require a paid membership to the site, as of May 2013 this one did not.)

Using R for your basic statistical Needs LISA Short Course. Aimed at those who already know stats but want to learn R, this is a file of R code with comments, making it easy to run (and alter) the code yourself. The programming is easy to follow, but if you haven't brushed up on your stats lately, be advised that comments such as

Suppose we'd like to produce a reduced set of independent variables. We could use the function # step() to perform stepwise model selection based on AIC which is -2log(Likelihood) + kp? Where k=2 # and p = number of model parameters (beta coefficients).

may be tough to follow. By Nels Johnson at Virginia Tech's Laboratory for Interdisciplinary Statistical Analysis.

Producing Simple Graphs with R. Although 6+ years old now, this gives a few more details and examples for several of the visualization concepts touched on in our beginner's guide. By Frank McCown at Harding University.

Short courses. Materials from various courses taught by Hadley Wickham, chief scientist at RStudio and author of several popular R packages including ggplot2. Features slides and code for topics beyond beginning R, such as R development master class.

Quick introduction to ggplot2. Very nice, readable and -- as promised -- quick introduction to the ggplot2 add-on graphic package in R, incuding lots of sample plots and code. By Google engineer Edwin Chen.

ggplot2 workshop presentation. This robust, single-but-very-long-page tutorial offers a detailed yet readable introduction to the ggplot2 graphing package. What sets this apart is its attention to its theoretical underpinnings while also offering useful, concrete examples. From a presentation at the Advances in Visual Methods for Linguistics conference. By Josef Fruehwald, then a PhD candidate at the University of Pennsylvania.

ggplot2_tutorial.R. This online page at, prepared for the Santa Barbara R User Group, includes a lot of commented R code and graph examples for creating data visualizations with ggplot2.

More and Fancier Graphics. This one-page primer features loads of examples, including explainers of a couple of functions that let you interact with R plots, locator() and identify() as well as a lot of core-R plotting customization. By William B. King, Coastal Carolina University.

ggplot2 Guide. This ggplot2 explainer skips the simpler qplot option and goes straight to the more powerful but complicated ggplot command, starting with basics of a simple plot and going through geoms (type of plot), faceting (plotting by subsets), statistics and more. By data analyst George Bull at Sharp Statistics.

Using R. In addition to covering basics, there are useful sections on data manipulation -- an important topic not easily covered for beginners -- as well as getting statistical summaries and generating basic graphics with base R, the Lattice package and ggplot2. Short explainers are interspersed with demo code, making this useful as both a tutorial and reference site. By analytics consultant Alastair Sanderson, formerly research fellow in the Astrophysics & Space Research (ASR) Group at the University of Birmingham in the U.K.

The Undergraduate Guide to R. This is a highly readable, painless introduction to R that starts with installation and the command environment and goes through data types, input and output, writing your own functions and programming tips. Viewable as a Google Doc or downloadable as a PDF, plus accompanying files. By Trevor Martin, then at Princeton University, funded in part by an NIH grant.

How to turn CSV data into interactive visualizations with R and rCharts. 9-page slideshow gives step-by-step instructions on various options for generating interactive graphics. The charts and graphs use jQuery libraries as the underlying technology but only a couple of line of R code are needed. By Sharon Machlis, Computerworld.

Higher Order Functions in R. If you're at the point where you want to apply functions on multiple vectors and data frames, you may start bumping up against the limits of R's apply family. This post goes over 6 extremely useful base R functions with readable explanations and helpful examples. By John Mules White, "soon-to-be scientist at Facebook."

Introductory Econometrics Using Quandl and R While this does indeed promote Quandl as your data source, that data is free, and for those interested in using R for regressions, you'll find several detailed walk-throughs from data import through statistical analysis.

Introduction to dplyr. The dplyr package (by ggplot2 creator Hadley Wickham) significantly speeds up operations like grouping and sorting of data frames. It also aims to rationalize such functions by using a common syntax. In this short introductory vignette, you'll learn about "five basic data manipulation" -- filter(), arrange(), select(), mutate() and summarise() -- including examples, as well as how to chain them together for more streamlined, readable code. Another useful package for manipulating data in R: doBy.

Applied Time Series Analysis. Text-based online class from Penn State "to learn and apply statistical methods for the analysis of data that have been observed over time." Access to the articles is free, although there is no community or instructor participation.

13 resources for time series analysis. A video and 12 slide presentations by Rob J. Hyndman, author of Forecasting time series using R. Also has links to exercises and answers to the exercises.

knitr in a knutshell. knitR is designed to easily create reports and other documents that can combine text, R code and the results of R code -- in short, a way to share your R analyses with others. This "minimal tutorial" by Karl Broman goes over subjects such as creating Markdown documents and adding graphics and tables, along with links to resources for more info.

More free downloads and websites from academia:

Little Book of R for Time Series. This is extremely useful if you want to use R for analyzing data collected over time, and also has some introductory sections for general R use even if you're not doing time series. By Avril Coghlan at the Wellcome Trust Sanger Instituie, Cambridge, U.K.

Introduction to ggplot2. 11-page PDF with some ggplot basics, by N. Matloff at UC Davis.


Pretty much every social media platform has an R group. I'd particularly recommend:

Statistics and R on Google+. Community members are knowledgeable and helpful, and various conversation threads engage both newbies and experts.

Twitter #rstats hashtag. Level of discourse here ranges from beginner to extremely advanced, with a lot of useful R resources and commentary getting posted.

You can also find R groups on LinkedIn, Reddit and Facebook, among other platforms.

Stackoverflow has a very active R community where people ask and answer coding questions. If you've got a specific coding challenge, it's definitely worth searching here to see if someone else has already asked about something similar.

There are dozens of R User Meetups worldwide. In addition, there are other user groups not connected with Revolution Analytics has an R User Group Directory.

Blogs & blog posts

R-bloggers. This site aggregates posts and tutorials from more than 250 R blogs. While both skill level and quality can vary, this is a great place to find interesting posts about R -- especially if you look at the "top articles of the week" box on the home page.

Revolutions. There's plenty here of interest to all levels of R users. Although author Revolution Analytics is in the business of selling enterprise-class R platforms, the blog is not focused exclusively on their products.

Post: 10 R packages I wish I knew about earlier. Not sure all of these would be in my top 10, but unless you've spent a fair amount of time exploring packages, you'll likely find at least a couple of interesting and useful R add-ons.

Post: R programming for those coming from other languages. If you're an experienced programmer trying to learn R, you'll probably find some useful tips here.

Post: A brief introduction to 'apply' in R. If you want to learn how the apply() function family works, this is a good primer.

Translating between R and SQL. If you're more experienced (and comfortable) with SQL than R, it can be frustrating and confusing at times to figure out how to do basic data tasks such as subsetting your data. Statistics consultant Patrick Burns shows how to do common data slicing in both SQL and R, making it easier for experienced database users to add R to their toolkit.

Graphs & Charts in base R, ggplot2 and rCharts. There are lots of sample charts with code here, showing how to do similar visualization tasks with basic R, the ggplot2 add-on package and rCharts for interactive HTML visualizations.

When to use Excel, when to use R? For spreadsheet users starting to learn R, this is a useful question to consider. Michael Milton, author of Head First Data Analysis (which discusses both Excel and R), offers practical (and short) advice on when to use each.

A First Step Towards R From Spreadsheets. Some advice on both when and how to start moving from Excel to R, with a link to a follow-up post, From spreadsheet thinking to R thinking.

Using dates and times in R. This post from a presentation by Bonnie Dixon at the Davis R Users' group goes over some of the intricacies of dates and times in R, including various date/time classes as well as different options for performing date/time calculations and other manipulations.

Scraping Pro-Football Data and Interactive Charts using rCharts, ggplot2, and shiny. This is a highly useful example of beginning-to-end data analysis with R. You'll see a sample of how to scrape data off a website, clean and restructure the data and then visualize it in several ways, including interactive Web graphics -- all with downloadable code. By Vivek Patil, an associate professor at Gonzaga University.


Searching for "R" on a general search engine like Google can be somewhat frustrating, given how many utterly unrelated English words include the letter r. Some search possibilities:

RSeek is a Web search engine that just returns results from certain R-focused websites.

R site search returns results just from R functions, package "vignettes" (documentation that helps explain how a function works) and task views (focusing on a particular field such as social science or econometrics).

You can also search the R mailing list archives.


Google's R Style Guide. Want to write neat code with a consistent style? You'll probably want a style guide; and Google has helpfully posted their internal R style for all to use. If that one doesn't work for you, Hadley Wickham has a fairly abbreviated R style guide based on Google's but "with a few tweaks."

RStudio documentation. If you're using RStudio, it's worth taking a look at parts of the documentation at some point so you can take advantage of all it has to offer.

History of R Financial Time Series Plotting. Although, as the name implies, this focuses on financial time-series graphics, it's also a useful look at various options for plotting any data over time. With lots of code samples along with graphics. By Timely Portfolio on GitHub.

Grouping & Summarizing Data in R. There are so many ways to do these tasks in R that it can be a little overwhelming even for those beyond the beginner stage to decide which to use when. This downloadable Slideshare presentation by analyst Jeffrey Breen from the Greater Boston useR Group is a useful overview.


R Instructor. This app is primarily a well-designed, very thorough index to R, offering snippets on how to import, summarize and plot data, as well as an introductory section. An "I want to..." section gives short how-to's on a variety of tasks such as changing data classes or column/row names, ordering or subsetting data and more. Similar information is available free online; the value-add is if you want the info organized in an attractive mobile app. Extras include instructional videos and a "statistical tests" section explaining when to use various tests as well as R code for each. For iOS and Android, about $5.


Comprehensive R Archive Network (CRAN). The most important of all: home of the R Project for Statistical Computing, including downloading the basic R platform, FAQs and tutorials as well as thousands of add-on packages. Also features detailed documentation and a number of links to more resources.

RStudio. You can download the free RStudio IDE as well as RStudio's Shiny project aimed at turning R analyses into interactive Web applications.

Revolution Analytics. In addition to its commercial Revolution R Enterprise, you can request a download of their free Revolution R Community (you'll need to provide an email address). Both are designed to improve R performance and reliability.

Tibco. This software company recently released a free Tibco Enterprise Runtime for R Developers Edition to go along with its commercial Tibco Enterprise Runtime for R engine aimed at helping to integrate R analysis into other enterprise platforms.

Shiny for interactive Web apps. This open-source project from RStudio is aimed at creating interactive Web applications from R analysis and graphics. There's a Shiny tutorial at the RStudio site; to see more examples, Show Me Shiny offers a gallery of apps with links to code.

Swirl. This R package for interactive learning teaches basic statistics and R together. See more info on version 2.0.

This article, 60+ R resources to improve your data skills, was originally published at as part of the Computerworld Beginner's Guide to R , which was written by Sharon Machlis and edited by Johanna Ambrosio.

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Sharon Machlis is Executive Editor, Online & Data Analytics at Computerworld, where she works on data journalism projects and in-house editor tools in addition to writing and editing.

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[Jun 20, 2015] The R Book (9780470973929) Michael J. Crawley Books

"...The book is best for people who have a rudimentary knowledge of R and want to learn about the many methods for analyzing data in R. The excellent breadth of coverage means that there is not a great deal of depth for any particular topic."

Hardcover: 1076 pages
Publisher: Wiley; 2 edition (December 26, 2012)

Format: Hardcover

ByKevin Wrighton October 21, 2014

Better name would be "Intro to data analysis using R"

1. The content of this book is similar to an earlier book by the same author (Statistical Computing: An Introduction to Data Analysis using S-Plus), but now capitalizing on the popularity of R The earlier book had a much more appropriate name, as the focus of the book is on data analysis (with R), not on R itself. People reading this book hoping to learn R will be disappointed. The book is best for people who have a rudimentary knowledge of R and want to learn about the many methods for analyzing data in R. The excellent breadth of coverage means that there is not a great deal of depth for any particular topic.

2. The R code makes extensive use of the "attach" function to attach data frames. This is nearly a cardinal sin, for it leads to a cluttered work environment. This is a carryover from the very ancient way of doing things in S-Plus. It's really not that hard to add "data= " arguments to the function calls, or use the "with" function.

3. Some people have had trouble finding the datasets used in the book. The general trend these days is that book authors often create an R package containing the data used in the book. I suggest this be done for any future edition of the book.

Bydrmrgdon September 21, 2013

Save you money...don't buy this one

This is by and large the worst book I've ever purchased for learning a new language and / or programming utility. I sorely regret buying this book, and wish I had searched a bit more thoroughly for a book that is better suited to helping me learn how to work with my data in R.

From the start, the author very quickly skims over many key topics without really going into detail on how to use them. Worse yet, he introduces functions and topics in command examples and formulas without explaining how they work until later in the chapter / book (and without a reference to the function). For example he uses 'tapply' on page 21 before explaining what it does (that doesn't happen until page 42), and there were several others that had me scratching my head. I could use the help function in R to see what the function does, but the help in R is completely cryptic to me and not terribly useful. The point of buying this book was to (hopefully) get a better idea of how these functions work and how to use them.

Understandably so, the author often imports datasets in order to show how to perform specific tasks. However, there is no obvious indication on where one can find these data, and there's no way to learn the material without actively following along. I did end up finding the datasets online after Google searching for them (the author apparently has a website with most (but not all!) of these data), and later on I ended up finding a note in the Acknowledgements section stating where the data is. Why is this not indicated in the "Getting Started" chapter in the "How to use this book" section? Further, not all of the data is located on his website, and I've come across several examples of missing datasets. As an example, I skipped ahead to the Classic Tests chapter to see what was going on there, and the first dataset referenced is called 'classic.txt', which is nowhere to be found. Guess I'll skip over that section without dissecting how the author is performing his analysis! One other piece of advice for anyone looking for these data: download all of the individual files rather than the .zip file linked. The zip file does not contain all of the individual files!

All of the author's material is strictly Windows based. There are no indications of comparable commands for Linux (and I'll assume this also holds true for Mac users). For example, the author occasionally uses the 'windows' command to scale a plot window. However, that is not a valid command in the Linux installation of R, and after searching for a while, I was able to find the equivalent command so that I can follow along again (it's 'X11()' by the way). Most of the other programming books I have read take into consideration that a user will be working on one of the big three operating systems and help guide you with the appropriate commands when it's relevant.

Overall it seems the author is more concerned with teaching statistics than how to use R and how to import, manipulate, and manage the data in R. I was hoping to learn more about actually using the language and learning how to work with my own datasets. But in the end (well not quite the end yet, I've only gotten to chapter 6 so far), I really don't have much of a better understanding than before I started of how factors, level, and vectors work, how to transform my data from a table (like generated from Excel or a Perl script) to R in order to work with it, and how to graphically represent these data the way I want. Amongst the missing data, the typos and bad commands, the only surface skimmed details of functions and commands, and mono-OS minded operations in the book, I'm still quite lost and don't expect to get a whole lot out of this book for the price. My money would definitely have been better spent elsewhere.

[Jun 12, 2015] Customer Reviews R Cookbook (O'Reilly Cookbooks)

Dimitri Shvorob on July 31, 2011

Great for beginners

You will be disappointed if you are a competent R programmer looking for "hacks". Note that the only negative review so far mentions its author's four-year experience with R. Mine is much shorter, yet I too find the book too "junior" for my needs. (With "R in Nutshell" and Google at my disposal, I can send "R Cookbook" back after making several notes to record what I learnt from it. There are several nuggets that you will not find in "R in Nutshell", or will not think to google). However, the book is not advertised as an "R in Depth", so no complaints.

"R Cookbook" is a friendly and highly informative introduction to "general-purpose" R (one half of the book) and doing basic statistics with R (the other half). A chapter on time series, with a look at "zoo" package, is a bonus; a somewhat light (but does-the-job) take on R graphics may be viewed as a downside, but I see the benefit of getting the basics right, and letting the reader explore other resources - I would recommend "R Graphs Cookbook" and Quick-R Web site - when he/she is ready.

Yes, there are free R tutorials out on the Web - but given that this one is widely praised and inexpensive (even if you are never going to resell it - and at some point you probably should, and move to "R in Nutshell" - $25 is not too much. How much saved time is worth $25 for you?), why not take a look?

PS. "R in Action" by Robert Kabacoff is another option, and one that I actually like better.

PPS. The second edition of Michael Crawley's "R Book" is a large improvement over the first, and is a stronger competitor to both "R Cookbook" and "R in Action".

Paulo C. Rios Jr. on May 14, 2015

A simple and masterful book with many insights - better than many textbooks on R

With clear and concise explanations covering most of the important areas, this is a brilliant book. It can not only save you plenty of time, but also help you to gain new insights as you learn from comprehensive but short and clear discussions of so many different topics. They are all explained in a such a way that you can directly access anyone of them without having to read the rest of the book. Some highlights:

What is missing? A coverage of RStudio, a great and free development environment for R. There is also a lack of any examples covering statistical learning data analysis other than linear regression which actually belongs more to standard statistical analysis.

But this is little in comparison to what it offers. This is a great book written in a masterful way not only in knowing the subject matter but also in knowing how to present it and teach it.

Data Science in R A Case Studies Approach to Computational Reasoning and Problem Solving (Chapman & Hall-CRC The R by Deborah Nolan (Author), Duncan Temple Lang (Author)

The Art of R Programming: A Tour of Statistical Software Design

R in Action Robert Kabacoff 9781935182399 Books

Best R book I ever read
By Junyu Lee on May 30, 2012
Format: Paperback

The reason I picked this book because I read a article in a Chinese R BBS which recomanded this book as the best book for beginner. I checked Amazon reviews, it only had 3.4. Someone even gave it one star. I was glad I didn't take these reviews serously. Just as the saying: one man's food is another man's poison. I found this book was right for me.

First, there is no book which can cover everything of R, even the very common used part. If there is one, it might be the most boring book in the world. Second, the part you use day in day out, might not be the part others use frequently. Third, a lot of stuff, you have to learn when you have a problem to solve. The simple example is, when I started to learn read.table, I have no idea what all these arguments were and how to use them. Later, I had problem, I gradually learned how to use skip, nrows, row.names. There are still a lot, such as check.names, I have never used so far and don't know and don't care. I don't think a book should list all these stuff. That would be super boring.

I had taught myself R using The R Book before I read this one. I found the best of book, it taught you something you could put to use right away. I especially like its statistic part, simple, clear, and staight forward. It covers a lot of stuff usually not covered by most of books. For example, it explains the output of the summary (lm(x~y)) which I wanted to know but felt too embarrassed to ask because I thought everybody knew except me.

This book is right for beginner like me. As I said, if you want to learn how to build a car, how the engine works before you learn how to drive a car, this book is not for you. If you simple want to learn just how to drive plus a little bit maintenance, totally don't care how engine works, this is the right book for you. By the way, The R Book is a really good book for the R part. The statistic part is too complicate for me. Two much engine manufacture stuff.

Learning R (9781449357108) Richard Cotton Books

There are better options now

By Dimitri Shvorob on November 1, 2013

Format: Paperback

The low rating reflects my opinion of the book vis-à-vis the alternatives, not its merits when viewed on its own. My original review was actually robust four stars;

I did point to a book which, in my opinion, was a better proposition than "Learning R" - "R in Action" by Robert Kabacoff - but acknowledged a few areas where "Learning R" had the edge.

Then "R for Everyone" by Jared Lander came out and, what can I say, I see "R in Action" and "R for Everyone" as the two plausible beginner-to-intermediate-R-book choices, and "Learning R" a very distant third.

R. J. Cotton1 year ago

Report abuse

Hi Dimitri,

Thanks for the thoughtful review.

The lack of statistical content was a deliberate design decision. Part of the reason that I wrote the book was that existing books introducing R were almost all focused on how to do statistics in R; they emphasized modelling at the expense of explaining data manipulation and programming. If you want to learn statistics at the same time as learning R, then I recommend The R Book by Michael Crawley.

Data visualization and graphics are also really well covered by other books. Paul Murrell's R Graphics is the definitive reference.

As for the arcana, there was a continual struggle between streamlining the book, and warning about R's quirks. Some people like knowing about weird design decisions, others just want bread-and-butter, so it's hard to please everyone.

In the end, it was a personal decision: I like quirks, so they stayed in.

Intro to the R language

By IADev on November 20, 2013

Format: Paperback

With a background as a developer, I found this book very approachable having not touched R before this book. It remained close to the R language itself and did not veer too deep into statistics.

For a programmer new to the language and the topic, I was fine with the subject matter depth. I really liked that the end of each chapter included a quiz as well as exercise portions. The answers are included as part of the Appendix and I think are a good reminder to readers to try out what they just learned. Part of learning new languages is finding about different concepts or how data structures are implemented with different names, functions, or packages. Newer topics to me were data frames and factors. These concepts are explained very well and allowed for me to quickly apply existing knowledge from other languages to make the concepts clear. In my opinion, I really liked the chapters involving getting data and on cleaning and transforming. These chapters are very practical and apply to anyone who has had to parse data in one format to put it into another. Topics like loading packages and data and time coverage are sections that you would expect to be in a book for beginners new to the language and hint at the audience this book is geared toward. It is a good starting point for those totally new to the language who will use this as a foundation to dig deeper for specific tasks later.

Overall conclusion: Good for the programmer interested in learning the R language over in-depth statistics.
Disclaimer: I got a copy of this book for review as part of O'Reilly blogger program.

Hands-On Programming with R Write Your Own Functions and Simulations (9781449359010) Garrett Grolemund Books

Wrong and bad choice of examples, uninteresting and pricey

ByPaulo C. Rios Jr.on May 9, 2015

Format: Paperback

The choice of a casino for nearly all the examples in the book is very annoying. There are so many better business cases and exciting public data available. What a poor choice. This isn't really hands-on. Almost nothing of the exciting applications of R in data science is actually shown. The examples are too simple and constrained. There is a lack of insight. And some kind of misdirected teaching.

The very long data frame in page 57, occupying half a page, with the entire deck of cards to display a bad idea is a sad example. The creation of a data frame with zero columns and rows in page 69 is another, but unfortunately not the only ones.

The use of R Studio is a great choice, but it is a very small positive point in front of the many shortcomings of this book.

The book is short, but very limited, with very bad examples and very pricey. Not recommended. There is a shortage of good R books in the market, but "R in Action" (second edition is coming), "The Art of R Programming" and "The R Book" are much superior choices.

Avoid "R for Everyone" as well.


By Dimitri Shvorob on August 2, 2014

Format: Paperback

In my mind, R programming is mostly about data manipulation and visualization. In this book, R programming is about procedural programming in R, with a quick glance at the language's OOP features. This places "Hands-On R Programming" between most R learning aids, which cover [most of] these things within a chapter - my recommendation in this field is Robert Kabacoff's "R in Action" - and Norman Matloff's "The Art of R Programming", which covers a lot more ground and has a lot more depth, while being just as accessible, and actually cheaper. I have no complaints about the author's execution of his plan for the book - put simply and positively, I like his writing - but am pessimistic about the plan itself, and do not see the book as a worthwhile investment of $30+.

Great R Book using the newer developments in R language such as RStudio

By Marc Telesha on February 5, 2015

Format: Kindle Edition Verified Purchase

Another great resource for learning R. While it is frustrating that all these books cover the same basic information they all cover it slightly differently. This book coming from the RStudio's chief trainer is a well designed book which covers many aspects not covered as well as other books.

R as a programing language has also evolved so much over the past 5 years that I find that the newer books are a better start for beginners, not that the classics should be skipped. This book has a cleaner narrower focus and is a great fit for someone new to R. It uses less libraries and the libraries it uses are clean and make working with R easier. Also I couldn't imagine working with R without using RStudio and this book also shows short cuts on the language's best IDE that is free for personal use.

My suggestion is that someone with little to no experience programing should maybe get two books to learn R.

1) R for Everyone by Jared P. Lander (Though the font for the code in Kindle is frustrating because it doesn't show symbols correctly unless you copy and paste the code!)

2) Hands-On Programming with R by Garrett Gromlemund.

Than after under standing these books and maybe doing a few free online courses get

1) The Art of R Programming by Norman Matloff and

2) R in Action by Robert Kabacoff (Only available for sale at the publishers website for the EBook)

What I want is a second book on using Hadley Wickham's libraries as a second book. Using Reshape2, ddplyr, tidyr, stringr, tidyr, ggplot2, ggvis and shiny.



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