60+ R resources to improve your data skills

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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.

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.

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.

r4stats.com. 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, including 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 RPubs.com, 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.

More free downloads and websites from academia:

Introducing R. Slide presentation from the UCLA Institute for Digital Research and Education, with downloadable data and code.

Introducing R. Although titled for beginners and including sections on getting started and reading data, this also shows how to use R for various types of linear models. By German Rodriguez at Princeton University's Office of Population Research.

R: A self-learn tutorial. Intro PDF from National Center for Ecological Analysis and Synthesis at UC Santa Barbara. While a bit dry, it goes over a lot of fundamentals and includes exercises.

Statistics with R Computing and Graphics. Unlike many PDF downloads from academia, this one is both short (15 pages) and basic, with some suggested informal exercises as well as explanations on things like getting data into R and statistical modeling (understanding statistical concepts like linear modeling is assumed). By Kjell Konis, then at the University of Oxford.

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 Institute, 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 Meetup.com. 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.


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.


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