Online Resources

Of course you could search online yourself for resources on how to program in R, but we thought we'd go ahead and share a few that we find useful. Some will provide content mainly for the very beginner while others present higher level topics geared more for those with prior coding experience and are just now learning R.

Free and Subscription Based Tutorials

Try R - Highly recommended for beginners. You can setup an online account with Code School, but is not necessary. Requires no installation of software and is a great introduction to the R language and using R as a tool for statistics and data modeling. Other free Code School courses are also offered by the linked website as well as others with a paid subscription (Note: completion of the free course may provide you a discounted rate!)

swirl - Free R package that can be downloaded and installed so that the user can learn R programming at their own pace using the R console. This is a great follow-up to TryR mentioned above and add-on modules can be installed (See Step 5 provided at the linked website). Material from the add-on module 'Statistical Inference' is similar to material covered in ISA 205, except you use R programming to learn the concepts.

RStuido/Shiny Webinars - All events are free, routinely updated, cover a variety of subjects and products including Open Source and Commercial.

R DataCamp - Free intro level courses with intermediate to advanced level requiring paid subscription.

Online Reference Material including Open Source Books

R WIKIBOOKS - free online source that has contents listed on the right of the page and can be searched for content using the provided search box

RStudio Cheat Sheets - Downloadable and printable reference guides for Base & Advanced R, RStudio IDE, R Markdown, Shiny, Data Viz, Package Development, among others

R Style Guide - Whether you are just starting to code or consider yourself an expert, this site suggests how to write readable, maintainable code

Faraway (2002) Practical Regression and ANOVA using R - Provides basic mathematical theory behind regression using R code and real datasets to explain the concepts

James et al. (2014) An Introduction to Statistical Learning with Applications in R - Overview of statistical learning including important modeling and prediction techniques, along with relevant applications. Authors assume reader has had a previous course in linear regression and no knowledge of matrix algebra. Datasets are also accessible via their website.

Wickham, H. (2014) Advanced R - Link is the companion website to the book and is designed primarily for intermediate R users and programmers from other languages.

Lavine (2013) Introduction to Statistical Thought - intended as an upper level undergraduate or introductory graduate textbook for students with a good knowledge of calculus. Focuses on mathematical statistics and explores real datasets using R with lines of code explained in detail. Links to download a pdf of the book and associated datasets can be found on the linked website.