Online Resources

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.

RStudio/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.

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

R Manuals - Documentation from CRAN, the online repository for just about everything R

Grolemund and Wickham (2017) R for Data Science - open sourse eBook written for R begginers

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.