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Free and Subscription Based Tutorials

 - 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!)

 - 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.

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

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

Online Reference Material including Open Source Books

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

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

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

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

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

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

James et al. (2014)  - 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)  - Link is the companion website to the book and is designed primarily for intermediate R users and programmers from other languages.

Lavine (2013)  - 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.

Center for Analytics and Data Science

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