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The purpose of this guide is to present resources for both the discovery and utilization of research data and statistics.

**R** is a free, open source program that is both powerful and flexible. However, it has a high learning curve when compared to other statistical and data analysis software.

**RStudio** is an optional (but useful) program that provides additional functionality to R, along with a much more comfortable user interface. RStudio is highly recommended for most first-time R users.

*Availability:* Free and open source for individual users; All students, faculty, and staff via Kenyon's Virtual Computer lab

- Download RStudio Desktop
*Note: You must have R installed in order to use RStudio*

- Statistical Inference Via Data Science by Chester Ismay and Albert Y. KimA very gentle starting point for anyone without a data science or coding background who wants to learn R. Primarily works in RStudio instead of R.

- Quick-RFor those with an existing knowledge of statistical methods, and possibly a familiarity with another statistical package, Quick-R aims to get you started in R quickly.
- RStudio CheatsheetsQuick reference guides on a number of topics, ranging from basics like visualization and tidying to advanced like interactive web apps and web APIs.

- The R ProjectThe developers/publishers of the R statistical software environment.
- R PackagesA comprehensive list of packages for R, add-ons that extend its functionality. For the most part, though, packages are installed using R or RStudio directly.
- The R JournalOpen access, refereed journal of the R project for statistical computing.

A great tutorial, but pretty long. If you're looking for something a bit shorter try this one: https://youtu.be/fpl_ny-jX5Y

- Modern Data Science with R by Baumer, Horton, & KaplanCall Number: QA76.9.D343 B38 2017Publication Date: 2017
- Discovering Statistics Using R by Field, Miles, & FieldCall Number: HA32 .F537 2012Publication Date: 2012

- URL: https://kenyon.libguides.com/data

Subjects: Statistics