Introduction to R for Libraries

These are the files associated with a webinar on "R for Libraries," delivered in May 2018 & sponsored by the Association for Library Collections & Technical Services. It includes step-by-step instructions in PDF for getting started using R, including sample datasets of print and e-books to allow users to follow along and practice the functions introduced in the sessions. Session information is available at http://www.ala.org/alcts/confevents/upcoming/webinar/050918.Session 1: The first session will include what R is, reasons to use R over other data tools, getting used to a command line software, and how to install R and R Studio, an orientation to the R Studio software, the data structures in R (e.g. numeric data, text string, factors).Session 1 recording: https://youtu.be/-uf8II9Sy7wSession 2: The second session will demonstrate how to enter commands, read data and do some basic exploration, common troubleshooting issues in R and how to interpret error messages.Session 2 recording: https://youtu.be/SS16WKFvOl4Session 3: The third session will go deeper into data exploration and manipulation, including subsetting data, creating and renaming variables, creating a basic visualization, installing packages, and a brief overview of more complex operations one can run in R.Session 3 recording: https://youtu.be/lMyscN_Dd5sLibraries now have access to an abundance of data reflecting multiple facets of our collections, users, and services. Examples include collections data pulled from the ILS, ebook and database usage data, university enrollment data, and local user demographic data. This data is often large, untidy, and difficult to work with in traditional spreadsheet software. "R" is a free and open source programming language used for data manipulation, analysis, and visualization. It is more powerful and flexible than spreadsheet software and can run complex operations in a fraction of the time. This webinar will provide a soft introduction to working with data using R in the R Studio software environment. Participants will learn how to use R Studio, read in and explore datasets, understand error messages, execute some simple manipulations such as subsetting and renaming, and create a basic visualization.