Demystifying R: An Introduction for Librarians
If you work with researchers or stay up-to-date on “big data” or data science, you have probably heard of R. But what exactly is it, and why should librarians learn it? This webinar will help demystify this popular programming language and provide some real-world examples of how it can help librarians in their daily work. Whether it’s for assisting patrons with their research data or working with your own library data, R can be a useful skill to add to your librarian toolboxes. This webinar will provide an introduction to R, including how it can be used for data processing, visualization, and analysis of a variety of different types of data. We will also discuss some key terminology and concepts to get you started and provide you with resources for learning more about R.
Objectives
Attendees will learn:
- what the R programming language is and some of its key features
- some key terminology and a basic understanding of how R works
- some uses for that R may be a good solution for your data needs, including data processing and management, visualization, and statistical analysis
- how R can be useful for working with research data, as well as with library data, including bibliometric data, library statistics, or budget data
- where to find free resources for learning R
Presenter
Lisa Federer, AHIP, currently serves as research data informationist at the National Institutes of Health Library, where she provides training and support in the management, organization, sharing, and reuse of biomedical research data. She is the author of several peer-reviewed articles and the editor of the forthcoming Medical Library Association Guide to Data Management for Librarians. An active member of MLA, she currently chairs the Medical Informatics Section and the Lucretia W. McClure Excellence in Education Award Jury. She holds a master’s of library and information science from the University of California–Los Angeles and graduate certificates in data science (Georgetown University) and data visualization (New York University), and she is a doctoral student at the University of Maryland.