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Indiana University Bloomington

Workshop in Methods (WIM)

The Workshop in Methods (WIM) was created in 2009 with the mission of providing introductory education and training in sophisticated research methods to graduate students and faculty in the social sciences at Indiana University. Our goal is to supplement statistics and methods courses across the Bloomington campus with topical workshops led by leading methodological scholars from IU and across the United States.

WIM is currently directed by Stephen Benard, working with the WIM advisory committee and the Social Science Research Commons. The initial idea for WIM began with Scott Long, who discussed his vision with Dr. William Alex Pridemore. Pridemore created WIM and directed the series until 2013.

If you would like to receive updates from WIM – including announcements of upcoming presentations – you can request to join our mailing list by sending an email to

Spring 2017 Upcoming Workshops

For materials from past workshops, visit the archive page


Friday, April 21, 2017

Jefferson Davis, Introduction to R

2-3:30pm, Social Science Research Commons Grand Hall (Woodburn Hall 200)


R is a flexible, free software language for statistical computing and visualizations. Its popularity is increasing across a broad range of disciplines. This workshop will provide and introduction to using R including

  • Downloading R and where to find R on IU computers
  • Basic R syntax
  • The Rstudio environment
  • Creating and importing data
  • Producing and editing graphs
  • Using statistical techniques such as t-tests, simple linear regressions, and mixed models.

No prior knowledge of R is assumed. We do, however, recommend that if you are using your own laptop that you download R and RStudio from the following links:

Jefferson Davis has worked in Research Analytics for over ten years, and has extensive experience with R, Matlab, and other numerical packages. Some of Jefferson's recent projects have included: developing interfaces for computational text analysis; cluster analysis of institutional "big data" sets; and numerical simulations of resource management in fisheries.