Demystifying R Part 2: An introduction to coding in R and RStudio

Date & Time
Monday, September 4, 2023, 4:00 PM - 5:30 PM
Location Name
Session Type
Workshop - training
Evidence synthesis innovations and technology
Target audience
information specialists, review authors
Level of difficulty

Background: Tools like R and Python are becoming increasingly useful in the conduct of systematic reviews and evidence synthesis. In R specifically, many tools have been developed to facilitate the systematic review process. Some of these tools provide vignettes and examples to help novice coders make use of the tools in a coding environment like RStudio, and others have graphical user interfaces that make them accessible to users without coding experience.
Objectives: Building on Demystifying R Part 1 (workshop proposal submitted separately by Elke Hausner), this workshop will introduce learners to the basics of the R coding language and the RStudio coding environment, in the context of litsearchr, an R package that supports term harvesting, Boolean search construction, and search strategy testing, among other steps.
Description: We will draw from recently developed Library Carpentry curriculum ( to provide a live coding, fully hands-on session. The workshop will cover the basics of RStudio, creating objects and variables, working with packages and libraries, and best practices for code documentation. This workshop is geared towards participants with no coding experience. Taking Part 1 of this series is optional but recommended. Participants will learn basic concepts of working with files, folders, and objects in R and RStudio, will understand different data structures, and will be introduced to litsearchr as a tool that can be incorporated into their systematic review workflows. Using open-source tools like R for evidence synthesis allows for more reproducible methods, expediting the evidence synthesis process, and improving transparency. Thus, this workshop will contribute to improved patient care through the facilitation of more efficient and transparent evidence synthesis products.

Grames E1
1University of Nevada at Reno, United States