Introduction to Collaborative and Reproducible Research Practices with R -- 4-Part Series

Description

This four-day workshop series (9/12, 9/13, 9/14, 9/15) provides an introduction to coding, with an emphasis on leveraging open source tools to develop workflows for collaborative and reproducible research. The goal of this series is to increase research integrity via exposure to basic command line tools and version control (Git), collaborative cloud tools (GitHub), programming (R), and data and software management best practices. Materials build across the sessions. All UC Davis graduate students and postdocs with little to no prior programming experience are eligible to apply.

Workshop series dates and time: September 12, 13, 14, and 15, 10:00 AM – 3:00 PM, with a break for lunch.

Optional: Data Challenge (following each session) 3:00 PM – 5:00 PM

Workshop Series Schedule

Day 1: Collaborative and Reproducible Research Overview and Introduction to the Command Line.

Day 2: Git and GitHub.

Days 3 & 4: R Basics. At the end of each session the learners are encouraged to continue practicing their skills by engaging in the fall Data Challenge. This series is scheduled for in person participation only and seats are limited.

Recordings of prior similar workshops are available in the DataLab training archive (https://datalab.ucdavis.edu/workshops/). Completion of all sessions and corresponding assessments is required to obtain badges for the GradPathways Research Computing credential (https://gradpathways.ucdavis.edu/research-computing-pathway).   

Learning Objectives

By the end of this series, learners will be able to:

• Describe tidy data, project organization, and programming best practices;
• Explain the directory structure of their computers and use command line tools to create, copy, edit, and delete files;
• Create new repositories and begin using Git for version control of their individual projects;
• Push local changes to a repository on GitHub, open and merge pull requests, and create issues for project management;
• Load tabular data sets into R, compute simple summaries and visualizations, do common data-tidying tasks, write reusable functions;
• Identify where to go to learn more.

Prerequisites: None      

Software: R, Git. If you are using Windows, you will need to install additional software prior to the start of the workshop. Instructions can be found in the DataLab Install Guide (https://datalab.ucdavis.edu/install-guide/).

Instructors: Pamela Reynolds, Oliver Kreylos, Tyler Shoemaker, Nick Ulle, Wesley Brooks