&# Ensuring-reproducibility-with-renv
This repository provides materials for a session that is part of the I2DS Tools for Data Science workshop run at the Hertie School, Berlin in October 2024. The student-run workshop is part of the course Introduction to Data Science taught by Simon Munzert at the Hertie School, Berlin, in Fall 2024.
In this session, we’ll focus on the importance of reproducibility in data science and how the renv package in R can help achieve it. Reproducibility allows others to replicate your findings transparently.
We'll explore how renv manages project environments by isolating dependencies and ensuring consistent package versions. You’ll learn to set up and manage R projects with renv, integrate version control with Git, troubleshoot common issues, and maintain a reproducible workflow.
The goals of this session are to:
Develop a comprehensive understanding of reproducibility challenges in data science and how renv addresses them. Provide practical knowledge on initializing renv, managing package versions, and sharing environments. Equip participants with the skills to troubleshoot common reproducibility issues, fostering smooth collaboration across different environments.
The session is accompanied by a tutorial, which can be accessed using this link.
- Matheus Galiza
- Mika Moeser
- Sattiki Ganguly
- https://rstudio.github.io/renv/
- https://cran.r-project.org/web/packages/renv/vignettes/renv.html
- https://github.com/r-lib/rig
The material in this repository is made available under the MIT license.
Matheus Galiza prepared the practice material.
Sattiki Ganguly prepared the presentation slides and recording.
Mika Moeser edited the slides, processed and presented the video.