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đź§Ş Working with Virtual Environments in R Using
A tutorial about {revn}
When developing R projects, it’s common to run into version conflicts or package compatibility issues. One project might require ggplot2 v3.3.6
, while another depends on v3.4.0
. If you’ve experienced this, you’ll understand why virtual environments are essential.
Luckily, R has a powerful solution: the {renv}
package.
In this blog post, you’ll learn:
- What virtual environments are in R
- How to create and manage them using
{renv}
- How to share a reproducible R environment with others
đź’ˇ Why Use Virtual Environments in R?
A virtual environment is an isolated space where a project can have its own package library, independent of your global R installation. This makes your project:
- Reproducible: Anyone can install the exact same packages and versions.
- Safe: Changes in one project won’t break another.
- Portable: Easily share with colleagues or deploy to servers.
đź”§ Step 1: Install {renv}
First, install {renv}
if you haven't already:
install.packages("renv")