🧪 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

💡 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.

🔧 Step 1: Install {renv}

First, install {renv} if you haven't already:

install.packages("renv")

🏗️ Step 2: Initialize a Project Environment