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Conda acts as a smart organizer for your coding projects, ensuring that all the necessary software "ingredients" work together perfectly without breaking your computer's setup. It supports both Python and R, making it easy to manage complex tools and share your exact environment so your work runs reliably on any machine.

To make your work reliable and easy to share, this article recommends using a special blueprint file (called environment.yml) to list all the software tools your project needs. By using this file, anyone can recreate your exact workspace with a single command, ensuring your code runs the same way on every computer. Keeping your projects in their own separate folders and "freezing" your settings when finished prevents future updates from breaking your work.

Think of Conda as a way to create separate, private "workspaces" on your computer for different projects so their software settings don't get mixed up or cause errors. While other tools only handle one coding language, Conda is a "heavy hitter" that can manage almost any software component, making it the top choice for complex fields like Data Science and AI. By using these isolated spaces, you keep your computer stable and make it easy to share your exact setup with others.

Best Practices for Managing Integrated R & Python Projects with Conda

Using VS Code as Integrated Development Environment (IDE) in Conda Environment for Python and R Development - Mac

Using VS Code as Integrated Development Environment (IDE) in Conda Environment for Python and R Development - Windows

Quick start guide to using R, Rstudio and Python together on University-managed computers including installation without administrator credentials, utilization of unique conda environments for each project, adding additional channels for packages, adding R and Python packages to the environment, launching Rstudio, utilizing the reticulate R package and backing up configurations for portability and reproducibility.

Best Practices and Managing Library Paths and Packages in RStudio (User-Level Installation Only)

Best practices for managing portable and reproducible R code across OS platforms.

Quick start guide to using Rstudio on University-managed computers including installation from Company Portal, utilization of creating individual renv environments for each project, adding additional packages and creating snapshots of configurations for portability and reproducibility.

A collection of best practices and how-to guides for managing R workflows.

Launching RStudio from a specific Conda environment ensures that your R sessions use the exact package versions, dependencies, and R executable isolated within that environment.

Index of Miniconda How-To Articles for Virtual Conda Environments

Verify Rstudio is using Rtools that was installed in user profile.

Index of R/RStudio guides