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

Compare three versions of the Conda engine to help you pick the best one for your technical skills and computer type. You can choose Anaconda Navigator for a beginner-friendly, "point-and-click" experience, or go with Miniconda or Miniforge if you prefer a lightweight, professional setup that saves disk space. While they differ in size and interface, all three create isolated "sandboxes" to ensure your software projects don't interfere with each other.

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.

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.

Conda channels act like specialized "app stores" or online libraries where you can find and download different software packages for your projects. This article explains how to choose between major stores like the curated Anaconda Defaults or the massive, community-run Conda-Forge. It also covers how to set a "priority" for these stores so your computer always knows which one to check first to keep your software stable and up-to-date.

Information on how to order software from UM DoIT Software Sales.

Index of Advanced Topics for Conda, Python, and R

Export a Conda Environment to Share Across OS Platforms

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 for Managing Integrated R & Python Projects with Conda

Index of Miniconda How-To Articles for Virtual Conda Environments

Integrating R and Python on Windows can be notoriously finicky. The rpy2 library bridges this gap beautifully, but installing it via traditional methods often triggers a cascade of compilation headaches. Using Conda eliminates this friction by managing pre-compiled binaries and dependencies smoothly.

Install Miniconda as user without administrator permissions in Windows.