How-To: Getting Started With R/RStudio & Python in Conda Enviroment

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.

How do I quickly get started using R/RStudio and Python together on a University-managed computer?

1. Install Miniconda. For most usage cases a default installation as performed as your non-administrative user will work best.

How-To: Install Miniconda Without Administrator Privileges in Windows.

How-To: Install Miniconda Without Administrator Privileges on a Mac.

2. Install R.

How-To: Install R From Company Portal

3. Install Rstudio.

How-To: Install RStudio From Company Portal

4. Create a unique Conda environment for each project to avoid dependency conflicts and ensure portablility

How-To: RStudio: Create Conda Environment for Using RStudio

5. Add channels to increase the number of available packages and versions.

How-To: Add Channel to Top of List in a Virtual Conda Environment via Command Line Interface.

How-To: Add Channel to Bottom of List in a Virtual Conda Environment via Command Line Interface.

6. Add R and Python packages to the Conda environment as needed.

How-To: Using Anaconda - Install an R Package in a Virtual Conda Environment via Command Line Interface

How-To: Install a Package in a Virtual Conda Environment via Command Line Interface.

7. Launch RStudio in an activated Conda environment from the command line.

 

How-To: RStudio: Launch RStudio in Conda Environment From Command Line

8. Use reticulate and rpy2 packages for cross language object utilization.

R projects with embedded Python code:

How-To: RStudio: Use Reticulate Package to Manage R-Python Integration

Python projects with embedded R code:

How-To: Use rpy2 Package to Manage Python-R Integration

9. Create backups to keep your work reproducible.

How-To: Backup a Virtual Conda Environment Configuration via Command Line Interface.

 

Informational: Best Practices for Managing Integrated R & Python Projects with Conda