Activating Conda Environment Inside R Script

Introduction

As a programmer, it’s common to work with multiple environments and packages across different languages. In this article, we’ll explore how to activate a Conda environment inside an R script. We’ll delve into the world of Conda, R, and Python to provide a comprehensive guide on how to achieve this.

Background

Conda is an open-source package manager that allows you to easily manage dependencies for your projects. It provides a convenient way to create, manage, and switch between multiple environments. An environment in Conda refers to a self-contained collection of packages, libraries, and other dependencies.

R is a popular programming language used extensively in data science, statistics, and machine learning. While R has its own package manager, CRAN (Comprehensive R Archive Network), it’s often useful to use Conda environments for R projects as well.

In this article, we’ll explore how to activate a Conda environment inside an R script and demonstrate the process using Python and R scripts.

Setting up Conda Environments

Before we dive into activating Conda environments inside R scripts, let’s set up two Conda environments: one for Python and another for R. We’ll create these environments using the conda command-line tool.

# base python env:
conda create -n conda_python -c conda-forge python

# base r env + httr:
conda create -n conda_r -c conda-forge r-base r-httr

Executing Python and R Scripts

We’ll now execute the Python script using Conda and invoke the R script from within it.

# testpy.py

import os
import subprocess
print('CONDA_PREFIX (py) :', os.environ['CONDA_PREFIX'])
subprocess.call(['conda', 'run',  '-n', 'conda_r', 'Rscript', 'testr.R'] )

Activating Conda Environments Inside R Script

Now that we’ve set up our Conda environments, let’s explore how to activate them inside an R script.

Using `conda run` Command

We can use the conda run command to invoke a Python script and execute a R script within it. Here’s how you can do it:

# testr.R

message("-- R --")
message("CONDA_PREFIX (R)  : ", Sys.getenv("CONDA_PREFIX"))
message("R .libPaths()     : ", .libPaths())
resp &lt;- httr::GET("https://api.stackexchange.com/2.3/info?site=stackoverflow")
message('Questions in SO :', resp$questions)

When we run this Python script, it will invoke the R script and print its output.

As an Alternative, Using Conda Nested Activation

Another way to activate Conda environments inside an R script is by using nested activation. We’ll modify our Python script to use nested activation:

# testpy_noconda.py

import os
import subprocess
print('CONDA_PREFIX (py) :', os.environ['CONDA_PREFIX'])
subprocess.call(['Rscript', 'testr.R'] )

We can then activate both environments using the --stack option and run this modified Python script:

# Run testpy_noconda.py with --stack option

(base): conda activate conda_python
(conda_python): conda activate --stack conda_r
(conda_r): python testpy_noconda.py

In this example, the --stack option allows us to activate both environments and run the Python script.

Conclusion

Activating a Conda environment inside an R script can be achieved using the conda run command or nested activation. Both methods provide flexibility and convenience when working with multiple environments and packages across different languages. By following this guide, you’ll be able to create and manage your own Conda environments for Python and R projects.

Example Use Case

Suppose you’re working on a data science project that requires both Python and R scripts. You’ve set up two Conda environments: one for Python and another for R. To activate the R environment inside your Python script, you can use either method described above:

Using conda run command:

subprocess.call([‘conda’, ‘run’, ‘-n’, ‘conda_r’, ‘Rscript’, ’testr.R’] )

*   Using nested activation:
    ```markdown
subprocess.call(['Rscript', 'testr.R'] )

By activating the R environment inside your Python script, you can leverage the strengths of both languages and efficiently manage dependencies for your project.

References

Conda documentation: https://docs.conda.io/en/latest/user-guide/index.html
CRAN (Comprehensive R Archive Network): https://cran.r-project.org/
HTTR package in R: https://github.com/jaredl/httr

Last modified on 2023-09-01