Using R language with Anaconda#

With Anaconda (or Miniconda), you can easily install the R programming language and over 6,000 commonly used R packages for data science. You can also create and share your own custom R packages.

Note

When using conda to install R packages, you will need to add r- before the regular package name. For instance, if you want to install rbokeh, you will need to use conda install r-rbokeh or for rJava, type conda install r-rjava.

The R Essentials bundle contains approximately 200 of the most popular R packages for data science, including the IRKernel, dplyr, shiny, ggplot2, tidyr, caret, and nnet. It is used as an example in the following guides.

R is the default interpreter installed into new environments. You can specify the R interpreter with the r-base package. Unless you change the R interpreter, conda will continue to use the default interpreter in each environment.

To run the commands below on Windows, use Start - Anaconda Prompt. On macOS or Linux, open a terminal.

Updating R packages#

  • Update all of the packages and their dependencies with one command:

    conda update r-caret
    
  • If a new version of a package is available in the R channel, you can use conda update to update specific packages.

Creating and sharing custom R bundles#

Creating and sharing custom R bundles is similar to creating and sharing conda packages.

EXAMPLE: Create a simple custom R bundle metapackage named “Custom-R-Bundle” that contains several popular programs and their dependencies:

conda metapackage custom-r-bundle 0.1.0 --dependencies r-irkernel jupyter r-ggplot2 r-dplyr --summary "My custom R bundle"

Share the new metapackage by uploading it to your channel on anaconda.org:

conda install anaconda-client
anaconda login
anaconda upload custom-r-bundle-0.1.0-0.tar.bz2

Anyone can now access your custom R bundle from any computer:

conda install -c <your anaconda.org username> custom-r-bundle

Creating an environment with R#

  1. Download and install Anaconda.

  2. Create a new conda environment with all the r-essentials conda packages built from CRAN:

    conda create -n r_env r-essentials r-base

  3. Activate the environment:

    conda activate r_env

  4. List the packages in the environment:

    conda list

The list shows that the package r-base is installed and r is listed in the build string of the other R packages in the environment.

Anaconda Navigator, the Anaconda graphical package manager and application launcher, creates R environments by default.

Creating a new environment with R#

When creating a new environment, you can use R by explicitly including r-base in your list of packages.

With conda 4.6:

conda create -n r-environment r-essentials r-base
conda activate r-environment

Mirroring the R channel#

Many Enterprise customers maintain a local mirror of the R channel.

When mirroring the R channel for the first time, clean the existing packages by running the command anaconda-server-sync-conda with the option --clean.

Uninstalling R Essentials#

To uninstall the R Essentials package, run: conda remove r-essentials

Note

This removes only R Essentials and disables R language support. Other R language packages are not removed.

Resources#

Here are some additional resources on using Anaconda with the R programming language:

  • R Language packages available for use with Anaconda–There are hundreds of R language packages now available and several ways to get them.

  • Navigator tutorial–Use the R programming language with Anaconda Navigator. The Anaconda Navigator graphical interface (GUI) makes it easy for even new users to use and run the R language in a Jupyter Notebook.

  • Webinar: Anaconda for R Users–Download the slides from the webinar to see how Anaconda makes package, dependency and environment management easy with R language and other Open Data Science languages.