Using R language with Anaconda

With Anaconda, 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.


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

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 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


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

Using MRO with Anaconda

If you prefer to use the Microsoft R Open (MRO) platform with Anaconda, as opposed to R, you can switch the default R interpreter from R to MRO. To get MRO, you need to explicitly include mro-base. Anaconda will maintain an archive of MRO packages but will not update MRO packages. Support for MRO packages will be on a case-by-case basis.

If you are using MRO, it is recommended to migrate to R. Follow the migration directions.

Switch the default R interpreter from R to MRO

Run conda info and check your version of conda. If your version of conda is below 4.6, run conda update conda to update conda to the latest version.


conda config --system --set pinned_packages _r-mutex=*=anacondar*

The default R interpreter will switch from R to MRO.

To learn more about how to use MRO with Anaconda, see Using MRO language with Anaconda.


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.