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End of Support — As of November 4, 2025, the Anaconda R channel is no longer actively maintained; it is End of Support under Anaconda’s Support Policy. No new R versions, package updates, or security fixes will be provided. The channel and its contents remain available for historical reference, and existing environments will continue to function. These packages do not receive updates and might contain known, unpatched vulnerabilities, so assess them against your own security requirements before use. See our announcement for more information. Customers with a written agreement with Anaconda should refer to that agreement and the Support Policy for the support terms that apply to them.
With Anaconda (or Miniconda), you can install the R programming language and over 6,000 commonly used for data science. You can also create and share your own custom R packages.
When using conda to install R packages, add r- before the regular package name. For instance, to install rbokeh, use conda install r-rbokeh. To install rJava, use 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. The version of the R interpreter installed into your R environments is based on the version of the r-base package.
Run the commands in the following sections in (Terminal on macOS/Linux).

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Updating R packages

Exercise caution when using conda to update RStudio or other R packages to their latest versions. This might break your conda RStudio environment.
  • Update all of the packages and their dependencies by running the following command:
  • If a new version of a package is available in the R channel, update specific packages by running the following command:

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Creating and sharing custom R bundles

Creating and sharing custom R bundles is similar to creating and sharing conda packages. In the following example, we will create a simple custom R bundle metapackage named “Custom-R-Bundle”.
  1. Create the metapackage “Custom-R-Bundle” that contains several popular programs and their dependencies by running the following command:
  2. Upload the new metapackage to your channel on anaconda.org by running the following commands:
Anyone can now access your custom R bundle from any computer by running the following command:

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Creating an environment with R

  1. Download and install Anaconda.
  2. Create a new with all the r-essentials conda packages built from CRAN by running the following command:
  3. Activate the environment by running the following command:
  4. List the packages in the environment by running the following command:
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.

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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:

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Uninstalling R Essentials

Uninstall the R Essentials package by running the following command:
This removes only R Essentials and disables R language support. Other R language packages are not removed.

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Available R packages

The R language packages are available to install with conda at repo.anaconda.com/pkgs/r. Many Comprehensive R Archive Network (CRAN) packages are available as conda packages, but Anaconda does not provide builds of the entire CRAN repository, so some CRAN packages are not available. Anaconda’s R packages use the r- prefix, while CRAN packages do not. To install an R package into your current environment, run:
You can also search for an R package by name with conda search <PACKAGE_NAME>. For example, search for the rbokeh package with conda search r-rbokeh.

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