With Anaconda (or Miniconda), you can 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, 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 Anaconda Prompt (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: 
    conda update r-caret
  • If a new version of a package is available in the R channel, update specific packages by running the following command: 
    conda update

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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: 
    conda metapackage custom-r-bundle 0.1.0 --dependencies r-irkernel jupyter r-ggplot2 r-dplyr --summary "Custom-R-Bundle"
  2. Upload the new metapackage to your channel on anaconda.org by running the following commands: 
    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 by running the following command: 
# Replace <USERNAME> with your anaconda.org username
conda install --channel <USERNAME> custom-r-bundle

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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 by running the following command: 
    conda create --name r_env r-essentials r-base
  3. Activate the environment by running the following command: 
    conda activate r_env
  4. List the packages in the environment by running the following command: 
    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.

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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: 
# Replace <ENV_NAME> with a name for your R environment
conda create --name <ENV_NAME> r-base r-essentials
conda activate <ENV_NAME>

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Uninstalling R Essentials

Uninstall the R Essentials package by running the following command: 
# Replace <ENV_NAME> with the name of the R environment
conda activate <ENV_NAME>
conda remove r-essentials
This removes only R Essentials and disables R language support. Other R language packages are not removed.

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Resources

Hundreds of R Language packages are available for use with Anaconda with several ways to get them.