Using R language with Anaconda

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.

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.

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Updating R packages

• 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

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Resources

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