Using R language with Anaconda
With Anaconda you can easily install the R programming language and over 80 of the most used R packages for data science, and easily create and share your own custom R packages.
The R Essentials bundle contains the IRKernel and more than 80 of the most popular R packages for data science, including dplyr, shiny, ggplot2, tidyr, caret and nnet.
As of early 2018, the default R interpreter installed into new environments is
MRO. You can specify the MRO interpreter with the
mro-base package or the R
interpreter with the
r-base package. Unless you request a change, conda
will continue to use the existing interpreter in each environment.
To run the commands below on Windows use Start - Anaconda Prompt. On macOS or Linux open a Terminal.
- 64-bit systems only for all operating systems—Windows, macOS and Linux.
- Windows 7.0 SP1, Windows 8.1, Windows 10, Windows Server 2008 R2 SP1 and Windows Server 2012.
- Linux—CentOS, Red Hat Enterprise Linux, Debian and Ubuntu.
Anaconda with MRO is not currently supported on macOS but will soon be supported on macOS El Capitan (10.11) and later.
Anaconda with R is supported on macOS Yosemite (10.10) and later.
Create a new conda environment with MRO and all the r-essentials conda packages built from MRAN:
conda create -n mro_env r-essentials
Activate the environment:
conda activate mro_env
List the packages in the environment:
The list shows that the package
mro-base is installed and
mro is listed
in the build string of the other R packages in the environment.
When using MRO conda packages, starting the R interactive interpreter shows Microsoft R Open in the startup message.
Anaconda Navigator, the Anaconda graphical package manager and application launcher, also creates MRO environments by default. You may instead select R when creating a new conda environment from within Navigator.
Microsoft R Client is a free, community-supported data science tool for high performance analytics built on top of MRO. Additionally, R Client introduces the powerful ScaleR technology and its proprietary functions to benefit from parallelization and remote computing.
Microsoft R Client is now available as a conda package (
of Windows or RHEL-7/CentOS7/Ubuntu 14.04 and above also have the option to
conda install the MicrosoftML R package for machine learning
r-mrclient-mml) and the pre-trained models for sentiment analysis and
Update all of the packages and their dependencies with one command:
conda update r-essentials
If a new version of a package is available in the R channel, you can use
conda updateto update specific packages.
When creating a new environment, you can use R and not MRO by explicitly
r-base in your list of packages. This option will continue to be
supported for users who prefer R or use platforms that do not support MRO,
including 32-bit operating systems and older versions of macOS.
With conda 4.4:
conda create -n r-environment r-essentials r-base conda activate r-environment
Activate the environment containing R.
If you use conda 4.4 or later, run:
conda install mro-base
If you use conda 4.3, run:
conda remove --force r-base _r-mutex conda install mro-base
The environment’s R interpreter will switch from R to MRO.
conda infoand check your version of conda.
If your version of conda is below 4.4, run
conda update condato update conda to the latest version.
conda config --system --set pinned_packages _r-mutex=*=anacondar*
The default R interpreter will switch from MRO to R.
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 Cloud:
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
For more information, see Jupyter and conda for R language.
Many Anaconda Enterprise customers maintain a local mirror of the R channel.
When mirroring the R channel for the first time after the early 2018 update,
clean the existing packages by running the command
anaconda-server-sync-conda with the option
r-essentials: The R Essentials bundle contains the IRKernel and more than 80 of the most popular R packages for data science, including dplyr, shiny, ggplot2, tidyr, caret and nnet.
mro-basics: The MRO Basics metapackage contains everything in the Microsoft MRO installers. It does not include
r-mrclient: Microsoft R Client is a free, community-supported, data science tool for high performance analytics.
r-mrclient-mml: MicrosoftML provides state-of-the-art fast, scalable machine learning algorithms and transforms for R. Depends on
r-mrclient-mlm: MicrosoftML Machine Learning Models are pre-trained machine learning models for sentiment analysis and image detection. Depends on
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.
For community help on using conda with MRO, join the conda email group.
Here are our more popular 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.
- Using R packages with Anaconda and Cloudera CDH–Anaconda Scale provides resource management tools to easily deploy Anaconda across a cluster. It helps you manage multiple conda environments and packages, including Python and R language, on bare-metal or cloud-based clusters.
- Blog post: Jupyter and conda for R–The many benefits that Jupyter, the IRKernel and conda can provide for data scientists working with the R programming language.
- Blog post: Anaconda for R users: SparkR and rBokeh– Data Scientist Christine Doig presents two projects for the R programming language that are powered by Anaconda. rBokeh allows you to create beautiful interactive visualizations. Scale your predictive models with SparkR through Anaconda’s cluster management capabilities.
- Using Anaconda with Hadoop: Distributed language processing with PySpark– This notebook example shows how Anaconda for cluster management makes it easy to manage packages, including Python and R, on a Hadoop cluster with PySpark.
- Webinar: Predict. Share. Deploy.–Download the webinar video
- Build predictive models in Python with Anaconda using Python packages such as pandas and scikit-learn in Jupyter Notebooks.
- Use modern open data science languages including Python and R together in your analysis.
- Share your results with your entire data science team.
- 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.