Using R Language with Anaconda
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For Windows, macOS and Linux 

Here are our more popular resources on using Anaconda with the R programming language.

`How to use R with Anaconda <http://conda.pydata.org/docs/r-with-conda.html>`_
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If you have conda installed, you can easily install R and more than 80 of the most popular R packages for data science with one command. Conda helps you keep your packages and dependencies up to date. You can also easily create and share your own custom R packages.

:doc:`R Language packages available for use with Anaconda <r-language-pkg-docs>`
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There are hundreds of r language packages now available, and several ways to get them. 

`How to get R Essentials <http://conda.pydata.org/docs/r-with-conda.html>`_
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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. 

`How to install R Essentials <http://conda.pydata.org/docs/r-with-conda.html>`_
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Download Anaconda if you don’t already have it, and then install the R Essentials package with the conda install command::

    conda install -c r r-essentials


How to uninstall R Essentials
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To uninstall R Essentials package run::

    conda remove r-essentials

NOTE: This will only remove r-essentials and disable R Language support. The 
other packages listed above will not be removed.


`Use the R programming language with Anaconda Navigator <https://docs.continuum.io/anaconda/navigator-tutorial>`_
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The Anaconda Navigator graphical interface (GUI) makes it easy for even new users to use and run the R language in a Jupyter Notebook.

`Create and share your own custom set of R packages <http://conda.pydata.org/docs/r-with-conda.html>`_
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You can create your own custom set of R packages to share data with colleagues with the conda metapackage command. 

`Install Microsoft R Open (MRO) <http://conda.pydata.org/docs/mro.html>`_
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There are several ways to install Microsoft R Open (MRO) with conda on 64-bit Windows, 64-bit macOS, and 64-bit Linux. 

`Install R packages from CRAN or the Microsoft R Application Network (MRAN) <http://conda.pydata.org/docs/mro.html>`_
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Use conda to easily install R packages from the Comprehensive R Archive Network 
(CRAN) or the Microsoft R Application Network (MRAN). 

`Install Math Kernel Library (MKL) with Microsoft R Open (MRO) <http://conda.pydata.org/docs/mro.html>`_
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The Intel Math Kernel Library (MKL) extensions are available for MRO on Windows and Linux.

`Write and run R language code with Jupyter Notebook <https://www.continuum.io/blog/developer/jupyter-and-conda-r>`_
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It’s easy to get R programs up and running by using Jupyter Notebook. 

`Install R packages across multiple cluster nodes <https://docs.continuum.io/anaconda-cluster/index>`_
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Anaconda for cluster management 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. 

`Using R packages with Anaconda and Cloudera CDH <https://docs.continuum.io/anaconda-cluster/cloudera-cdh>`_
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Anaconda for cluster management provides additional functionality, including the ability to manage multiple conda environments and packages (including Python and R) alongside an existing CDH cluster.

`Blog post: Jupyter and conda for R <https://www.continuum.io/blog/developer/jupyter-and-conda-r>`_
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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 <https://www.continuum.io/blog/developer-blog/anaconda-r-users-sparkr-and-rbokeh>`_
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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.

`Notebook: Using Anaconda with Hadoop: Distributed language processing with PySpark <https://anaconda.org/anaconda-cluster/notebook-pyspark-language/notebook>`_
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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. <http://go.continuum.io/predict-share-deploy/>`_
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Download the webinar video to 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, and share your results with your entire data science team.

`Webinar: Anaconda for R Users <https://speakerdeck.com/chdoig/anaconda-for-r-users>`_
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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. 
