==============================
Using R language with Anaconda
==============================


Installing 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.

To install R Essentials, download Anaconda if you don't already have it. Then install the R Essentials package with the conda install command::

    conda install -c r r-essentials


Uninstalling R Essentials
=========================

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.

Resources
=========

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

* `Using R with Anaconda <http://conda.pydata.org/docs/r-with-conda.html>`_--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 <../../packages/r-language-pkg-docs>`--There are hundreds of R language packages now available, and several ways to get them. 

* :doc:`Navigator tutorial <../../navigator/tutorials/index>`--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.

* `Create and share your own custom set of R packages <http://conda.pydata.org/docs/r-with-conda.html>`_--Share data with colleagues by creating your own custom set of R packages with the ``conda metapackage`` command. 

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

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

* `Jupyter and conda for R <https://www.continuum.io/blog/developer/jupyter-and-conda-r>`_--It's easy to get R programs up and running by using Jupyter Notebook. 

* :doc:`Using R packages with Anaconda and Cloudera CDH <../../../anaconda-scale/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 <https://www.continuum.io/blog/developer/jupyter-and-conda-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 <https://www.continuum.io/blog/developer-blog/anaconda-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 <https://anaconda.org/anaconda-cluster/notebook-pyspark-language/notebook>`_--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/>`_--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.

  * Share your results with your entire data science team.

* `Webinar: Anaconda for R Users <https://speakerdeck.com/chdoig/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. 
