Continuum Analytics: Documentation

# Using R Language with Anaconda

For Windows, OS X and Linux

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

## How to use R with Anaconda

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.

## R Language packages available for use with Anaconda

There are hundreds of r language packages now available, and several ways to get them.

## How to get 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.

## How to install R Essentials

Download Anaconda if you don’t already have it, then install the R Essentials package with the conda install command.

## 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

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)

There are several ways to install Microsoft R Open (MRO) with conda on 64-bit Windows, 64-bit OS X, and 64-bit Linux.

## Install R packages from CRAN or the Microsoft R Application Network (MRAN)

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)

The Intel Math Kernel Library (MKL) extensions are available for MRO on Windows and Linux.

## Write and run R language code with Jupyter Notebook

It’s easy to get R programs up and running by using Jupyter Notebook.

## Install R packages across multiple cluster nodes

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

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

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.

## Notebook: 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 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

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.