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.


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

  • Using 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.
  • 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.
  • Create and share your own custom set of R packages–Share data with colleagues by creating your own custom set of R packages with the conda metapackage command.
  • Using Microsoft R Open (MRO)–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–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–The Intel Math Kernel Library (MKL) extensions are available for Microsoft R Open (MRO) on Windows and Linux.
  • Jupyter and conda for R–It’s easy to get R programs up and running by using 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 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–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.

Was this helpful?