# Resources for using R with Amazon SageMaker AI
R

This document lists resources that can help you learn how to use Amazon SageMaker AI features with the R software environment. The following sections introduce SageMaker AI's built-in R kernel, explain how to get started with R on SageMaker AI, and provide several example notebooks.

The examples are organized in three levels: beginner, intermediate, and advanced. They start with [Getting Started with R on SageMaker AI](https://sagemaker-examples.readthedocs.io/en/latest/r_examples/r_sagemaker_hello_world/r_sagemaker_hello_world.html), continue with end-to-end machine learning with R on SageMaker AI, and then finish with more advanced topics such as SageMaker Processing with R script, and bring-your-own R algorithm to SageMaker AI.

For information on how to bring your own custom R image to Studio, see [Custom Images in Amazon SageMaker Studio Classic](studio-byoi.md). For a similar blog article, see [Bringing your own R environment to Amazon SageMaker Studio](https://aws.amazon.com/blogs/machine-learning/bringing-your-own-r-environment-to-amazon-sagemaker-studio/).

**Topics**
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## RStudio support in SageMaker AI
](#rstudio-for-r)
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## R kernel in SageMaker AI
](#r-sagemaker-kernel-ni)
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## Example notebooks
](#r-sagemaker-example-notebooks)
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# Get started with R in SageMaker AI
](r-sagemaker-get-started.md)

## RStudio support in SageMaker AI


Amazon SageMaker AI supports RStudio as a fully-managed integrated development environment (IDE) integrated with Amazon SageMaker AI domain. With RStudio integration, you can launch an RStudio environment in the domain to run your RStudio workflows on SageMaker AI resources. For more information, see [RStudio on Amazon SageMaker AI](rstudio.md).

## R kernel in SageMaker AI


SageMaker notebook instances support R using a pre-installed R kernel. Also, the R kernel has the reticulate library, an R to Python interface, so you can use the features of SageMaker AI Python SDK from within an R script. 
+ [reticulatelibrary](https://rstudio.github.io/reticulate/): provides an R interface to the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable). The reticulate package translates between R and Python objects.

## Example notebooks


**Prerequisites**
+ [Getting Started with R on SageMaker AI](https://sagemaker-examples.readthedocs.io/en/latest/r_examples/r_sagemaker_hello_world/r_sagemaker_hello_world.html) – This sample notebook describes how you can develop R scripts using Amazon SageMaker AI‘s R kernel. In this notebook you set up your SageMaker AI environment and permissions, download the [abalone dataset](https://archive.ics.uci.edu/ml/datasets/abalone) from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/datasets), do some basic processing and visualization on the data, then save the data as .csv format to S3.

**Beginner Level**
+ [SageMaker AI Batch Transform using R Kernel](https://sagemaker-examples.readthedocs.io/en/latest/r_examples/r_batch_transform/r_xgboost_batch_transform.html) – This sample Notebook describes how to conduct a batch transform job using SageMaker AI’s Transformer API and the [XGBoost algorithm](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html). The notebook also uses the Abalone dataset.

**Intermediate Level**
+ [Hyperparameter Optimization for XGBoost in R](https://sagemaker-examples.readthedocs.io/en/latest/r_examples/r_xgboost_hpo_batch_transform/r_xgboost_hpo_batch_transform.html) – This sample notebook extends the previous beginner notebooks that use the abalone dataset and XGBoost. It describes how to do model tuning with [hyperparameter optimization](https://sagemaker.readthedocs.io/en/stable/tuner.html). You will also learn how to use batch transform for batching predictions, as well as how to create a model endpoint to make real-time predictions.
+ [Amazon SageMaker Processing with R](https://sagemaker-examples.readthedocs.io/en/latest/r_examples/r_in_sagemaker_processing/r_in_sagemaker_processing.html) – [SageMaker Processing](https://aws.amazon.com/blogs/aws/amazon-sagemaker-processing-fully-managed-data-processing-and-model-evaluation/) lets you preprocess, post-process and run model evaluation workloads. This example shows you how to create an R script to orchestrate a Processing job.

**Advanced Level**
+ [Train and Deploy Your Own R Algorithm in SageMaker AI](https://sagemaker-examples.readthedocs.io/en/latest/r_examples/r_byo_r_algo_hpo/tune_r_bring_your_own.html) – Do you already have an R algorithm, and you want to bring it into SageMaker AI to tune, train, or deploy it? This example walks you through how to customize SageMaker AI containers with custom R packages, all the way to using a hosted endpoint for inference on your R-origin model.

# Get started with R in SageMaker AI


This topic explains how to get started using the R software environment in SageMaker AI. For more information about using R with SageMaker AI, see [Resources for using R with Amazon SageMaker AI](r-guide.md).

**To get started with R in the SageMaker AI console**

1. [Create a notebook instance](https://docs.aws.amazon.com/sagemaker/latest/dg/howitworks-create-ws.html) using the t2.medium instance type and default storage size. You can pick a faster instance and more storage if you plan to continue using the instance for more advanced examples, or you can create a bigger instance later.

1. Wait until the status of the notebook is **In Service**, and then choose **Open Jupyter**.  
![\[Location of the InService status and the Open Jupyter link in the console.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/An-R-User-Guide-to-SageMaker/An-R-User-Guide-to-SageMaker-1.png)

1. Create a new notebook with R kernel from the list of available environments.  
![\[Location of the R kernel in the list of available environments.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/An-R-User-Guide-to-SageMaker/An-R-User-Guide-to-SageMaker-2.png)

1. When the new notebook is created, you should see an R logo in the upper right corner of the notebook environment, and also R as the kernel under that logo. This indicates that SageMaker AI has successfully launched the R kernel for this notebook.  
![\[Location of the R logo and R kernel of the notebook environment.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/An-R-User-Guide-to-SageMaker/An-R-User-Guide-to-SageMaker-3.png)

Alternatively, when you are in a Jupyter notebook, you can use the **Kernel** menu, and then select **R** from the **Change kernel** submenu.

![\[Location of where to change your notebook kernel to R.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/An-R-User-Guide-to-SageMaker/An-R-User-Guide-to-SageMaker-4.png)