Tutorial: Learn in-database analytics using R in SQL Server

APPLIES TO: yesSQL Server (Windows only) noAzure SQL Database noAzure SQL Data Warehouse noParallel Data Warehouse

In this tutorial for SQL programmers, you gain hands-on experience using the R language to build and deploy a machine learning solution by wrapping R code in stored procedures.

This tutorial uses a well-known public dataset, based on trips in New York City taxis. To make the sample code run quicker, we created a representative 1% sampling of the data. You'll use this data to build a binary classification model that predicts whether a particular trip is likely to get a tip or not, based on columns such as the time of day, distance, and pick-up location.


The same solution is available in Python. SQL Server 2017 is required. See In-database analytics for Python developers


The process of building an end-to-end solution typically consists of obtaining and cleaning data, data exploration and feature engineering, model training and tuning, and finally deployment of the model in production. Development and testing of the actual code is best performed using a dedicated development environment. For R, that might mean RStudio or R Tools for Visual Studio.

However, after the solution has been created, you can easily deploy it to SQL Server using Transact-SQL stored procedures in the familiar environment of Management Studio.


This tutorial assumes familiarity with basic database operations such as creating databases and tables, importing data, and writing SQL queries. It does not assume you know R. As such, all R code is provided. A skilled SQL programmer can use a supplied PowerShell script, sample data on GitHub, and Transact-SQL in SQL Server Management Studio to complete this example.

Before starting the tutorial:


We recommend that you do not use SQL Server Management Studio to write or test R code. If the code that you embed in a stored procedure has any problems, the information that is returned from the stored procedure is usually inadequate to understand the cause of the error.

For debugging, we recommend you use a tool such as R Tools for Visual Studio, or RStudio. The R scripts provided in this tutorial have already been developed and debugged using traditional R tools.

Next steps