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SQL Server R Service – Complete Reference

This article serves as a single point repository for all content, resources, links, information and latest updates about Microsoft SQL Server R Services, and various ecosystem around it.

Contributions from everyone are most welcome.


Introduction and Overview

Microsoft SQL Server R Services provides a platform for developing and deploying intelligent applications using R language, from with the SQL Server environment. Microsoft provides the software, services, and support that combine to make the very popular R statistical computing environment a compelling tool not only for academia, exploration, and prototyping, but for deployment within an enterprise. 

The key features provided by the Microsoft R Services are:

  • Microsoft R Open: Commonly used math libraries installed on top of Open Source R (including Base and Recommended Packages). Microsoft R Services
    connects to a version of Microsoft R Open that delivers Open Source R. This enables users to use any third-party packages (comptible with Open Source R) with Microsoft R Services. 
  • **DistributedR: **Parallel and distributed computing framework for Big Data environment.  It allows users to run the same R script on multiple platforms; for instance, users can create a data model in one environment (workstation), and then deploy it on a different environment (on-site Microsoft SQL Server or  a Hadoop cluster) just by providing the information about location for running the computations.
  • **ScaleR: **High performance, scalable, parallelized and distributable Data Analytics in R
  • ConnectR: Data connections for the entire Data platform
  • DevelopR: An integrated development environment (IDE) for R on Windows
  • **DeployR: **A web services software development kit for integrating R with third party products (including business intelligence, data visualization, rules engines, etc.)

Technical Documentation

Setting up R Services

Installation

Configuration

R Development Tools

Microsoft R Client offers three ways for developing and testing predictive models:

  • **Microsoft R Open: **A distribution of the R runtime and a set of packages, such as the Intel math kernel library.  It is a complete open source platform for statistical analysis and data science, available for download here: Microsoft R Open: The Enhanced R Distribution.
  • **RevoScaleR:**An R package that enables pushing computations to any instance of Microsoft R Enterprise. It also includes a set of common R functions redesigned to provide optimized performance. and scalability (functions by the rx prefix) and enhanced data providers for a variety of sources (functions prefixed with Rx). For more details, see Data Science Deep Dive: Using the RevoScaleR Packages
  • **Additional development tools: **Any Windows-based code editor that supports R, can be used, such as R Tools for Visual Studio or RStudio. The download of Microsoft R Open also includes common command-line tools for R such as RGui.exe.

Microsoft advantage

  • Parallelizes predictive analytics to increase data scale and speed
  • Analyzes data a block-at-a-time and combines results
  • Escapes R’s traditional memory limits
  • Parallelized by distributing computation cores & socket

Working Code Samples (GitHub)

Downloads and Resources