| title | Prerequisites for the data science walkthrough for SQL Server and R | Microsoft Docs | |
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| ms.date | 08/23/2017 | |
| ms.prod | sql-server-2016 | |
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| ms.tgt_pltfrm | ||
| ms.topic | article | |
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| ms.assetid | 0b0582b8-8843-4787-94a8-2e28bdc04fb2 | |
| caps.latest.revision | 13 | |
| author | jeannt | |
| ms.author | jeannt | |
| manager | jhubbard |
We recommend that you do this walkthrough on a laptop or other computer that has the Microsoft R libraries installed. You must be able to connect, on the same network, to a [!INCLUDEssNoVersion] computer with machine learning services and the R language enabled.
You can run the walkthrough on a computer that has both [!INCLUDEssNoVersion] and an R development environment but we don't recommend this configuration for a production environment.
You must have access to an instance of SQL Server with support for R installed, using either of the following:
- Machine Learning Services (In-Database) for SQL Server 2017
- SQL Server 2016 R Services
For more information, see Set up SQL Server R Services (In-Database.
Important
Be sure to use [!INCLUDEssCurrent] or later. Previous versions of [!INCLUDEssNoVersion] do not support integration with R. However, you can use older SQL databases as an ODBC data source.
For this walkthrough, we recommend that you use an R development environment. Here are some suggestions:
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R Tools for Visual Studio (RTVS) is a free plug-in that provides Intellisense, debugging, and support for Microsoft R. YOu can use it with both R Server and SQL Server Machine Learning Services. To download, see R Tools for Visual Studio.
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Microsoft R Client is a lightweight development tool that supports development in R using the ScaleR packages. To get it, see Get Started with Microsoft R Client.
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RStudio is one of the more popular environments for R development. For more information, see https://www.rstudio.com/products/RStudio/.
You cannot complete this tutorial using a generic installation of RStudio or other environment; you must also install the R packages and connectivity libraries for Microsoft R Open. For more information, see Set Up a Data Science Client.
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Basic R tools (R.exe, RTerm.exe, RScripts.exe) are also installed by default when you install [!INCLUDErsql_rro-noversion]. If you do not wish to install an IDE, you can use these tools.
To connect to an instance of [!INCLUDEssNoVersion] to run scripts and upload data, you must have a valid login on the database server. You can use either a SQL login or integrated Windows authentication. Ask the database administrator to configure the following permissions for the account, in the database where you use R:
- Create database, tables, functions, and stored procedures
- Write data into tables
- Ability to run R script (
GRANT EXECUTE ANY EXTERNAL SCRIPT to <user>)
For this walkthrough, we have used the SQL login RTestUser. We generally recommend that you use Windows integrated authentication, but using the SQL login is simpler for some demo purposes.
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This sample was originally developed using SQL Server 2016 R Services. However, breaking changes were introduced in the Microsoft R components for 2016 SP1. Specifically, the varsToDrop and varsToKeep parameters were no longer supported for SQL Server data sources. Therefre, if you downloaded a version of the tutorial prior to SP1, it will no longer work with post-SP1 builds.
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The current version of the sample has been tested using a pre-release build of SQL Server 2017 Machine Learning Services (RC1 and RC2). In general, almost all steps should run without modification between 2016 SP1 and 2017.