Appliesto for R

davidpeterh · davidpeterh · commit 5c95f628041a · 2020-08-11T21:56:01.000-04:00
diff --git a/docs/machine-learning/r/set-up-a-data-science-client.md b/docs/machine-learning/r/set-up-a-data-science-client.md
@@ -12,7 +12,7 @@ ms.custom: seo-lt-2019
 monikerRange: ">=sql-server-2016||>=sql-server-linux-ver15||=sqlallproducts-allversions"
 ---
 # Set up a data science client for R development on SQL Server
- [!INCLUDE [SQL Server](../../includes/applies-to-version/sqlserver.md)]
+[!INCLUDE [SQL Server 2016 and later ](../../includes/applies-to-version/sqlserver2016.md)]
 
 R integration is available in SQL Server 2016 or later when you include the R language option in an [SQL Server 2016 R Services](../install/sql-r-services-windows-install.md) or [SQL Server Machine Learning Services (In-Database)](../install/sql-machine-learning-services-windows-install.md) installation. 
 
diff --git a/docs/machine-learning/r/sql-server-configuration-r-services.md b/docs/machine-learning/r/sql-server-configuration-r-services.md
@@ -12,7 +12,7 @@ ms.custom: seo-lt-2019
 monikerRange: ">=sql-server-2016||>=sql-server-linux-ver15||=sqlallproducts-allversions"
 ---
 # SQL Server configuration for use with R
- [!INCLUDE [SQL Server](../../includes/applies-to-version/sqlserver.md)]
+[!INCLUDE [SQL Server 2016 and later ](../../includes/applies-to-version/sqlserver2016.md)]
 
 This article is the second in a series that describes performance optimization for R Services based on two case studies.  This article provides guidance about the hardware and network configuration of the computer that is used to run SQL Server R Services. It also contains information about ways to configure the SQL Server instance, database, or tables used in a solution. Because use of NUMA in SQL Server blurs the line between hardware and database optimizations, a third section discusses CPU affinitization and resource governance in detail.
 
diff --git a/docs/machine-learning/r/sql-server-r-services-performance-tuning.md b/docs/machine-learning/r/sql-server-r-services-performance-tuning.md
@@ -12,7 +12,7 @@ ms.custom: seo-lt-2019
 monikerRange: ">=sql-server-2016||>=sql-server-linux-ver15||=sqlallproducts-allversions"
 ---
 # Performance tuning for R in SQL Server
- [!INCLUDE [SQL Server](../../includes/applies-to-version/sqlserver.md)]
+[!INCLUDE [SQL Server 2016 and later ](../../includes/applies-to-version/sqlserver2016.md)]
 
 This article is the first in a series of four articles that describe performance optimization for R Services, based on two case studies:
 
diff --git a/docs/machine-learning/r/sql-server-r-services.md b/docs/machine-learning/r/sql-server-r-services.md
@@ -11,8 +11,7 @@ ms.author: davidph
 monikerRange: "=sql-server-2016||=sqlallproducts-allversions"
 ---
 # What is SQL Server 2016 R Services?
-
-[!INCLUDE[SQL Server 2016 only](../../includes/applies-to-version/sqlserver2016-only.md)]
+[!INCLUDE [SQL Server 2016 and later ](../../includes/applies-to-version/sqlserver2016.md)]
 
 R Services is a feature in SQL Server 2016 that gives the ability to run R scripts with relational data. You can use open-source packages and frameworks, and the [Microsoft R packages](#packages) for predictive analytics and machine learning. The scripts are executed in-database without moving data outside SQL Server or over the network. This article explains the basics of SQL Server R Services.
 
diff --git a/docs/machine-learning/r/using-data-from-olap-cubes-in-r.md b/docs/machine-learning/r/using-data-from-olap-cubes-in-r.md
@@ -12,7 +12,7 @@ ms.custom: seo-lt-2019
 monikerRange: ">=sql-server-2016||>=sql-server-linux-ver15||=sqlallproducts-allversions"
 ---
 # Using data from OLAP cubes in R
- [!INCLUDE [SQL Server](../../includes/applies-to-version/sqlserver.md)]
+[!INCLUDE [SQL Server 2016 and later ](../../includes/applies-to-version/sqlserver2016.md)]
 
 The **olapR** package is an R package, provided by Microsoft for use with Machine Learning Server and SQL Server, that lets you run MDX queries to get data from OLAP cubes. With this package, you don't need to create linked servers or clean up flattened rowsets; you can get OLAP data directly from R.
 
diff --git a/docs/machine-learning/r/using-r-code-profiling-functions.md b/docs/machine-learning/r/using-r-code-profiling-functions.md
@@ -11,7 +11,7 @@ ms.author: davidph
 monikerRange: ">=sql-server-2016||>=sql-server-linux-ver15||=sqlallproducts-allversions"
 ---
 # Use R code profiling functions to improve performance
- [!INCLUDE [SQL Server](../../includes/applies-to-version/sqlserver.md)]
+[!INCLUDE [SQL Server 2016 and later ](../../includes/applies-to-version/sqlserver2016.md)]
 
 In addition to using SQL Server resources and tools to monitor R script execution, you can use performance tools provided by other R packages to get more information about internal function calls. 
 
