TOC entries for old docs, plus some acrolinx edits

Author: HeidiSteen

docs/advanced-analytics/r/r-libraries-and-data-types.md

@@ -12,12 +12,12 @@ manager: cgronlun
 # R libraries and R data types
 [!INCLUDE[appliesto-ss-xxxx-xxxx-xxx-md-winonly](../../includes/appliesto-ss-xxxx-xxxx-xxx-md-winonly.md)]
 
-This topic describes the R libraries that are included and the data types that are supported in the following products:
+This article describes the R libraries that are included and the data types that are supported in the following products:
 
 + SQL Server 2016 R Services (In-Database)
 + SQL Server Machine Learning Services (In-Database)
 
-This topic also lists unsupported data types, and lists the data type conversions that might be performed implicitly when data is passed between R and SQL Server.
+This article also lists unsupported data types, and lists the data type conversions that might be performed implicitly when data is passed between R and SQL Server.
 
 ## R Libraries
 
@@ -70,7 +70,7 @@ The following table shows the changes in data types and values when data from [!
 
 Of the categories of data types supported by the [SQL Server type system](../../t-sql/data-types/data-types-transact-sql.md), the following types are likely to pose problems when passed to R code:
 
-+ Data types listed in the **Other** section of the SQL type system topic: **cursor**, **timestamp**, **hierarchyid**, **uniqueidentifier**, **sql_variant**, **xml**, **table**
++ Data types listed in the **Other** section of the SQL type system article: **cursor**, **timestamp**, **hierarchyid**, **uniqueidentifier**, **sql_variant**, **xml**, **table**
 + All spatial types
 + **image**
 
@@ -97,7 +97,7 @@ For more information, see [SQL Server 2016 improvements in handling some data ty
 
 In general, whenever you have any doubt about how a particular data type or data structure is being used in R, use the  `str()` function to get the internal structure and type of the R object. The result of the function is printed to the R console and is also available in the query results, in the **Messages** tab in [!INCLUDE[ssManStudio](../../includes/ssmanstudio-md.md)]. 
 
-When retrieving data from a database for use in R code, you should always eliminate columns that cannot be used in R, as well as columns that are not useful for analysis, such as GUIDS (uniqueidentifier), timestamps and other columns used for auditing, or lineage informatioh created by ETL processes. 
+When retrieving data from a database for use in R code, you should always eliminate columns that cannot be used in R, as well as columns that are not useful for analysis, such as GUIDS (uniqueidentifier), timestamps and other columns used for auditing, or lineage information created by ETL processes. 
 
 Note that inclusion of unnecessary columns can greatly reduce the performance of R code, especially if high cardinality columns are used as factors. Therefore, we recommend that you use SQL Server system stored procedures and information views to get the data types for a given table in advance, and eliminate or convert incompatible columns. For more information, see [Information Schema Views in Transact-SQL](../../relational-databases/system-information-schema-views/system-information-schema-views-transact-sql.md)
 

docs/advanced-analytics/toc.yml

@@ -155,29 +155,6 @@
     href: concepts/extension-r.md
   - name: Python extension
     href: concepts/extension-py.md
-- name: Concepts (old)
-  href: architecture-overview-machine-learning.md
-  items:
-  - name: R
-    href: r/architecture-overview-sql-server-r.md
-    items:
-    - name: R interoperability
-      href: r/r-interoperability-in-sql-server.md
-    - name: Components that support R integration
-      href: r/new-components-in-sql-server-to-support-r.md
-    - name: Security for R
-      href: r/security-overview-sql-server-r.md
-  - name: Python
-    href: python/architecture-overview-sql-server-python.md
-    items:
-    - name: Python in Machine Learning Services
-      href: python/sql-server-python-services.md
-    - name: Python interoperability
-      href: python/python-interoperability.md
-    - name: Components to support Python
-      href: python/new-components-in-sql-server-to-support-python-integration.md
-    - name: Python security
-      href: python/security-overview-sql-server-python-services.md
 - name: How To
   href: r/sql-server-machine-learning-tasks.md
   items:

docs/advanced-analytics/what-is-sql-server-machine-learning.md

@@ -26,11 +26,11 @@ SQL Server 2017 supports R and Python. The following table describes the compone
 | Component | Description |
 |-----------|-------------|
 | SQL Server Launchpad service | A service that manages communications between the external R and Python runtimes and the database engine instance. |
-| R packages | [**RevoScaleR**](r/revoscaler-overview.md) is the primary library for scaleable R. Functions in this library are among the most widely used. Data transformations and manipulation, statistical summarization, visualization, and many forms of modeling and analyses are found in these libraries. Additionally, functions in these libraries automatically distribute workloads across available cores for parallel processing, with the ability to work on chunks of data that are coordinated and managed by the calculation engine.  <br/>[**MicrosoftML (R)**](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/microsoftml-package) adds machine learning algorithms to create custom models for text analysis, image analysis, and sentiment analysis. <br/>[**sqlRUtils**](r/generating-an-r-stored-procedure-for-r-code-using-the-sqlrutils-package.md) provides helper functions for putting R scripts into a T-SQL stored procedure, registering a stored procedure with a database, and running the stored procedure from an R development environment.<br/>[**olapR**](r/how-to-create-mdx-queries-using-olapr.md) is for building or executing an MDX query in R script.|
+| R packages | [**RevoScaleR**](r/revoscaler-overview.md) is the primary library for scalable R. Functions in this library are among the most widely used. Data transformations and manipulation, statistical summarization, visualization, and many forms of modeling and analyses are found in these libraries. Additionally, functions in these libraries automatically distribute workloads across available cores for parallel processing, with the ability to work on chunks of data that are coordinated and managed by the calculation engine.  <br/>[**MicrosoftML (R)**](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/microsoftml-package) adds machine learning algorithms to create custom models for text analysis, image analysis, and sentiment analysis. <br/>[**sqlRUtils**](r/generating-an-r-stored-procedure-for-r-code-using-the-sqlrutils-package.md) provides helper functions for putting R scripts into a T-SQL stored procedure, registering a stored procedure with a database, and running the stored procedure from an R development environment.<br/>[**olapR**](r/how-to-create-mdx-queries-using-olapr.md) is for building or executing an MDX query in R script.|
 | Microsoft R Open (MRO) | [**MRO**](https://mran.microsoft.com/open) is Microsoft's open-source distribution of R. The package and interpreter are included. Always use the version of MRO installed by Setup. |
 | R tools | R console windows and command prompts are standard tools in an R distribution.  |
 | R Samples and scripts |  Open-source R and RevoScaleR packages include built-in data sets so that you can create and run script using pre-installed data. |
-| Python packages | [**revoscalepy**](python/what-is-revoscalepy.md) is the primary library for scaleable Python with functions for data manipulation, transformation, visualization, and analysis. <br/>[**microsoftml (Python)**](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/microsoftml-package) adds machine learning algorithms to create custom models for text analysis, image analysis, and sentiment analysis.  |
+| Python packages | [**revoscalepy**](python/what-is-revoscalepy.md) is the primary library for scalable Python with functions for data manipulation, transformation, visualization, and analysis. <br/>[**microsoftml (Python)**](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/microsoftml-package) adds machine learning algorithms to create custom models for text analysis, image analysis, and sentiment analysis.  |
 | Python tools | The built-in Python command line tool is useful for ad hoc testing and tasks.  |
 | Anaconda | Anaconda is an open-source distribution of Python and essential packages. |
 | Python samples and scripts | As with R, Python includes built-in data sets  and scripts.  |