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‎docs/machine-learning/r/converting-r-code-for-use-in-sql-server.md‎

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+ Consult with a database developer to determine ways to improve performance by using SQL Server features such as [memory-optimized tables](https://docs.microsoft.com/sql/relational-databases/in-memory-oltp/introduction-to-memory-optimized-tables), or, if you have Enterprise Edition, [Resource Governor](https://docs.microsoft.com/sql/relational-databases/resource-governor/resource-governor)).
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For more information, see [SQL Server Optimization Tips and Tricks for Analytics Services](https://gallery.cortanaintelligence.com/Tutorial/SQL-Server-Optimization-Tips-and-Tricks-for-Analytics-Services)
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### Step 3. Prepare for deployment
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‎docs/machine-learning/r/creating-workflows-that-use-r-in-sql-server.md‎

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By using [!INCLUDE[rsql_productname](../../includes/rsql-productname-md.md)], you can run complex operations in R via [!INCLUDE[tsql](../../includes/tsql-md.md)] stored procedures, which can easily be consumed by a variety of enterprise reporting tools, including [!INCLUDE[ssRSnoversion](../../includes/ssrsnoversion-md.md)] and Power BI.
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### SSRS example
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[R Graphics Device for Microsoft Reporting Services (SSRS)](https://rgraphicsdevice.codeplex.com/)
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This CodePlex project provides the code to help you create a custom report item that renders the graphics output of R as an image that can be used in [!INCLUDE[ssRSnoversion](../../includes/ssrsnoversion-md.md)] reports. By using the custom report item, you can:
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+ Publish charts and plots created using the R Graphics Device to [!INCLUDE[ssRSnoversion](../../includes/ssrsnoversion-md.md)] dashboards
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+ Pass [!INCLUDE[ssRSnoversion](../../includes/ssrsnoversion-md.md)] parameters to R plots
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> [!NOTE]
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> For this sample, the code that supports the R Graphics Device for Reporting Services must be installed on the Reporting Services server, as well as in Visual Studio. Manual compilation and configuration is also required.
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## Next steps
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The SSIS and SSRS examples in this article illustrate two cases of executing stored procedures that contain embedded R or Python script. A key takeaway is that you can make R or Python script available to any application or tool that can send an execution request on a stored procedure. An additional takeaway for SSIS is that you can create packages that automate and schedule wide range of operations, such as data acquisition, cleansing, manipulation, and so forth, with R or Python data science functionality included in the chain of operations. For more information and ideas, see [Operationalize R code using stored procedures in SQL Server Machine Learning Services](operationalizing-your-r-code.md).

‎docs/machine-learning/r/how-to-create-a-stored-procedure-using-sqlrutils.md‎

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ms.prod: sql
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ms.technology: machine-learning-services
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ms.date: 08/28/2020
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ms.date: 08/31/2020
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ms.topic: how-to
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author: dphansen
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‎docs/machine-learning/r/operationalizing-your-r-code.md‎

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Converting your R code in SQL is easier if some optimizations are done beforehand in the R or Python code. These include avoiding data types that cause problems, avoiding unnecessary data conversions, and rewriting the R code as a single function call that can be easily parameterized. For more information, see:
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+ [R libraries and data types](r-libraries-and-data-types.md)
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+ [Converting R code for use in R Services](converting-r-code-for-use-in-sql-server.md)
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+ [Use sqlrutils helper functions](ref-r-sqlrutils.md)
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## Integrate R and Python with applications
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+ Single-row scoring, for calling from an application
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+ [Native scoring](../predictions/native-scoring-predict-transact-sql.md), for fast batch prediction from SQL Server without calling R
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This walkthrough provides examples of scoring using a stored procedure in both batch and single-row modes:
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This walkthrough provides an example of scoring using a stored procedure in both batch and single-row modes:
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+ [End to end data science walkthrough for R in SQL Server](../tutorials/walkthrough-data-science-end-to-end-walkthrough.md)
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See these solution templates for examples of how to integrate scoring in an application:
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+ [Retail forecasting](https://github.com/Microsoft/SQL-Server-R-Services-Samples/blob/master/RetailForecasting/README.md)
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+ [Fraud detection](https://github.com/Microsoft/r-server-fraud-detection)
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+ [Customer clustering](https://github.com/Microsoft/sql-server-samples/tree/master/samples/features/r-services/getting-started/customer-clustering)
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## Boost performance and scale
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Although the open-source R language has known limitations with regards to large data sets, the [RevoScaleR package APIs](ref-r-revoscaler.md) included with SQL Server Machine Learning Service can operate on large datasets and benefit from multi-threaded, multi-core, multi-process in-database computations.
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If your R solution uses complex aggregations or involves large datasets, you can leverage SQL Server's highly efficient in-memory aggregations and columnstore indexes, and let the R code handle the statistical computations and scoring.
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For more information about how to improve performance in SQL Server Machine Learning, see:
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+ [Performance tuning for SQL Server R Services](../../machine-learning/r/sql-server-r-services-performance-tuning.md)
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+ [Performance optimization tips and tricks](https://gallery.cortanaintelligence.com/Tutorial/SQL-Server-Optimization-Tips-and-Tricks-for-Analytics-Services)
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## Adapt R code for other platforms or compute contexts
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The same R code that you run against [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] data can be used against other data sources, such as Spark over HDFS, when you use the [standalone server option](../install/sql-machine-learning-standalone-windows-install.md) in SQL Server setup or when you install the non-SQL branded product, Microsoft Machine Learning Server (formerly known as **Microsoft R Server**):
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+ [Machine Learning Server Documentation](https://docs.microsoft.com/r-server/)
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+ [Explore R to RevoScaleR](https://docs.microsoft.com/r-server/r/tutorial-r-to-revoscaler)
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+ [Write chunking algorithms](https://docs.microsoft.com/r-server/r/how-to-developer-write-chunking-algorithms)
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+ [Computing with big data in R](https://docs.microsoft.com/r-server/r/tutorial-large-data-tips)
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+ [Develop your own parallel algorithm](https://docs.microsoft.com/r-server/r-reference/revopemar/pemar)
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