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title Analyze Data in Local Compute Context | Microsoft Docs
ms.custom
ms.date 05/18/2017
ms.prod sql-server-2016
ms.reviewer
ms.suite
ms.technology
r-services
ms.tgt_pltfrm
ms.topic article
applies_to
SQL Server 2016
dev_langs
R
ms.assetid 787bb526-4a13-40fa-9343-75d3bf5ba6a2
caps.latest.revision 13
author jeannt
ms.author jeannt
manager jhubbard

Analyze Data in Local Compute Context (Data Science Deep Dive)

Although it might be faster to run complex R code using the server context, sometimes it is just more convenient to get your data out of [!INCLUDEssNoVersion] and analyze it on your private workstation.

In this section, you'll learn how to switch back to a local compute context, and move data between contexts to optimize performance.

Create a Local Summary

  1. Change the compute context to do all your work locally.

    rxSetComputeContext ("local")

  2. When extracting data from [!INCLUDEssNoVersion], you can often get better performance by increasing the number of rows extracted for each read. To do this, increase the value for the rowsPerRead parameter on the data source.

    sqlServerDS1 <- RxSqlServerData(
   connectionString = sqlConnString,
   table = sqlFraudTable,
   colInfo = ccColInfo,
   rowsPerRead = 10000)

    Previously, the value of rowsPerRead was set to 5000.

  3. Now, call rxSummary on the new data source.

    rxSummary(formula = ~gender + balance + numTrans + numIntlTrans + creditLine, data = sqlServerDS1)

    The actual results should be the same as when you run rxSummary in the context of the [!INCLUDEssNoVersion] computer. However, the operation might be faster or slower. Much depends on the connection to your database, because the data is being transferred to your local computer for analysis.

Next Step

Move Data between SQL Server and XDF File

Previous Step

Perform Chunking Analysis using rxDataStep