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title Analyze data in local compute context (SQL and R deep dive) | Microsoft Docs
ms.prod sql
ms.technology machine-learning
ms.date 04/15/2018
ms.topic tutorial
author HeidiSteen
ms.author heidist
manager cgronlun

Analyze data in local compute context (SQL and R deep dive)

[!INCLUDEappliesto-ss-xxxx-xxxx-xxx-md-winonly]

This article is part of the Data Science Deep Dive tutorial, on how to use RevoScaleR with SQL Server.

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

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

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. Previously, the value of rowsPerRead was set to 5000.

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

  3. 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