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title Move data between SQL Server and XDF file (SQL and R deep dive)| Microsoft Docs
ms.custom
ms.date 12/14/2017
ms.reviewer
ms.suite sql
ms.prod machine-learning-services
ms.prod_service machine-learning-services
ms.component
ms.technology
r-services
ms.tgt_pltfrm
ms.topic tutorial
applies_to
SQL Server 2016
SQL Server 2017
dev_langs
R
ms.assetid 40887cb3-ffbb-4769-9f54-c006d7f4798c
caps.latest.revision 17
author jeannt
ms.author jeannt
manager cgronlund
ms.workload Inactive

Move data between SQL Server and XDF file (SQL and R deep dive)

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

In this step, you learn to use an XDF file to transfer data between remote and local compute contexts. Storing the data in an XDF file allows you to perform transformations on the data.

When you're done, you use the data in the file to create a new [!INCLUDEssNoVersion] table. The function rxDataStep can apply transformations to the data and performs the conversion between data frames and .xdf files.

Create a SQL Server table from an XDF file

For this exercise, you use the credit card fraud data again. In this scenario, you've been asked to do some extra analysis on users in the states of California, Oregon, and Washington. To be more efficient, you've decided to store data for only these states on your local computer, and work with only the variables gender, cardholder, state, and balance.

  1. Re-use the stateAbb variable you created earlier to identify the levels to include, and write them to a new variable, statesToKeep.

    statesToKeep <- sapply(c("CA", "OR", "WA"), grep, stateAbb)
statesToKeep

    Results

    CA OR WA
    5 38 48

  2. Define the data you want to bring over from SQL Server, using a [!INCLUDEtsql] query. Later you use this variable as the inData argument for rxImport.

    importQuery <- paste("SELECT gender,cardholder,balance,state FROM",  sqlFraudTable,  "WHERE (state = 5 OR state = 38 OR state = 48)")

    Make sure there are no hidden characters such as line feeds or tabs in the query.

  3. Next, define the columns to use when working with the data in R. For example, in the smaller data set, you need only three factor levels, because the query returns data for only three states. Apply the statesToKeep variable to identify the correct levels to include.

    importColInfo <- list(
    gender = list( type = "factor",  levels = c("1", "2"), newLevels = c("Male", "Female")),
    cardholder = list(  type = "factor",  levels = c("1", "2"), newLevels = c("Principal", "Secondary")),
    state = list(   type = "factor",  levels = as.character(statesToKeep), newLevels = names(statesToKeep))
        )

  4. Set the compute context to local, because you want all the data available on your local computer.

    rxSetComputeContext("local")

    The rxImport function can import data from any supported data source to a local XDF file. Using a local copy of the data is convenient when you want to do many different analyses on the data, but want to avoid running the same query over and over.

  5. Create the data source object by passing the variables previously defined as arguments to RxSqlServerData.

    sqlServerImportDS <- RxSqlServerData(
    connectionString = sqlConnString,
    sqlQuery = importQuery,
    colInfo = importColInfo)

  6. Call rxImport to write the data to a file named ccFraudSub.xdf, in the current working directory.

    localDS <- rxImport(inData = sqlServerImportDS,
    outFile = "ccFraudSub.xdf",
    overwrite = TRUE)

    The localDs object returned by the rxImport function is a light-weight RxXdfData data source object that represents the ccFraud.xdf data file stored locally on disk.

  7. Call rxGetVarInfo on the XDF file to verify that the data schema is the same.

    rxGetVarInfo(data = localDS)

    Results

    rxGetVarInfo(data = localDS)

    Var 1: gender, Type: factor, no factor levels available

    Var 2: cardholder, Type: factor, no factor levels available

    Var 3: balance, Type: integer, Low/High: (0, 22463)

    Var 4: state, Type: factor, no factor levels available

  8. You can now call various R functions to analyze the localDs object, just as you would with the source data on [!INCLUDEssNoVersion]. For example, you might summarize by gender:

    rxSummary(~gender + cardholder + balance + state, data = localDS)

Now that you've mastered the use of compute contexts and working with various data sources, it's time to try something fun. In the next and final lesson, you create a simple simulation that runs a custom R function on the remote server.

Next step

Create a simple simulation

Previous step

Analyze data in local compute context