---
title: Load data using rxImport
description: "RevoScaleR tutorial 10: How to load data using the R language on SQL Server."
ms.prod: sql
ms.technology: machine-learning

ms.date: 11/27/2018  
ms.topic: tutorial
author: dphansen
ms.author: davidph
ms.custom: seo-lt-2019
monikerRange: ">=sql-server-2016||>=sql-server-linux-ver15||=sqlallproducts-allversions"
---
# Load data into memory using rxImport (SQL Server and RevoScaleR tutorial)
[!INCLUDE[appliesto-ss-xxxx-xxxx-xxx-md](../../includes/appliesto-ss-xxxx-xxxx-xxx-md.md)]

This is tutorial 10 of the [RevoScaleR tutorial series](deepdive-data-science-deep-dive-using-the-revoscaler-packages.md) on how to use [RevoScaleR functions](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/revoscaler) with SQL Server.

In this tutorial, you'll learn how to get data from [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)], and then use the **rxImport** function to put the data of interest into a local file. That way, you can analyze it in the local compute context repeatedly, without having to re-query the database.

The [rxImport](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/rximport) function can be used to move data from a data source into a data frame in session memory, or into an XDF file on disk. If you don't specify a file as destination, data is put into memory as a data frame.

## Extract a subset of data from SQL Server to local memory

You've decided that you want to examine only the high risk individuals in more detail. The source table in [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] is big, so you want to get the information about just the high-risk customers. You then load that data into a data frame in the memory of the local workstation.

1. Reset the compute context to your local workstation.

    ```R
    rxSetComputeContext("local")
    ```

2. Create a new SQL Server data source object, providing a valid SQL statement in the *sqlQuery* parameter. This example gets a subset of the observations with the highest risk scores. That way, only the data you really need is put in local memory.

    ```R
    sqlServerProbDS \<- RxSqlServerData(
        sqlQuery = paste("SELECT * FROM ccScoreOutput2",
        "WHERE (ccFraudProb > .99)"),
        connectionString = sqlConnString)
    ```

3. Call the function [rxImport](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/rximport) to read the data into a data frame in the local R session.

    ```R
    highRisk <- rxImport(sqlServerProbDS)
    ```

    If the operation was successful, you should see a status message like this one: "Rows Read: 35, Total Rows Processed: 35, Total Chunk Time: 0.036 seconds"

4. Now that the high-risk observations are in an in-memory data frame, you can use various R functions to manipulate the data frame. For example, you can order customers by their risk score, and print a list of the customers who pose the highest risk.

    ```R
    orderedHighRisk <- highRisk[order(-highRisk$ccFraudProb),]
    row.names(orderedHighRisk) <- NULL
    head(orderedHighRisk)
    ```

**Results**

```R
ccFraudLogitScore   state gender cardholder balance numTrans numIntlTrans creditLine ccFraudProb1
9.786345    SD   Male  Principal   23456       25            5 75   0.99994382
9.433040    FL Female  Principal   20629       24           28 75   0.99992003
8.556785    NY Female  Principal   19064       82           53 43   0.99980784
8.188668    AZ Female  Principal   19948       29            0 75   0.99972235
7.551699    NY Female  Principal   11051       95            0 75   0.99947516
7.335080    NV   Male  Principal   21566        4            6  75   0.9993482
```

## More about rxImport

You can use **rxImport** not just to move data, but to transform data in the process of reading it. For example, you can specify the number of characters for fixed-width columns, provide a description of the variables, set levels for factor columns, and even create new levels to use after importing.

The **rxImport** function assigns variable names to the columns during the import process, but you can indicate new variable names by using the *colInfo* parameter, or change data types using the *colClasses* parameter.

By specifying additional operations in the *transforms* parameter, you can do elementary processing on each chunk of data that is read.

## Next steps

> [!div class="nextstepaction"]
> [Create new SQL Server table using rxDataStep](../../advanced-analytics/tutorials/deepdive-move-data-between-sql-server-and-xdf-file.md)