Analyze BigQuery Data in R

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Google BigQuery ODBC Driver

The Google BigQuery ODBC Driver is a powerful tool that allows you to connect with live Google BigQuery data, directly from any applications that support ODBC connectivity.

Access Google BigQuery like you would a database - read, write, and update Datasets, Tables, etc. through a standard ODBC Driver interface.



Create data visualizations and use high-performance statistical functions to analyze BigQuery data in Microsoft R Open.

Access BigQuery data with pure R script and standard SQL. You can use the CData ODBC Driver for BigQuery and the RODBC package to work with remote BigQuery data in R. By using the CData Driver, you are leveraging a driver written for industry-proven standards to access your data in the popular, open-source R language. This article shows how to use the driver to execute SQL queries to BigQuery data and visualize BigQuery data in R.

Install R

You can complement the driver's performance gains from multi-threading and managed code by running the multithreaded Microsoft R Open or by running R linked with the BLAS/LAPACK libraries. This article uses Microsoft R Open (MRO).

Connect to BigQuery as an ODBC Data Source

Information for connecting to BigQuery follows, along with different instructions for configuring a DSN in Windows and Linux environments.

Google uses the OAuth authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app.

OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, you will need to register an application to obtain the OAuth JWT values.

In addition to the OAuth values, you will need to specify the DatasetId and ProjectId. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

When you configure the DSN, you may also want to set the Max Rows connection property. This will limit the number of rows returned, which is especially helpful for improving performance when designing reports and visualizations.

Windows

If you have not already, first specify connection properties in an ODBC DSN (data source name). This is the last step of the driver installation. You can use the Microsoft ODBC Data Source Administrator to create and configure ODBC DSNs.

Linux

If you are installing the CData ODBC Driver for BigQuery in a Linux environment, the driver installation predefines a system DSN. You can modify the DSN by editing the system data sources file (/etc/odbc.ini) and defining the required connection properties.

/etc/odbc.ini

[CData GoogleBigQuery Source] Driver = CData ODBC Driver for BigQuery Description = My Description DataSetId = MyDataSetId ProjectId = MyProjectId

For specific information on using these configuration files, please refer to the help documentation (installed and found online).

Load the RODBC Package

To use the driver, download the RODBC package. In RStudio, click Tools -> Install Packages and enter RODBC in the Packages box.

After installing the RODBC package, the following line loads the package:

library(RODBC)

Note: This article uses RODBC version 1.3-12. Using Microsoft R Open, you can test with the same version, using the checkpoint capabilities of Microsoft's MRAN repository. The checkpoint command enables you to install packages from a snapshot of the CRAN repository, hosted on the MRAN repository. The snapshot taken Jan. 1, 2016 contains version 1.3-12.

library(checkpoint) checkpoint("2016-01-01")

Connect to BigQuery Data as an ODBC Data Source

You can connect to a DSN in R with the following line:

conn <- odbcConnect("CData GoogleBigQuery Source")

Schema Discovery

The driver models BigQuery APIs as relational tables, views, and stored procedures. Use the following line to retrieve the list of tables:

sqlTables(conn)

Execute SQL Queries

Use the sqlQuery function to execute any SQL query supported by the BigQuery API.

orders <- sqlQuery(conn, "SELECT OrderName, Freight FROM Orders", believeNRows=FALSE, rows_at_time=1)

You can view the results in a data viewer window with the following command:

View(orders)

Plot BigQuery Data

You can now analyze BigQuery data with any of the data visualization packages available in the CRAN repository. You can create simple bar plots with the built-in bar plot function:

par(las=2,ps=10,mar=c(5,15,4,2)) barplot(orders$Freight, main="BigQuery Orders", names.arg = orders$OrderName, horiz=TRUE)