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Analyze BigQuery Data in R

Use standard R functions and the development environment of your choice to analyze BigQuery data with the CData JDBC Driver for BigQuery.

Access BigQuery data with pure R script and standard SQL on any machine where R and Java can be installed. You can use the CData JDBC Driver for BigQuery and the RJDBC 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 and visualize BigQuery data by calling standard R functions.

Install R

You can match the driver's performance gains from multi-threading and managed code by running the multithreaded Microsoft R Open or by running open R linked with the BLAS/LAPACK libraries. This article uses Microsoft R Open 3.2.3, which is preconfigured to install packages from the Jan. 1, 2016 snapshot of the CRAN repository. This snapshot ensures reproducibility.

Load the RJDBC Package

To use the driver, download the RJDBC package. After installing the RJDBC package, the following line loads the package:

library(RJDBC)

Connect to BigQuery as a JDBC Data Source

You will need the following information to connect to BigQuery as a JDBC data source:

  • Driver Class: Set this to cdata.jdbc.googlebigquery.GoogleBigQueryDriver
  • Classpath: Set this to the location of the driver JAR. By default this is the lib subfolder of the installation folder.

The DBI functions, such as dbConnect and dbSendQuery, provide a unified interface for writing data access code in R. Use the following line to initialize a DBI driver that can make JDBC requests to the CData JDBC Driver for BigQuery:

driver <- JDBC(driverClass = "cdata.jdbc.googlebigquery.GoogleBigQueryDriver", classPath = "MyInstallationDir\lib\cdata.jdbc.googlebigquery.jar", identifier.quote = "'")

You can now use DBI functions to connect to BigQuery and execute SQL queries. Initialize the JDBC connection with the dbConnect function.

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.

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the BigQuery JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.googlebigquery.jar

Fill in the connection properties and copy the connection string to the clipboard.

Below is a sample dbConnect call, including a typical JDBC connection string:

conn <- dbConnect(driver,"jdbc:googlebigquery:DataSetId=MyDataSetId;ProjectId=MyProjectId;InitiateOAuth=GETANDREFRESH")

Schema Discovery

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

dbListTables(conn)

Execute SQL Queries

You can use the dbGetQuery function to execute any SQL query supported by the BigQuery API:

orders <- dbGetQuery(conn,"SELECT OrderName, Freight FROM Orders")

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)