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Analyze Azure Table Data in R

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

Access Azure Table 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 Azure Table and the RJDBC package to work with remote Azure Table 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 Azure Table and visualize Azure Table 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 Azure Table as a JDBC Data Source

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

  • Driver Class: Set this to cdata.jdbc.azuretables.AzureTablesDriver
  • 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 Azure Table:

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

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

Specify your AccessKey and your Account to connect. Set the Account property to the Storage Account Name and set AccessKey to one of the Access Keys. Either the Primary or Secondary Access Keys can be used. To obtain these values, navigate to the Storage Accounts blade in the Azure portal. You can obtain the access key by selecting your account and clicking Access Keys in the Settings section.

Built-in Connection String Designer

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

java -jar cdata.jdbc.azuretables.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:azuretables:AccessKey=myAccessKey;Account=myAccountName;")

Schema Discovery

The driver models Azure Table 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 Azure Table API:

northwindproducts <- dbGetQuery(conn,"SELECT Name, Price FROM NorthwindProducts")

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

View(northwindproducts)

Plot Azure Table Data

You can now analyze Azure Table 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(northwindproducts$Price, main="Azure Table NorthwindProducts", names.arg = northwindproducts$Name, horiz=TRUE)