Analyze BigCommerce Data in R via JDBC

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

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

• Driver Class: Set this to cdata.jdbc.bigcommerce.BigCommerceDriver
• 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 BigCommerce:

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

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

BigCommerce authentication is based on the standard OAuth flow. To authenticate, you must initially create an app via the Big Commerce developer platform where you can obtain an OAuthClientId, OAuthClientSecret, and CallbackURL. These three parameters will be set as connection properties to your driver.

Additionally, in order to connect to your BigCommerce Store, you will need your StoreId. To find your Store Id please follow these steps:

1. Log in to your BigCommerce account.
2. From the Home Page, select Advanced Settings > API Accounts.
3. Click Create API Account.
4. A text box named API Path will appear on your screen.
5. Inside you can see a URL of the following structure: https://api.bigcommerce.com/stores/{Store Id}/v3.
6. As demonstrated above, your Store Id will be between the 'stores/' and '/v3' path paramters.
7. Once you have retrieved your Store Id you can either click Cancel or proceed in creating an API Account in case you do not have one already.

Built-in Connection String Designer

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

java -jar cdata.jdbc.bigcommerce.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:bigcommerce:OAuthClientId=YourClientId; OAuthClientSecret=YourClientSecret; StoreId='YourStoreID'; CallbackURL='http://localhost:33333';InitiateOAuth=GETANDREFRESH;")

Schema Discovery

The driver models BigCommerce 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 BigCommerce API:

customers <- dbGetQuery(conn,"SELECT FirstName, LastName FROM Customers WHERE FirstName = 'Bob'")

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

View(customers)

Plot BigCommerce Data

You can now analyze BigCommerce 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(customers$LastName, main="BigCommerce Customers", names.arg = customers$FirstName, horiz=TRUE)