Analyze JSON Services in R via JDBC

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

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

• Driver Class: Set this to cdata.jdbc.json.JSONDriver
• 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 JSON:

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

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

See the Getting Started chapter in the data provider documentation to authenticate to your data source: The data provider models JSON APIs as bidirectional database tables and JSON files as read-only views (local files, files stored on popular cloud services, and FTP servers). The major authentication schemes are supported, including HTTP Basic, Digest, NTLM, OAuth, and FTP. See the Getting Started chapter in the data provider documentation for authentication guides.

After setting the and providing any authentication values, set to more closely match the data representation to the structure of your data.

The property is the controlling property over how your data is represented into tables and toggles the following basic configurations.

• Document (default): Model a top-level, document view of your JSON data. The data provider returns nested elements as aggregates of data.
• FlattenedDocuments: Implicitly join nested documents and their parents into a single table.
• Relational: Return individual, related tables from hierarchical data. The tables contain a primary key and a foreign key that links to the parent document.

See the Modeling JSON Data chapter for more information on configuring the relational representation. You will also find the sample data used in the following examples. The data includes entries for people, the cars they own, and various maintenance services performed on those cars.

Built-in Connection String Designer

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

java -jar cdata.jdbc.json.jar

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

👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)

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

conn <- dbConnect(driver,"jdbc:json:URI=C:/people.json;DataModel=Relational;")

Schema Discovery

The driver models JSON 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 JSON API:

people <- dbGetQuery(conn,"SELECT [people].[personal.age] AS age, [people].[personal.gender] AS gender, [people].[personal.name.first] AS first_name, [people].[personal.name.last] AS last_name, [vehicles].[model], FROM [people] JOIN [vehicles] ON [people].[_id] = [vehicles].[people_id]")

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

View(people)

Plot JSON Services

You can now analyze JSON services 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(people$[ personal.name.last ], main="JSON people", names.arg = people$[ personal.name.first ], horiz=TRUE)

👁 A basic bar plot. (Salesforce is shown.)