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Neo4J Icon Neo4J ODBC Driver

The Neo4J ODBC Driver is a powerful tool that allows you to connect with live data from Neo4J, directly from any applications that support ODBC connectivity.

Access Neo4J data like you would a database - read Neo4J data through a standard ODBC Driver interface.

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Analyze Neo4J Data in R


Create data visualizations and use high-performance statistical functions to analyze Neo4J data in Microsoft R Open.

Access Neo4J data with pure R script and standard SQL. You can use the CData ODBC Driver for Neo4J and the RODBC package to work with remote Neo4J 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 Neo4J data and visualize Neo4J 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 Neo4J as an ODBC Data Source

Information for connecting to Neo4J follows, along with different instructions for configuring a DSN in Windows and Linux environments.

To connect to Neo4j, set the following connection properties:

  • Server: The server hosting the Neo4j instance.
  • Port: The port on which the Neo4j service is running. The provider connects to port 7474 by default.
  • User: The username of the user using the Neo4j instance.
  • Password: The password of the user using the Neo4j instance.

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 Neo4J 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 Neo4j Source] Driver = CData ODBC Driver for Neo4J Description = My Description Server = localhost Port = 7474 User = my_user Password = my_password

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 Neo4J Data as an ODBC Data Source

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

conn <- odbcConnect("CData Neo4j Source")

Schema Discovery

The driver models Neo4J 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 Neo4J API.

productcategory <- sqlQuery(conn, "SELECT CategoryId, CategoryName FROM ProductCategory WHERE CategoryOwner = 'CData Software'", believeNRows=FALSE, rows_at_time=1)

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

View(productcategory)

Plot Neo4J Data

You can now analyze Neo4J 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(productcategory$CategoryName, main="Neo4J ProductCategory", names.arg = productcategory$CategoryId, horiz=TRUE)