Analyze CSV Data in R via JDBC

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

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

• Driver Class: Set this to cdata.jdbc.csv.CSVDriver
• 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 CSV:

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

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

Connecting to Local or Cloud-Stored (Box, Google Drive, Amazon S3, SharePoint) CSV Files

CData Drivers let you work with CSV files stored locally and stored in cloud storage services like Box, Amazon S3, Google Drive, or SharePoint, right where they are.

Setting connection properties for local files

Set the URI property to local folder path.

Setting connection properties for files stored in Amazon S3

To connect to CSV file(s) within Amazon S3, set the URI property to the URI of the Bucket and Folder where the intended CSV files exist. In addition, at least set these properties:

• AWSAccessKey: AWS Access Key (username)
• AWSSecretKey: AWS Secret Key

Setting connection properties for files stored in Box

To connect to CSV file(s) within Box, set the URI property to the URI of the folder that includes the intended CSV file(s). Use the OAuth authentication method to connect to Box.

Dropbox

To connect to CSV file(s) within Dropbox, set the URI proprerty to the URI of the folder that includes the intended CSV file(s). Use the OAuth authentication method to connect to Dropbox. Either User Account or Service Account can be used to authenticate.

SharePoint Online (SOAP)

To connect to CSV file(s) within SharePoint with SOAP Schema, set the URI proprerty to the URI of the document library that includes the intended CSV file. Set User, Password, and StorageBaseURL.

SharePoint Online REST

To connect to CSV file(s) within SharePoint with REST Schema, set the URI proprerty to the URI of the document library that includes the intended CSV file. StorageBaseURL is optional. If not set, the driver will use the root drive. OAuth is used to authenticate.

Google Drive

To connect to CSV file(s) within Google Drive, set the URI property to the URI of the folder that includes the intended CSV file(s). Use the OAuth authentication method to connect and set InitiateOAuth to GETANDREFRESH.

Built-in Connection String Designer

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

java -jar cdata.jdbc.csv.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:csv:URI=/PATH/TO/MyCSVFilesFolder;")

Schema Discovery

The driver models CSV 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 CSV API:

customer <- dbGetQuery(conn,"SELECT City, SUM(TotalDue) FROM Customer GROUP BY City")

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

View(customer)

Plot CSV Data

You can now analyze CSV 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(customer$TotalDue, main="CSV Customer", names.arg = customer$City, horiz=TRUE)

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