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The CSV ODBC Driver is a powerful tool that allows you to connect with live flat-file delimited data (CSV/TSV files), directly from any applications that support ODBC connectivity.

Access flat-file data like you would any standard database - read, write, and update etc. through a standard ODBC Driver interface.

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Analyze CSV Data in R


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

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

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

The DataSource property must be set to a valid local folder name.

Also, specify the IncludeFiles property to work with text files having extensions that differ from .csv, .tab, or .txt. Specify multiple file extensions in a comma-separated list. You can also set Extended Properties compatible with the Microsoft Jet OLE DB 4.0 driver. Alternatively, you can provide the format of text files in a Schema.ini file.

Set UseRowNumbers to true if you are deleting or updating in CSV. This will create a new column with the name RowNumber which will be used as key for that table.

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 CSV 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 CSV Source] Driver = CData ODBC Driver for CSV Description = My Description DataSource = MyCSVFilesFolder

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 CSV Data as an ODBC Data Source

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

conn <- odbcConnect("CData CSV Source")

Schema Discovery

The driver models CSV 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 CSV API.

customer <- sqlQuery(conn, "SELECT City, SUM(TotalDue) FROM Customer GROUP BY City", believeNRows=FALSE, rows_at_time=1)

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)