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Analyze YouTube Analytics Data in R

Use standard R functions and the development environment of your choice to analyze YouTube Analytics data with the CData JDBC Driver for YouTube Analytics.

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

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

  • Driver Class: Set this to cdata.jdbc.youtubeanalytics.YouTubeAnalyticsDriver
  • 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 YouTube Analytics:

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

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

YouTube Analytics uses the OAuth authentication standard. You can use the embedded CData OAuth credentials or you can register an application with Google to obtain your own.

In addition to the OAuth values, to access YouTube Analytics data set ChannelId to the Id of a YouTube channel. You can obtain the channel Id in the advanced account settings for your channel. If not specified, the channel of the currently authenticated user will be used.

If you want to generate content owner reports, specify the ContentOwnerId property. This is the Id of the copyright holder for content in YouTube's rights management system. The content owner is the person or organization that claims videos and sets their monetization policy.

Built-in Connection String Designer

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

java -jar cdata.jdbc.youtubeanalytics.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:youtubeanalytics:ContentOwnerId=MyContentOwnerId;ChannelId=MyChannelId;InitiateOAuth=GETANDREFRESH")

Schema Discovery

The driver models YouTube Analytics 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 YouTube Analytics API:

groups <- dbGetQuery(conn,"SELECT Snippet_Title, ContentDetails_ItemCount FROM Groups")

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

View(groups)

Plot YouTube Analytics Data

You can now analyze YouTube Analytics 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(groups$ContentDetails_ItemCount, main="YouTube Analytics Groups", names.arg = groups$Snippet_Title, horiz=TRUE)