Google Analytics (and a little bit of Twitter) and R

This repository has ~5 examples of using R with Google Analytics (plus one example of using R with Twitter). These examples are designed to work with the free version of Google Analytics with just the base tag (implemented). This makes the examples less impactful, but the idea is that it makes them a safe and easy jumping off point for anyone who wants to use them.

There have been various presentations that, ultimately, point to this page for follow-up information. Depending on if/when you saw one of those presentations, you may be looking for different slides:

• One version – presented at Columbus Web Analytics Wednesday in January 2019 – is available in Google Slides or as slides with audio voiceover on YouTube.
• Another version that was presented at Superweek 2019 is available on Slideshare.
• A version that was presented at the Atlanta chapter of the Digital Analytics Association in September 2019 is also available in Google Slides.

The presentations themselves cover different territory, but they all reference this page for examples.

There are Two Versions of Each Example

There are two versions of each example here:

• The R Notebook version – the RMarkdown version that has pretty extensively annotated code and explanations. This is the code you can use to tinker with and just run locally within RStudio
• The Shiny version (app.R) – this is the web-enabled experience that, if published to a Shiny server (including shinyapps.io) can be used by anyone to log in to their own account and explore the example without seeing/experiencing any of the code.

Links to “executed code showing output” (RPubs) as well as links to Shiny apps (Shiny) that can be tried out with no coding are included in the table below.

Example	Folder Name	Examples
Time-Normalized Pageviews	time-normalized-pageviews	RPubs / Shiny
Page-Level Analysis/Comparison with Two Metrics	page-analysis-two-metrics	RPubs / Shiny
(Light) Text Mining of On-Site Search Data	site-search-analysis	RPubs / Shiny
(Light) Text Mining of Twitter Followers	twitter-followers	RPubs
Regression Analysis with a Nominal / Categorical Value (Day of Week)	regression	RPubs / Shiny
Time-Series Decomposition / Holt-Winters Forecasting / Anomaly Detection	forecasting	RPubs / Shiny

A Few Notes on the Shiny Apps

The Shiny apps listed above should work fairly well, although they have extremely limited error handling. So, if you’re using them and run into an error, please do log an issue in this repository.

If you are interested in using the Shiny code itself and modifying the code for your own purposes, there are a few things to be aware of:

• You need to set up “Web service” credentials for a Google app that has Google Analytics API access
• Download the JSON file for those credentials
• Place that JSON file either in the same folder as app.R or somewhere accessible and then add "GAR_CLIENT_WEB_JSON="[path to that file]" to the .Renviron file that will be referenced by that app
• Basically, see the last post in this issue for details.
• The apps include Google Analytics tracking in them. IF you want to do that as well, then you need to get the JavaScript (recommended is to use Google Tag Manager) and place it in an include file (gtm.js) in the same folder as the app. If you do not want to track activity on your app using Google Analytics, then simply remove this line of code from the app: tags$head(includeScript("gtm.js")),.

Resources to Learn More

For a list of sites, blogs, books, podcasts, and other educational resources related to this topic, check out this resource.