Google Analytics
A few topics I’ve been following have all converged into a single project that I’ll describe here and also demonstrate. I’ve blogged quite a bit about the googleAnalyticsR package which powers a number of my latest projects. The creator of that package, Mark Edmondson, has recently evangelized the use of Google Cloud Run and Google Cloud Build as cheap and effective ways to move your R projects into the cloud. I’ll be honest, until about 3 days ago I had no idea what these products did. I see the light now and am excited to share.
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Google Analytics
Google Analyitcs segments are a fantastic way to organize the results of an analysis. There are, however, a few limitations of using segments in GA:
- They cause reports to become sampled after 500,000 sessions (or 100M with GA360)
- Only 4 segments can be compared at one time
- Segments are saved under your Google account which makes sharing them a pain
- When comparing segments, it’s hard to tell how much they overlap
All of these limitations can be resolved by bringing your Google Analytics data into R with the googleAnalyticsR library, but this post will focus on #4 above: Understanding segment overlap. The code generating this blog post can be found here.
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Google Analytics
The CausalImpact R library measures the effects of an event on a response variable when establishing a traditional control group through a randomized trial is not a viable option. It does this by establishing a ‘synthetic control’ which serves as a baseline under which the actual data is compared.
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