Came across this nice e-book that addresses Google Analytics data in R Studio. This blog post uses/modifies the code from: https://michalbrys.gitbooks.io/r-google-analytics/content/chapter4/exploratory_data_analysis.html

The idea of running this code is to provide the summary metrics about the underlying dataset before doing a drill-down.

In this R script, we’re using the 2018 YTD data with dimensions = date, metric = sessions.

If you plot the data, you can immediately see some variability in the daily

You can now run the min/max queries to find out the range.

Let’s say we wanted to find out dates where sessions = 0. In the subset function, we’re running the parameter, sessions = 0.

You can now run a count on this data by running a conditional NRow function. This only counts rows where sessions = 0.

If you want to quickly know the summary stats for this data, you can run the summary function to provide this.

By running this, you now the min sessions = 0 was on 1st Jan (surprise, surprise). Max was on 13th Sep [shows that the traffic is increasing]. Mean/Median values are comparable with 7.2 and 7 while 50% of the website’s traffic values fall within 3 and 11 sessions [2nd Quartile - 3rd Quartile data]

Full code below: