Setup/Config

Be sure you’ve completed the steps on the Initial Setup page before running this code.

For the setup, we’re going to load a few libraries, load our specific Google Analytics credentials, and then authorize with Google.

# Load the necessary libraries. These libraries aren't all necessarily required for every
# example, but, for simplicity's sake, we're going ahead and including them in every example.
# The "typical" way to load these is simply with "library([package name])." But, the handy
# thing about using the approach below -- which uses the pacman package -- is that it will
# check that each package exists and actually install any that are missing before loading
# the package.
if (!require("pacman")) install.packages("pacman")
pacman::p_load(googleAnalyticsR,  # How we actually get the Google Analytics data
               tidyverse,         # Includes dplyr, ggplot2, and others; very key!
               devtools,          # Generally handy
               googleVis,         # Useful for some of the visualizations
               scales)            # Useful for some number formatting in the visualizations

# Authorize GA. Depending on if you've done this already and a .ga-httr-oauth file has
# been saved or not, this may pop you over to a browser to authenticate.
ga_auth(token = ".ga-httr-oauth")

# Set the view ID and the date range. If you want to, you can swap out the Sys.getenv()
# call and just replace that with a hardcoded value for the view ID. And, the start 
# and end date are currently set to choose the last 30 days, but those can be 
# hardcoded as well.
view_id <- Sys.getenv("GA_VIEW_ID")
start_date <- Sys.Date() - 31        # 30 days back from yesterday
end_date <- Sys.Date() - 1           # Yesterday

If that all runs with just some messages but no errors, then you’re set for the next chunk of code: pulling the data.

Pull the Data

We have to, first, define what we’re going to pivot. After that, it’s a pretty straightforward query.

# Start by defining the pivot object. See ?pivot_ga4() for details.
my_pivot_object <- pivot_ga4("deviceCategory",
                             metrics = "sessions")

# Pull the data. See ?google_analytics_4() for additional parameters. The anti_sample = TRUE
# parameter will slow the query down a smidge and isn't strictly necessary, but it will
# ensure you do not get sampled data.
ga_data <- google_analytics(viewId = view_id,
                              date_range = c(start_date, end_date),
                              metrics = "sessions",
                              dimensions = "medium",
                              pivots = list(my_pivot_object),
                              anti_sample = TRUE)

# Go ahead and do a quick inspection of the data that was returned. This isn't required,
# but it's a good check along the way.
head(ga_data)

This is the apparent/possible bug noted in the intro – we seem to be missing a column! Check out the issue on Github for an update.

Data Munging

The column headings are pretty ugly and redundant, and the sessions column isn’t necessarily one we’d want to keep, so let’s do a little cleanup.

# Remove the 'sessions' column
ga_data <- select(ga_data, -sessions)

# Use a little regEx to strip out "deviceCategory" and "sessions" from the column names
names(ga_data) <- gsub("deviceCategory\\.(.*)\\.sessions","\\1", names(ga_data))

# Check out the result of our handiwork
head(ga_data)

Data Visualization

This could be a nice little heatmap, but to do that with ggplot2, we’d have to gather it up into a tidy format. Basically…unpivot it! That seems silly – better to have pulled it unpivoted! I’m taking a moral stance and not jumping through that particular set of hoops for this example. So, no visualization on this one!