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

This all gets built up in what can feel very cumbersome. Check out ?segment_ga4() for the documentation of how the segment gets built. Correctly, it describes this as a “hierarchy.” In practical terms, though, we build from the bottom up:

1. Define a segment element using segment_element(). This is just a single conditional statement.
2. Combine one or more segment elements together into a segment vector using segment_vector_simple(). There are a few options here, but we’re going to stick with the simple approach. And, it’s still going to feel redundant, because we’re only including a single segment element.
3. Combine one or more segment vectors into a segment definition using segment_define(). This may feel like it’s the same as the previous step, but, if you think about the segment builder in the web interface, it will start to make sense – there are two levels at which can combine multiple “things” together to define a segment. Alas! Here, again, we’re just including a single segment vector, so it all feels really cumbersome.
4. Put that into a segment object, which is what we’re actually going to use in the data. We actually give the segment a name here that will be returned in the results.
5. Actually pull the data, passing in the segment object as an argument.

In addition to the “hierarchy” messiness for a simple segment, there is also some list() messiness. Note, for instance, how my_segment_vector in the example code includes a list within a list. Use this example (and other examples on this site) as well as the ?segment_ga4() documentation to troubleshoot.

# Create a segment element object. See ?segment_element() for details.
my_segment_element <- segment_element("deviceCategory", 
                                   operator = "EXACT",
                                   type = "DIMENSION",
                                   expressions = "Mobile")

# Create a segment vector that has just one element. See ?segment_vector_simple() for details. Note
# that the element is wrapped in a list(). This is how you would include multiple elements in the
# definition.
my_segment_vector <- segment_vector_simple(list(list(my_segment_element)))

# Define the segment with just the one segment vector in it. See ?segment_define() for details.
my_segment_definition <- segment_define(list(my_segment_vector))

# Create the actual segment object that we're going to use in the query. See ?segment_ga4()
# for details.
my_segment <- segment_ga4("Mobile Sessions Only",
                                 session_segment = my_segment_definition)

# <whew>!!!

# Pull the data. See ?google_analytics_4() for additional parameters. Depending on what
# you're expecting back, you probably would want to use an "order" argument to get the
# results in descending order. But, we're keeping this example simple. Note, though, that
# we're still wrapping my_segment in a list() (of one element).
ga_data <- google_analytics(viewId = view_id,
                              date_range = c(start_date, end_date),
                              metrics = "pageviews",
                              dimensions = "pagePath",
                              segments = my_segment)

# 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)

Data Munging

Since we didn’t sort the data when we queried it, let’s go ahead and sort it here and grab just the top 10 pages.

# Using dplyr, sort descending and then grab the top 10 values. We also need to make the
# page column a factor so that the order will be what we want when we chart the data.
# This is a nuisance, but you get used to it. That's what the mutate function is doing.
ga_data_top_10 <- ga_data %>%
  arrange(-pageviews) %>% 
  top_n(10) %>% 
  mutate(pagePath = factor(pagePath,
                           levels = rev(pagePath)))

# Take a quick look at the result. 
head(ga_data_top_10)

Data Visualization

This won’t be the prettiest bar chart, but let’s make a horizontal bar chart with the data. Remember, in ggplot2, a horizontal bar chart is just a normal bar chart with coord_flip().

# Create the plot. Note the stat="identity"" (because the data is already aggregated) and
# the coord_flip(). And, I just can't stand it... added on the additional theme stuff to
# clean up the plot a bit more.
gg <- ggplot(ga_data_top_10, mapping = aes(x = pagePath, y = pageviews)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  theme_light() +
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.border = element_blank(),
        axis.title.y = element_blank(),
        axis.ticks.y = element_blank())

# Output the plot. You *could* just remove the "gg <-" in the code above, but it's
# generally a best practice to create a plot object and then output it, rather than
# outputting it on the fly.
gg