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

There are two main parts to this:

1. Creating the dimension filter
2. Pulling the data while using that filter

Creating the dimension filter can feel convoluted. Or, at least, it can feel redundant. This is recognized – it’s the way the API is built and, for now, adding helper functions for “simple” filters would just add more confusion. Creating v4 dimension filters is a three-step process:

1. Create a “filter object” using the dim_filter() function. This is, essentially, “one condition” if you were building a filter in the web interface. You can have multiple filter objects, but, in this example, we’re only going to create one.
2. Create a “filter clause” using the filter_clause_ga4() function. This bundles together one or more filter objects. In this example, it’s going to be a 1-object bundle, which makes the code look a little funny.
3. Actually use the filter clause in the google_analytics_4() query.

# Create a dimension filter object. See ?dim_filter() for details. We're using a 
# regular expression here (REGEXP) but also could have used a "partial" match (PARTIAL).
# You should be using regEx in general. It's cleaner. And, the more comfortable you are
# with it, the easier it will be to use when you have more complex needs. </soapbox>
my_dim_filter_object <- dim_filter("pagePath", 
                                   operator = "REGEXP",
                                   expressions = ".*solutions.*")

# Now, put that filter object into a filter clause. If you had multiple filters, there
# are two aspects of this that wouldn't seem so weird. With one... they do seem weird:
# 1. The "filters" argument takes a list. So, you have to put your filter object in a
#    list. It's a one-element list.
# 2. The "operator" argument is moot -- it can be AND or OR...but you have to have
#    it be something, even though it doesn't do anything.
my_dim_filter_clause <- filter_clause_ga4(list(my_dim_filter_object),
                                          operator = "AND")

# 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.
ga_data <- google_analytics(viewId = view_id,
                              date_range = c(start_date, end_date),
                              metrics = "pageviews",
                              dimensions = "pagePath",
                              dim_filters = my_dim_filter_clause)

# 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