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 Adobe Analytics credentials, and then authorize with Adobe.

# Load the necessary libraries. The 'typical' way to do this is with a series of
# 'library([library name])' calls. The use of the pacman package, though, adds a
# check that will install any missing packages before then loading them.
if (!require("pacman")) install.packages("pacman")
pacman::p_load(RSiteCatalyst,
               tidyverse, 
               jsonlite)    # Needed for unbox() in examples with inline segments

# Load the username, shared secret, and report suite ID
username <- Sys.getenv("ADOBE_API_USERNAME")
secret <- Sys.getenv("ADOBE_API_SECRET")

# Authorize Adobe Aalytics.
SCAuth(username, secret)

# Set the RSID 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 RSID. And, the start 
# and end date are currently set to choose the last 30 days, but those can be 
# hardcoded as well.
rsid <- Sys.getenv("ADOBE_RSID")
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 gets built up in what can feel a bit cumbersome. Essentially (but not necessarily intuitively), Adobe Analytics segments can have containers within containers. R has an analog: lists. A list can have a list as an element. So, we use lists to define inline segments.

# Create the actual segment object that we're going to use in the query. See ?segment_ga4()
# for details.
my_segment <- list(container = list(type = unbox("visits"),
                                    rules = data.frame(name = "Entry Page Includes 'excel'",
                                                       element = "entrypage",
                                                       operator = "contains",
                                                       value = "Intelligence")))

# Pull the data. See ?QueueRankes() for details on the arguments available. Note that
# we're using segment.inline as an argument here.
aa_data <- QueueRanked(rsid,
                       date.from = start_date,
                       date.to = end_date,
                       metrics = "pageviews",
                       elements = "page",
                       top = 10,
                       segment.inline = 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(aa_data)

Data Munging

In order to keep the order in the bar chart, we need to convert the page column to be a factor. We’ll reverse the order so that, when displayed in a bar chart, they’ll be in descending order.

# Convert page to be a factor
aa_data$name <- factor(aa_data$name,
                       levels = rev(aa_data$name))

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(aa_data, mapping = aes(x = name, 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.
print(gg)