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 is a simple query with just three dimensions and one metric. We’re pulling the data at a daily level so we can plot the sparklines, but we’re then going to “roll it up” for the heatmap totals.

# Pull the data. See ?QueueTrended() for additional parameters. 
aa_data <- QueueTrended(rsid,
                        date.from = start_date,
                        date.to = end_date,
                        metrics = "pageviews",
                        elements = c("mobiledevicetype","sitesection"),
                        date.granularity = "day")

# 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

We’ve got three things we want to do here. Actually, two are “wants,” and one is a “need:”

• The Need – we need to aggregate/sum the page views so we can do the heatmap
• One Want – we want to convert the dimensions to factors with a “logical” order so that, more or less, both visualizations have page views descending from the top left corner to the bottom right corner
• Another Want – we want to only include the top 5 site sections by name, and then combine all of the others into an “Other” category

The first two of these are pretty straightforward to do. The third gets a bit messy, as we have to first total up the site sections to figure out which are the “top 5,” then go through an make all other site sections “(Other),” and then repeat the totalling process to treat “(Other)” as a single site section.

# Convert the datetime from POSIXlt to Date
aa_data$datetime <- as.Date(aa_data$datetime)

# Clean up the table a bit
aa_data <- aa_data %>% 
  mutate(device = ifelse(mobiledevicetype=="Other", "Desktop/Laptop", mobiledevicetype)) %>% 
  select(datetime, device, sitesection, pageviews) 

# Roll up to just be site section tables and then arrange the values for use in both grabbing the
# top 5 values and then, ultimately, in converting the site section column to a factor. This is 
# one of those things that is pretty standard to need to do. We're going to sort descending, but, 
# for one of the data frames, we're actually going to need to reverse the order as we create the 
# factors. This just comes with experimentation and experience.
sitesection_totals <- aa_data %>% 
  group_by(sitesection) %>% 
  summarise(pageviews = sum(pageviews)) %>% 
  arrange(-pageviews)

# Do the same thing, but for device. For this one, we actually want to sort 
# descending.
device_totals <- aa_data %>% 
  group_by(device) %>% 
  summarise(pageviews = sum(pageviews)) %>% 
  arrange(-pageviews)

# Get the top 5 site sections and then change all other values to be "(Other)"

# Get the top 5 site sections
sitesection_top5 <- top_n(sitesection_totals, n = 5, wt = pageviews)

# Add a column to the totals that uses "(Other)"
sitesection_totals <- sitesection_totals %>% 
  mutate(sitesection_new = ifelse(sitesection %in% sitesection_top5$sitesection,
                                  sitesection,
                                  "(Other)")) 

# Update the original data to use the new site section
aa_data <- aa_data %>% 
  left_join(sitesection_totals, by = c(sitesection = "sitesection"))

# Clean up the columns and update the totals by collapsing "(Other)" rows
aa_data_new <- aa_data %>% 
  mutate(pageviews = pageviews.x) %>% 
  select(-sitesection) %>% 
  mutate(sitesection = sitesection_new) %>% 
  select(datetime, device, sitesection, pageviews) %>% 
  group_by(datetime, device, sitesection) %>% 
  summarise(pageviews = sum(pageviews))

# Re-do the "totals" for sitesection
sitesection_totals <- aa_data_new %>% 
  group_by(sitesection) %>% 
  summarise(pageviews = sum(pageviews)) %>% 
  arrange(-pageviews)

# Convert the sitesection and device columns to factors in both of our main data frames.
aa_data_new$sitesection <- factor(aa_data_new$sitesection,
                         levels = sitesection_totals$sitesection)
aa_data_new$device <- factor(aa_data_new$device,
                         levels = device_totals$device)

# Remove the "datetime" component to get total page views for the heatmap
aa_data_totals <- aa_data_new %>% 
  group_by(device, sitesection) %>% 
  summarise(pageviews = sum(pageviews))

aa_data_totals$sitesection <- factor(aa_data_totals$sitesection,
                         levels = rev(sitesection_totals$sitesection))    # Reversing the factor order
aa_data_totals$device <- factor(aa_data_totals$device,
                         levels = device_totals$device)

# Let's just check that the aa_data_totals we created looks pretty normal
head(aa_data_totals)

Data Visualization

We’re going to break the standard for this site with this example by doing two visualizations:

• A heatmap of the data
• A similar grid, but with sparklines showing the data trended

# Create the plot
gg <- ggplot(aa_data_totals, mapping=aes(x = device, y = sitesection)) +
  geom_tile(aes(fill = pageviews), colour = "grey30") +
  geom_text(aes(label = format(pageviews, big.mark = ","))) +
  scale_fill_gradient(low = "white", high = "green") +    # Specify the gradient colors
  guides(fill = FALSE) +                                 # Remove the legend
  theme_light() +
  theme(panel.grid = element_blank(),
        panel.border = element_blank(),
        axis.ticks = 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)

# Create the plot
gg_sparklines <- ggplot(aa_data_new, mapping=aes(x = datetime, y = pageviews)) +
  geom_line() +
  facet_grid(sitesection ~ device, switch = "y") +
  theme_light() +
  theme(panel.grid = element_blank(),
        panel.border = element_rect(fill = NA, colour = "gray80"),
        panel.background = element_blank(),
        strip.background = element_blank(),
        strip.text = element_text(colour = "black"),
        strip.text.y = element_text(angle = 180, hjust = 1),
        axis.ticks = element_blank(),
        axis.text = element_blank(),
        axis.title.x = 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_sparklines)