Setup/Config

This example requires development versions of the googleAnalyticsR (>=0.5.0.9000) and ganalytics (>=0.10.4.9000) R packages available on GitHub, so the setup code below is a bit different (it has some additional code for loading a couple of packages from GitHub).

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(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

# A function to check that a sufficiently current version of a specific package is 
# installed and loaded. This isn't particularly elegant, but it works.
package_check <- function(package, min_version, github_location){
  # Check if ANY version of the package is installed. This is clunky, but p_load_current_gh
  # wasn't playing nice (and this does need some conditional checking.)
  if(package %in% rownames(installed.packages())){
    # IF a version of the package is already installed, then check the *version* of that
    # package to make sure it's current enough. If it'snot, then re-install from GitHub
    if(packageVersion(package) < min_version) {
      devtools::install_github(github_location)
    }
  } else {
    devtools::install_github(github_location)
  }
  # Load the package
  library(package, character.only = TRUE)
}

# As needed, install and load googleAnalyticsR and ganalytics from GitHub
package_check("googleAnalyticsR", "0.5.0.9001", "MarkEdmondson1234/googleAnalyticsR")
package_check("ganalytics", "0.10.4.9000", "jdeboer/ganalytics")

# 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

In this example, we’ll define the following filters: * Device category is desktop or tablet - a dimension filter using an OR condition. * New visitors using either a desktop or tablet device - a dimension filter involving both an AND and an OR condition. * At least one goal completion or transaction - a metric filter using an OR condition.

The above list of filters will be defined using ganalytics expressions as follows:

# Device category is desktop or tablet - a dimension filter using an OR condition.
desktop_or_tablet <- Expr(~deviceCategory == "desktop") | Expr(~deviceCategory == "tablet")

# New visitors using either a desktop or tablet device - a dimension filter involving both an AND and an OR condition.
new_desktop_and_tablet_visitors <- Expr(~userType == "new") & desktop_or_tablet

# At least one goal completion or transaction - a metric filter using an OR condition.
at_least_one_conversion <- Expr(~goalCompletionsAll > 0) | Expr(~transactions > 0)

We can now use googleAnalyticsR to pull the data. Chances are, the table below will only show a desktop row. That’s because the site we use for this doesn’t have a ton of traffic, and it rarely gets tablet conversions! But, trust us, this would return multiple rows on a site with more traffic and conversions.

# Pull the data
results <- google_analytics(
  viewId = view_id,
  date_range = c(start_date, end_date),
  metrics = c("users", "sessions", "goalCompletionsAll", "transactions"),
  dimensions = c("deviceCategory", "userType"),
  dim_filters = new_desktop_and_tablet_visitors,
  met_filters = at_least_one_conversion
)

# Display the results
results