ganalytics

Interact with Google Analytics using R

S4 classes and methods for interactive use of the Google Analytics core reporting API using R.

Installation

1. Install the necessary packages into R

Warning

• The ganalytics package is currently under development.
• The installation procedure below installs directly from the respective GitHub repositories.

Prerequisites

• Ensure you have installed the latest version of R
• If using Windows, you will also need the latest version of RTools
• For other operating systems, please refer to installation instructions for devtools

Execute the following statements in R to install ganalytics:

# Install the latest version of devtools via CRAN
install.packages("devtools", dependencies = TRUE)
# Install ganalytics via the GitHub repository.
devtools::install_github("jdeboer/ganalytics")
# End

Now, restart R. This is important to ensure you have a clean workspace to avoid possible errors.

2. Prepare your Google API application

• Browse to [Google API Console] (https://code.google.com/apis/console/)
• Check you are signed in to Google with the account you wish to use.
• Choose Create project from the Google API Console and give your project a name (or choose an existing project if you have one already). You may need to wait a minute for the project to be created once you submit the form.
• From the APIs page, set the status of Analytics API to ON.
• You will need to agree and accept the Google APIs and Analytics API Terms of Service to proceed.
• Go to the Credentials page and click the blue button Create new client ID.
• Select Installed application as the application type.
• Select Other as the Installed Application Type.
• Note your Client ID and Client secret or download the JSON file for future reference.

Note: For further information about Google APIs please refer to the References section at the end of this document.

3. Set your system environment variables (this is optional but recommended)

• Add the following two user variables:

	Variable name	Variable value
1	GANALYTICS_CONSUMER_ID	<Your client ID>
2	GANALYTICS_CONSUMER_SECRET	<Your client secret>

• To do this in Windows 7:

  • Search for and select "Edit environment variables for your account" from the Start menu.
  • Within the Environment variables window, add the above User variables by selecting New and entering the Variable name and Variable value, then click OK for each.
  • Click OK.
  • Restart your computer for the new environment variables to take effect.

• To do this in Mac OS there is a free open source utility called EnvPane

  Note: For other operating systems please refer to the Reference section at the end of this document.

4. Authenticate and attempt your first query with ganalytics

• To perform a query you will need to obtain your Google Analytics view ID. This can be accessed from either:

  • using the Google Analytics Query Explorer tool
  • the Admin page in Google Analytics under View Settings, or
  • the browser's address bar when viewing a report in Google Analytics - look for the digits between the letter 'p' and trailing '/', e.g. .../a11111111w22222222p33333333/ shows a view ID of 33333333.

  Note: A function for accessing your view IDs within R will be available in the near future.

• Return to R and execute the following, remembering to substitute view_id with the view ID you noted down:

If you completed step 3

library(ganalytics)
myQuery <- GaQuery( view_id )
GetGaData(myQuery)
# End

If you did NOT complete step 3

In addition to your view_id, also substitute client_id and client_secret below with your details from step 2

library(ganalytics)
myQuery <- GaQuery( view_id )
GetGaData(myQuery, key = client_id , secret = client_secret)
# End

• You should then be directed to http://accounts.google.com within your default web browser asking you to sign-in to your Google account if you are not already. Once signed-in you will be asked to grant read-only access to your Google Analytics account for the Google API product you created in step 1.
• Make sure you are signed in to the Google account you wish to use, then grant access by selecting "Allow access". You can then close the page and return back to R.
• If you have successfully executed all of the above R commands you should see the output of the default ganalytics query; sessions by day for the past 7 days.

Note: A small file named ganalytics_token.RDS will be saved to your home directory ('My Documents' in Windows) containing your new reusable authentication token.

Examples

As demonstrated in the installation steps above, before executing any of the following examples:

1. load the ganalytics package
2. generate a gaQuery object with a Google Analytics view ID assigned to it.

The following code performs these steps: (Remember to replace 123456789 with the view ID you wish to use.)

library(ganalytics)
myQuery <- GaQuery( 123456789 )  # Replace this with your Google Analytics view ID
# End

If you have just completed the installation steps, then you would have done this already.

Asumptions

The following examples assume you have successfully completed the above steps and that you have set your operating system's environment variables as described in step 3.

Example 1 - Setting the date range

# Set the date range from 1 January 2013 to 31 May 2013: (Dates are specified in the format "YYYY-MM-DD".)
GaDateRange(myQuery) <- c("2013-01-01", "2013-05-31")

myData <- GetGaData(myQuery)
summary(myData)

# Adjust the start date to 1 March 2013:
GaStartDate(myQuery) <- "2013-03-01"
# Adjust the end date to 31 March 2013:
GaEndDate(myQuery) <- "2013-03-31"

myData <- GetGaData(myQuery)
summary(myData)
# End

Example 2 - Choosing what metrics to report

# Report number of page views instead
GaMetrics(myQuery) <- "pageviews"

myData <- GetGaData(myQuery)
summary(myData)

# Report both pageviews and sessions
GaMetrics(myQuery) <- c("pageviews", "sessions")
# These variations are also acceptable
GaMetrics(myQuery) <- c("ga:pageviews", "ga.sessions")

myData <- GetGaData(myQuery)
summary(myData)
# End

Example 3 - Selecting what dimensions to split your metrics by

# Similar to metrics, but for dimensions
GaDimensions(myQuery) <- c("year", "week", "dayOfWeek", "hour")

# Lets set a wider date range
GaDateRange(myQuery) <- c("2012-10-01", "2013-03-31")

myData <- GetGaData(myQuery)
head(myData)
tail(myData)
# End

Example 4 - Sort by

# Sort by descending number of pageviews
GaSortBy(myQuery) <- "-pageviews"

myData <- GetGaData(myQuery)
head(myData)
tail(myData)
# End

Example 5 - Row filters

# Filter for Sunday sessions only
sundayExpr <- GaExpr("dayofweek", "=", "0")
GaFilter(myQuery) <- sundayExpr

myData <- GetGaData(myQuery)
head(myData)

# Remove the filter
GaFilter(myQuery) <- NULL

myData <- GetGaData(myQuery)
head(myData)
# End

Example 6 - Combining filters with AND

# Expression to define Sunday sessions
sundayExpr <- GaExpr("dayofweek", "=", "0")
# Expression to define organic search sessions
organicExpr <- GaExpr("medium", "=", "organic")
# Expression to define organic search sessions made on a Sunday
sundayOrganic <- GaAnd(sundayExpr, organicExpr)
GaFilter(myQuery) <- sundayOrganic

myData <- GetGaData(myQuery)
head(myData)

# Let's concatenate medium to the dimensions for our query
GaDimensions(myQuery) <- c(GaDimensions(myQuery), "medium")

myData <- GetGaData(myQuery)
head(myData)
# End

Example 7 - Combining filters with OR

# In a similar way to AND
loyalExpr <- GaExpr("sessionCount", "!~", "^[0-3]$") # Made more than 3 sessions
recentExpr <- GaExpr("daysSinceLastSession", "~", "^[0-6]$") # Visited sometime within the past 7 days.
loyalOrRecent <- GaOr(loyalExpr, recentExpr)
GaFilter(myQuery) <- loyalOrRecent

myData <- GetGaData(myQuery)
summary(myData)
# End

Example 8 - Filters that combine ORs with ANDs

loyalExpr <- GaExpr("sessionCount", "!~", "^[0-3]$") # Made more than 3 sessions
recentExpr <- GaExpr("daysSinceLastSession", "~", "^[0-6]$") # Visited sometime within the past 7 days.
loyalOrRecent <- GaOr(loyalExpr, recentExpr)
sundayExpr <- GaExpr("dayOfWeek", "=", "0")
loyalOrRecent_Sunday <- GaAnd(loyalOrRecent, sundayExpr)
GaFilter(myQuery) <- loyalOrRecent_Sunday

myData <- GetGaData(myQuery)
summary(myData)

# Perform the same query but change which dimensions to view
# - Notice "daysSince" is conveniently accepted as a partial match for "daysSinceLastSession".
GaDimensions(myQuery) <- c("sessionCount", "daysSince", "dayOfWeek")

myData <- GetGaData(myQuery)
summary(myData)
# End

Example 9 - Sorting 'numeric' dimensions (continuing from example 8)

# Continuing from example 8...

# Change filter to loyal session AND recent sessions AND visited on Sunday
loyalAndRecent_Sunday <- GaAnd(loyalExpr, recentExpr, sundayExpr)
GaFilter(myQuery) <- loyalAndRecent_Sunday

# Sort by decending visit count and ascending days since last visit.
GaSortBy(myQuery) <- c("-sessionCount", "+daysSince")
myData <- GetGaData(myQuery)
head(myData)

# Notice that Google Analytics' Core Reporting API doesn't recognise 'numerical' dimensions as
# ordered factors when sorting. We can use R to sort instead, using a plyr::arrange function.
library(plyr)
myData <- arrange(myData, desc(sessionCount), daysSinceLastSession)
head(myData)
tail(myData)
# End

Example 10 - Session segmentation

# Visit segmentation is expressed similarly to row filters and supports AND and OR combinations.
# Define a segment for sessions where a "thank-you", "thankyou" or "success" page was viewed.
thankyouExpr <- GaExpr("pagePath", "~", "thank\\-?you|success")
GaSegment(myQuery) <- thankyouExpr

# Reset the filter
GaFilter(myQuery) <- NULL

# Split by traffic source and medium
GaDimensions(myQuery) <- c("source", "medium")

# Sort by decending number of sessions
GaSortBy(myQuery) <- "-sessions"

myData <- GetGaData(myQuery)
head(myData)
# End

Example 11 - Using automatic pagination to get more than 10,000 rows of data per query

# Sessions by date and hour for the years 2011 (leap year) and 2012: 2 * 365.5 * 24 = 17544 rows
# First let's clear any filters or segments defined previously
GaFilter(myQuery) <- NULL
GaSegment(myQuery) <- NULL
# Define our date range
GaDateRange(myQuery) <- c("2011-01-01", "2012-12-31")
# Define our metrics and dimensions
GaMetrics(myQuery) <- "sessions"
GaDimensions(myQuery) <- c("date", "dayOfWeek", "hour")
# Let's allow a maximum of 17544 rows (default is 10000)
GaMaxResults(myQuery) <- 17544

myData <- GetGaData(myQuery)
nrow(myData)

## Let's use plyr::ddply to analyse the data
library(plyr)

# Sessions by day of week
sessions_by_dayOfWeek <- ddply(myData, ~dayOfWeek, summarise, sessions = sum(sessions))
with(sessions_by_dayOfWeek, barplot(sessions, names.arg=dayOfWeek))

# Sessions by hour of day
sessions_by_hour <- ddply(myData, ~hour, summarise, sessions = sum(sessions))
with(sessions_by_hour, barplot(sessions, names.arg=hour))
# End

Example 12 - Using ggplot2

To run this example first install ggplot2 if you haven't already.

install.packages("ggplot2")
#End

Once installed, then run the following example.

library(ggplot2)
library(plyr)

# Sessions by date and hour for the years 2011 (leap year) and 2012: 2 * 365.5 * 24 = 17544 rows
# First let's clear any filters or segments defined previously
GaFilter(myQuery) <- NULL
GaSegment(myQuery) <- NULL
# Define our date range
GaDateRange(myQuery) <- c("2011-01-01", "2012-12-31")
# Define our metrics and dimensions
GaMetrics(myQuery) <- "sessions"
GaDimensions(myQuery) <- c("date", "dayOfWeek", "hour", "isMobile")
# Let's allow a maximum of 40000 rows (default is 10000)
GaMaxResults(myQuery) <- 40000

myData <- GetGaData(myQuery)

# Sessions by hour of day and day of week
avg_sessions_by_hour_wday_mobile <- ddply(myData, ~hour + dayOfWeek + isMobile, summarise, sessions = mean(sessions))

# Relabel the days of week
levels(avg_sessions_by_hour_wday_mobile$dayOfWeek) <- c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat")

# Plot the summary data
qplot(
  x = hour,
  y = sessions,
  data = avg_sessions_by_hour_wday_mobile,
  facets = ~dayOfWeek,
  fill = isMobile,
  geom = "bar",
  position = "stack",
  stat = "identity"
)

# End

Useful references

1. Google Analytics Core Reporting API reference guide
2. Google Analytics Dimensions and Metrics reference
3. Creating a Google API project
4. Generating an OAuth 2.0 client ID for Google APIs
5. Using OAuth 2.0 for Installed Applications
6. Setting environment variables in Windows XP
7. Setting environment variables in earlier versions of OSX
8. EnvPane utility for setting environment variables in OSX
9. Setting environment variables in Ubuntu Linux