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 and the required branch of httr are currently under active 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` and the required branch of `httr`:

# Install devtools via CRAN
install.packages("devtools", dependencies = TRUE)
# Install ganalytics and httr branches via GitHub repositories.
library(devtools)
install_github(repo = "httr", username = "jdeboer")
install_github(repo = "ganalytics", username = "jdeboer")
# Install dependencies
install.packages(c("plyr", "stringr", "RJSONIO", "Rook", "httpuv"))
# End

Now, restart R. This is important to ensure you have a clean workspace and to avoid 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 (or choose an existing project if you have one already).
From the Services 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 API Access page, then click the blue button Create an OAuth 2.0 client ID.
Enter a unique Product Name (keep in mind you may want to add more Google services in future). This is the only required form field. Click Next.
Within Client ID Settings, select Installed application as the Application type and choose Other as the Installed application type. Click Create client ID.
Note your Client ID and Client secret.

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.
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 profile ID. This can be accessed from either:
- using the Google Analytics Query Explorer tool
- the Admin page in Google Analytics under Profile 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 profile ID of 33333333.
Note: A function for accessing your profile IDs within R will be available in the near future.
Return to R and execute the following, remembering to substitute profile_id with the profile ID you noted down:

If you completed step 3

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

If you did NOT complete step 3

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

library(ganalytics)
myQuery <- GaQuery( profile_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; visits 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:

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

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

library(ganalytics)
myQuery <- GaQuery( 123456789 )  # Replace this with your Google Analytics profile 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 visits
GaMetrics(myQuery) <- c("pageviews", "visits")
# These variations are also acceptable
GaMetrics(myQuery) <- c("ga:pageviews", "ga.Visits")

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 visits 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 visits
sundayExpr <- GaExpr("dayofweek", "=", "0")
# Expression to define organic search visits
organicExpr <- GaExpr("medium", "=", "organic")
# Expression to define organic search visits 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("visitCount", "!~", "^[0-3]$") # Made more than 3 visits
recentExpr <- GaExpr("daysSinceLastVisit", "~", "^[0-6]$") # 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("visitCount", "!~", "^[0-3]$") # Made more than 3 visits
recentExpr <- GaExpr("daysSinceLastVisit", "~", "^[0-6]$") # 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 "daysSinceLastVisit".
GaDimensions(myQuery) <- c("visitCount", "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 visit AND recent visits 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("-visitCount", "+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(visitCount), daysSinceLastVisit)
head(myData)
tail(myData)
# End

Example 10 - Visit segmentation

# Visit segmentation is expressed similarly to row filters and supports AND and OR combinations.
# Define a segment for visits 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 visits
GaSortBy(myQuery) <- "-visits"

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

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

# Visits 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) <- "visits"
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)

# Visits by day of week
visits_by_dayOfWeek <- ddply(myData, ~dayOfWeek, summarise, visits = sum(visits))
with(visits_by_dayOfWeek, barplot(visits, names.arg=dayOfWeek))

# Visits by hour of day
visits_by_hour <- ddply(myData, ~hour, summarise, visits = sum(visits))
with(visits_by_hour, barplot(visits, 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)

# Visits 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) <- "visits"
GaDimensions(myQuery) <- c("date", "dayOfWeek", "hour", "isMobile")
# Let's allow a maximum of 40000 rows (default is 10000)
GaMaxResults(myQuery) <- 40000

myData <- GetGaData(myQuery)

# Visits by hour of day and day of week
avg_visits_by_hour_wday_mobile <- ddply(myData, ~hour + dayOfWeek + isMobile, summarise, visits = mean(visits))

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

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

# End

itjil/ganalytics

ganalytics

Installation

1. Install the necessary packages into R

Warning

Prerequisites

Execute the following statements in R to install `ganalytics` and the required branch of `httr`:

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

2. Prepare your Google API application

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

4. Authenticate and attempt your first query with ganalytics

If you completed step 3

If you did NOT complete step 3

Examples

Asumptions

Example 1 - Setting the date range

Example 2 - Choosing what metrics to report

Example 3 - Selecting what dimensions to split your metrics by

Example 4 - Sort by

Example 5 - Row filters

Example 6 - Combining filters with AND

Example 7 - Combining filters with OR

Example 8 - Filters that combine ORs with ANDs

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

Example 10 - Visit segmentation

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

Example 12 - Using ggplot2

Useful references

itjil/ganalytics

ganalytics

Installation

1. Install the necessary packages into R

Warning

Prerequisites

Execute the following statements in R to install ganalytics and the required branch of httr:

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

2. Prepare your Google API application

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

4. Authenticate and attempt your first query with ganalytics

If you completed step 3

If you did NOT complete step 3

Examples

Asumptions

Example 1 - Setting the date range

Example 2 - Choosing what metrics to report

Example 3 - Selecting what dimensions to split your metrics by

Example 4 - Sort by

Example 5 - Row filters

Example 6 - Combining filters with AND

Example 7 - Combining filters with OR

Example 8 - Filters that combine ORs with ANDs

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

Example 10 - Visit segmentation

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

Example 12 - Using ggplot2

Useful references

Execute the following statements in R to install `ganalytics` and the required branch of `httr`: