In MarkEdmondson1234/googleAnalyticsR: Google Analytics API into R
The Management API v3 covers all API calls that are not data reporting related and are for getting meta information about your Google Analytics account or to change account settings.
Account structure
Shows the details for your account structure (Accounts > Web-properties > Views (profiles)) including creation time, user permissions etc.
The most day-to-day useful function is ga_account_list() which summarises all account web properties and views available to your user.
ga_account_list() - Get account summary including the ViewIdga_accounts() - Get account metadata for your userga_webproperty() - Get web property ga_webproperty_list() - List web properties for a particular accountIdga_view() - Get single View (Profile)ga_view_list() - List Views (Profile) for a particular accountId/webPropertyId
You can then also get the meta-data for that particular entity via ga_webproperty() or ga_view()
# ga_account_list is most commonly used
# (restricted to top 10 with the head() function)
head(ga_account_list(), n = 10)
## # A tibble: 10 x 11
## accountId accountName internalWebProp… level websiteUrl type starred
## <chr> <chr> <chr> <chr> <chr> <chr> <lgl>
## 1 79301104 cloudyr 118427305 STAN… http://cl… WEB NA
## 2 54516992 Demo Accou… 87479473 STAN… https://s… WEB NA
## 3 54516992 Demo Accou… 87479473 STAN… https://s… WEB NA
## 4 54516992 Demo Accou… 87479473 STAN… https://s… WEB NA
## 5 47480439 MarkEdmond… 78737968 STAN… http://ma… WEB NA
## 6 47480439 MarkEdmond… 78742336 STAN… http://ma… WEB NA
## 7 54019251 Sunholo We… 86845257 STAN… https://g… WEB NA
## 8 54019251 Sunholo We… 86845257 STAN… https://g… WEB NA
## 9 54019251 Sunholo We… 94325478 PREM… http://ma… WEB NA
## 10 54019251 Sunholo We… 94325478 PREM… http://ma… WEB TRUE
## # … with 4 more variables: webPropertyId <chr>, webPropertyName <chr>, viewId <chr>,
## # viewName <chr>
# this only lists account meta-data
ga_accounts()
## id name created updated
## 1 47480439 MarkEdmondson 2014-01-25 12:17:05 2019-06-01 08:09:58
## 2 54019251 Sunholo Websites 2014-08-20 19:17:05 2019-07-31 18:51:55
## 3 54516992 Demo Account 2014-09-06 17:53:33 2019-06-10 17:56:03
## 4 79301104 cloudyr 2016-06-14 14:29:50 2016-06-14 14:29:50
## permissions.effective
## 1 COLLABORATE,EDIT,MANAGE_USERS,READ_AND_ANALYZE
## 2 COLLABORATE,EDIT,MANAGE_USERS,READ_AND_ANALYZE
## 3 READ_AND_ANALYZE
## 4
# this gives meta-data for all web-properties for this accountId
ga_webproperty_list(47480439)
## id accountId internalWebPropertyId name
## 1 UA-47480439-1 47480439 78742336 markedmondson.me
## 2 UA-47480439-2 47480439 78737968 MarkEdmondson oldGA
## websiteUrl level profileCount industryVertical dataRetentionTtl
## 1 http://markedmondson.me STANDARD 1 HOBBIES_AND_LEISURE MONTHS_26
## 2 http://markedmondson.me STANDARD 1 HOBBIES_AND_LEISURE INDEFINITE
## dataRetentionResetOnNewActivity created updated
## 1 TRUE 2014-01-25 12:17:05 2014-01-25 12:17:06
## 2 TRUE 2014-01-25 12:19:20 2018-04-12 11:37:38
## defaultProfileId permissions.effective
## 1 <NA> COLLABORATE,EDIT,MANAGE_USERS,READ_AND_ANALYZE
## 2 81416156 COLLABORATE,EDIT,MANAGE_USERS,READ_AND_ANALYZE
# this is meta-data for one particular web-property
ga_webproperty(accountId = 47480439, webPropertyId = "UA-47480439-1")
## $id
## [1] "UA-47480439-1"
##
## $kind
## [1] "analytics#webproperty"
##
## $selfLink
## [1] "https://www.googleapis.com/analytics/v3/management/accounts/47480439/webproperties/UA-47480439-1"
##
## $accountId
## [1] "47480439"
##
## $internalWebPropertyId
## [1] "78742336"
##
## $name
## [1] "markedmondson.me"
##
## $websiteUrl
## [1] "http://markedmondson.me"
##
## $level
## [1] "STANDARD"
##
## $profileCount
## [1] 1
##
## $industryVertical
## [1] "HOBBIES_AND_LEISURE"
##
## $dataRetentionTtl
## [1] "MONTHS_26"
##
## $dataRetentionResetOnNewActivity
## [1] TRUE
##
## $permissions
## $permissions$effective
## [1] "COLLABORATE" "EDIT" "MANAGE_USERS" "READ_AND_ANALYZE"
##
##
## $created
## [1] "2014-01-25T12:17:05.492Z"
##
## $updated
## [1] "2014-01-25T12:17:06.625Z"
##
## $parentLink
## $parentLink$type
## [1] "analytics#account"
##
## $parentLink$href
## [1] "https://www.googleapis.com/analytics/v3/management/accounts/47480439"
##
##
## $childLink
## $childLink$type
## [1] "analytics#profiles"
##
## $childLink$href
## [1] "https://www.googleapis.com/analytics/v3/management/accounts/47480439/webproperties/UA-47480439-1/profiles"
# this is meta-data for the views under this accountId/webPropertyId
ga_view_list(accountId = 47480439, webPropertyId = "UA-47480439-1")
## id accountId webPropertyId internalWebPropertyId name currency
## 1 81416941 47480439 UA-47480439-1 78742336 All Web Site Data USD
## timezone websiteUrl type created
## 1 Europe/Copenhagen http://markedmondson.me WEB 2014-01-25 12:17:05
## updated eCommerceTracking
## 1 2015-06-14 13:18:53 FALSE
## permissions.effective
## 1 COLLABORATE,EDIT,MANAGE_USERS,READ_AND_ANALYZE
# this is meta-data for this particular viewId (profileId)
ga_view(accountId = 47480439, webPropertyId = "UA-47480439-1", profileId = 81416941)
## $id
## [1] "81416941"
##
## $kind
## [1] "analytics#profile"
##
## $selfLink
## [1] "https://www.googleapis.com/analytics/v3/management/accounts/47480439/webproperties/UA-47480439-1/profiles/81416941"
##
## $accountId
## [1] "47480439"
##
## $webPropertyId
## [1] "UA-47480439-1"
##
## $internalWebPropertyId
## [1] "78742336"
##
## $name
## [1] "All Web Site Data"
##
## $currency
## [1] "USD"
##
## $timezone
## [1] "Europe/Copenhagen"
##
## $websiteUrl
## [1] "http://markedmondson.me"
##
## $type
## [1] "WEB"
##
## $permissions
## $permissions$effective
## [1] "COLLABORATE" "EDIT" "MANAGE_USERS" "READ_AND_ANALYZE"
##
##
## $created
## [1] "2014-01-25T12:17:05.492Z"
##
## $updated
## [1] "2015-06-14T13:18:53.638Z"
##
## $eCommerceTracking
## [1] FALSE
##
## $parentLink
## $parentLink$type
## [1] "analytics#webproperty"
##
## $parentLink$href
## [1] "https://www.googleapis.com/analytics/v3/management/accounts/47480439/webproperties/UA-47480439-1"
##
##
## $childLink
## $childLink$type
## [1] "analytics#goals"
##
## $childLink$href
## [1] "https://www.googleapis.com/analytics/v3/management/accounts/47480439/webproperties/UA-47480439-1/profiles/81416941/goals"
Helper functions
These are helper utility functions to work with Google Analytics data.
ga_meta() - get a data.frame of available dimensions and metrics, or just use meta directly to get the version that comes with the library.
# you can just use `meta` as is to get the available metrics,
# here we just return the first 5 columns and rows for brevity
head(meta[,1:5])
## name type dataType group status
## 1 ga:userType DIMENSION STRING User PUBLIC
## 2 ga:visitorType DIMENSION STRING User DEPRECATED
## 3 ga:sessionCount DIMENSION STRING User PUBLIC
## 4 ga:visitCount DIMENSION STRING User DEPRECATED
## 5 ga:daysSinceLastSession DIMENSION STRING User PUBLIC
## 6 ga:userDefinedValue DIMENSION STRING User PUBLIC
# or ensure an up to date version by calling the metadata API.
head(ga_meta())[,1:5]
## name type dataType group status
## 1 ga:userType DIMENSION STRING User PUBLIC
## 2 ga:visitorType DIMENSION STRING User DEPRECATED
## 3 ga:sessionCount DIMENSION STRING User PUBLIC
## 4 ga:visitCount DIMENSION STRING User DEPRECATED
## 5 ga:daysSinceLastSession DIMENSION STRING User PUBLIC
## 6 ga:userDefinedValue DIMENSION STRING User PUBLIC
ga_aggregate - aggregate data down to the dimensions you specify, treating metrics correctly regarding if they should be averaged or summed. This is used internally when using anti-sampling, and is also useful to respond to dimension selections in Shiny dashboards, where you don't want to perform a new API call each time you need aggregated data. Download all data first in a more unaggregated form, then use this function.
# use `aggregateGAData` so you can on the fly create summary data
ga_data <- google_analytics(81416156,
date_range = c("10daysAgo", "yesterday"),
metrics = c("sessions","bounceRate"), dimensions = c("hour","date"))
## [36mℹ[39m 2020-05-26 11:40:05 > Downloaded [ 240 ] rows from a total of [ 240 ].
head(ga_data)
## hour date sessions bounceRate
## 1 00 2020-05-16 10 60.00000
## 2 00 2020-05-17 8 37.50000
## 3 00 2020-05-18 9 44.44444
## 4 00 2020-05-19 8 50.00000
## 5 00 2020-05-20 11 63.63636
## 6 00 2020-05-21 8 50.00000
# if we want totals per hour over the dates:
ga_aggregate(ga_data[,c("hour","sessions")], agg_names = "hour")
## # A tibble: 24 x 2
## hour sessions
## <chr> <dbl>
## 1 00 92
## 2 01 64
## 3 02 58
## 4 03 64
## 5 04 70
## 6 05 74
## 7 06 59
## 8 07 66
## 9 08 82
## 10 09 89
## # … with 14 more rows
# it knows not to sum metrics that are rates:
ga_aggregate(ga_data[,c("hour","bounceRate")], agg_names = "hour")
## # A tibble: 24 x 2
## hour bounceRate
## <chr> <dbl>
## 1 00 53.4
## 2 01 65.1
## 3 02 69.3
## 4 03 60.5
## 5 04 65.4
## 6 05 79.9
## 7 06 78.8
## 8 07 66.1
## 9 08 75.4
## 10 09 66.3
## # … with 14 more rows
ga_allowed_metric_dim() - Create named list of allowed GA metrics/dimensions. This is useful to have nice looking labels for dimension and metric selection pickers, or to create lookup tables.
amd <- ga_allowed_metric_dim()
head(amd)
## Users New Users % New Sessions
## "ga:users" "ga:newUsers" "ga:percentNewSessions"
## 1 Day Active Users 7 Day Active Users 14 Day Active Users
## "ga:1dayUsers" "ga:7dayUsers" "ga:14dayUsers"
User management
ga_users_list() - list user access to your Google Analytics accounts, web properties or viewsga_users_delete() - delete user access via emailga_users_delete_linkid() - delete user access via the linkIdga_users_add() - add users to accountsga_users_update() - update a user
User management follows the recommended workflows outlined in this Google reference article.
The ga_users_* functions use the management APIs batching endpoint, to take advantage of the special increased quota limits when dealing with User management that API offers. For example, every 30 API calls that are batched that deal with user management only increments one API call against your management API quota. You can have 300 API calls in one batch (e.g. that costs 10 API calls against the quota), and googleAnalyticsR will split any list of emails into 300 emails per batch for you, so you can send in all emails you have.
By default, each Google Cloud Project gets 50 write requests per project per day, which if using batching means 1500 write/delete/update user operations per day.
You will need to use your own Google Cloud Platform project if using any write API operations seriously, otherwise you will be at the mercy of the shared googleAnalyticsR's quota limits. See the section in the Setup page on how to setup using your own Google Project
To see which users are attached, use ga_users_list(). Each user email is referenced under the userRef.email column, whereas the unique IDs used to see which user is linked to where is the linkId which is unique to each user and link to your account, web property or view/profile.
Users can be attached at various levels in the hierarchy so you can call the function at various viewing angles.
# default will list all users that match the id you supply
ga_users_list(47480439)
ga_users_list(47480439, webPropertyId = "UA-47480439-2")
ga_users_list(47480439, webPropertyId = "UA-47480439-2", viewId = 81416156)
# only list users who have account level access
ga_users_list(47480439, webPropertyId = NULL, viewId = NULL)
# only list users who have webProperty and above access
ga_users_list(47480439, webPropertyId = "UA-47480439-2", viewId = NULL)
Users can be attached at account, web-property and view level.
Batching is incorporated for adding users, so you can pass in a vector of emails to add.
ga_users_add(c("the_email@company.com", "another_email@company.com"),
permissions = "EDIT", accountId = 47480439)
To delete a user from all accounts, web-properties and views takes a few API calls to ga_users_list() to find all the possible links, which is done behind the scenes of ga_users_delete(). You can pass the function multiple emails to delete many at once, which is done via batching:
ga_users_list(47480439)
ga_users_delete("the_email@company.com", 47480439)
# delete many emails at once
ga_users_delete(c("the_email@company.com", "another_email@company.com"), accountId = 47480439)
For more fine grained control, the below examples shows a workflow to list and delete user access for one user at View level using the linkId.
# get the linkId for the user you want to delete
ga_users_list(47480439, webPropertyId = "UA-47480439-2", viewId = 81416156)
ga_users_delete_linkid("81416156:114834495587136933146", 47480439,
webPropertyId = "UA-47480439-2", viewId = 81416156)
# check its gone
ga_users_list(47480439, webPropertyId = "UA-47480439-2", viewId = 81416156)
# can only delete at level user has access, the above deletion woud have failed if via:
ga_users_delete_linkid("47480439:114834495587136933146", 47480439)
You can also modify existing users via ga_users_update() - the below modifies on the web property level:
# the update to perform
o <- list(permissions = list(local = list("EDIT")))
ga_users_update("UA-123456-1:1111222233334444",
update_object = o,
accountId = 123456,
webPropertyId = "UA-123456-1")
When using linkIds, make sure to use the correct linkId for the level of account, web-property or view you are operating on - the linkId starts with the accountId, UA code of web property, or ViewId number to distinguish between the various types.
Custom variables
Custom variable management for a Google Analytics property.
ga_custom_vars() - get meta data for a specific custom variablega_custom_vars_list() - list all custom dimensions or metricsga_custom_vars_create() - create a new custom variablega_custom_vars_patch() - update an existing custom variable
Using these you can scale setting up custom dimensions across your Google Analytics web properties. Custom dimension updates are governed by the same rules as when you create them in the web interface, as this Google article on custom dimensions and metrics explains.
You can't delete custom dimensions, you can only de-activate them for reuse later on.
A workflow is shown below that creates, then updates a custom variable to a new name.
# create custom var
ga_custom_vars_create("my_custom_dim",
index = 15,
accountId = 54019251,
webPropertyId = "UA-54019251-4",
scope = "HIT",
active = FALSE)
# view custom dimension in list
ga_custom_vars_list(54019251, webPropertyId = "UA-54019251-4", type = "customDimensions")
# change a custom dimension
ga_custom_vars_patch("ga:dimension7",
accountId = 54019251,
webPropertyId = "UA-54019251-4",
name = "my_custom_dim2",
scope = "SESSION",
active = TRUE)
# view custom dimensions again to see change
ga_custom_vars_list(54019251, webPropertyId = "UA-54019251-4", type = "customDimensions")
AdWords
ga_adwords() Get Google Analytics - AdWords Link meta dataga_adwords_list() List AdWordsga_adwords_add_linkid() Create a link between and Adwords (Google ads) account and a Google Analytics propertyga_adwords_delete_linkid() Delete a Google Analytics webProperty-Google Ads link
For many websites Google search advertising, aka Adwords, is a key traffic channel which comes with its own reporting system.
Linking Google Analytics with Adwords makes sense as it allows Google ads data to be included in Analytics reports and vice versa.
For instance linking the two together can add to the Analytics reports more than 30 metrics and dimensions related to ads (such as the keywords that triggered an ad or the ad ROI)
You can use the function ga_adwords_list() to request a list of existing links between Google Analytics and Adwords associated with your user.
The ga_adwords() function provides meta data about a specific Google Analytics - Adwords link.
If a link between Analytics and Google ads has not been established, this can be done easily via the Management API using the ga_adwords_add_linkid() function.
A requirement is that your email has access rights to both the Analytics and Adwords account.
You will also need the Adwords customer ID which can be obtained via the Adwords API or UI.
Also required is the id of the Google Analytics account and associated GA property to which the Adwords account will be linked.
This operation will create a new entityAdWordsLinks resource.
To remove this entityAdWordsLinks later if needed the ga_adwords_delete_linkid() function can be used.
# Lists webProperty-Google Ads links
ga_adwords_list(accountId = 65973592, webPropertyId = "UA-65973592-1")
# Get information about a web property-Google Ads link
ga_adwords(accountId = 65973592, webPropertyId = "UA-65973592-1", webPropertyAdWordsLinkId = "QrcfI2DTSMayqbrLiHYUqw")
# establish a new link between GA and Adwords
ga_adwords_add_linkid(adwordsAccountId = "280-234-7592", linkName = "Google Ads Link", accountId = "65973592", webPropertyId = "UA-65973592-1")
#check that it has been added
ga_adwords_list(accountId = 65973592, webPropertyId = "UA-65973592-1")
# delete the link
ga_adwords_delete_linkid(accountId = 65973592, webPropertyId ="UA-65973592-1", webPropertyAdWordsLinkId = "ezW2dyaiQcGheWRAo69nCw")
#check that the link has been removed
ga_adwords_list(accountId = 65973592, webPropertyId = "UA-65973592-1")
Custom Data Sources
See and upload custom data sources to Google Analytics
ga_custom_datasource() - List Custom Data Sourcesga_custom_upload() - Custom Data Source Upload Statusga_custom_upload_file()- Upload a file to GA custom uploadsga_custom_upload_list() - List the files in a GA custom upload
Experiments
ga_experiment() - Experiments Meta dataga_experiment_list() - List Experiments
View Filters
The filter edit functions are contributed by @zselinger which allow you to update filters for your Google Analytics views at scale.
ga_filter() - Get specific filter for accountga_filter_add() - Create a new filter and add it to the view (optional).ga_filter_apply_to_view() - Apply an existing filter to view.ga_filter_delete() - Delete a filter from account or remove from view.ga_filter_list() - List filters for accountga_filter_update() - Updates an existing filter.ga_filter_update_filter_link() - Update an existing profile filter link. Patch semantics supportedga_filter_view() - Get specific filter for view (profile)ga_filter_view_list() - List filters for view (profile)
Goals
ga_goal() - Get goalga_goal_list() - List goalsga_goal_add() - Create and add Goals to a web propertyga_goal_update() - Modify an existing goal
Example: Copying GA goals
We can programatically copy goal configurations from any GA view (the source) to another GA view (the destination) within an entirely separate property. This can be helpful to maintain standardised goal configurations for multiple properties, and also for GA migrations.
This example will first copy a single goal from one property to another, then use the map() function from purrr to copy multiple goals at once.
We use the Google Analytics demo account as the source view for this example - you can read more about the demo account here.
Auth and setup
We authenticate and load the necessary packages, following the instructions from the documentation.
library(tidyverse)
library(stringr)
library(rlist)
## Error in library(rlist): there is no package called 'rlist'
Copying a single goal
For this first section, we copy a single Goal from one view to another. To achieve this, we will:
- Retrieve the accountId, webPropertyId and viewId from our source GA view (the one where our goal to be copied over currently exists).
- Get a list of the goals currently configured in the source GA view.
- Retrieve the accountId, webPropertyId and viewId from our destination GA view (the one where our goal will be copied to).
- Get the configuration details of a single goal to be copied.
- Submit the goal details to our destination property.
1. Retrieve the accountId, webPropertyId and viewId from our source GA view
We use the ga_account_list() function, which returns an account summary for all available accounts as a dataframe.
Then, we filter the dataframe down to a specific account and view name using the filter() and str_detect() functions from the tidyverse.
You can supply your own filter conditions as necessary, to locate your desired view information.
# Account info and list of goals for source, to be copied
source_account <- ga_account_list() %>%
filter(str_detect(accountName, "Demo Account"), str_detect(viewName, "Master"))
source_account
## # A tibble: 1 x 11
## accountId accountName internalWebProp… level websiteUrl type starred webPropertyId
## <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr>
## 1 54516992 Demo Accou… 87479473 STAN… https://s… WEB NA UA-54516992-1
## # … with 3 more variables: webPropertyName <chr>, viewId <chr>, viewName <chr>
2. Get a list of the goals currently configured from our source GA view
Now that we have the account summary, we can use the view details to get a list of goal data, with each item in the list showing goal details.
source_goals <- ga_goal_list(source_account$accountId, source_account$webPropertyId, source_account$viewId)
source_goals
## id accountId webPropertyId internalWebPropertyId profileId name
## 1 1 54516992 UA-54516992-1 87479473 92320289 Purchase Completed
## 2 2 54516992 UA-54516992-1 87479473 92320289 Engaged Users
## 3 3 54516992 UA-54516992-1 87479473 92320289 Registrations
## 4 4 54516992 UA-54516992-1 87479473 92320289 Entered Checkout
## value active type created updated
## 1 0 TRUE URL_DESTINATION 2014-10-10 20:13:47 2016-11-10 23:07:35
## 2 0 TRUE VISIT_NUM_PAGES 2015-02-23 16:18:06 2015-04-14 12:02:21
## 3 0 TRUE URL_DESTINATION 2015-02-27 12:15:01 2016-07-26 16:14:09
## 4 0 TRUE URL_DESTINATION 2015-07-20 23:57:22 2016-07-26 16:19:40
## urlDestinationDetails.url
## 1 /ordercompleted\\.html
## 2 <NA>
## 3 /registersuccess\\.html
## 4 /yourinfo\\.html|/guestregister\\.html|/shipping\\.html
## urlDestinationDetails.caseSensitive urlDestinationDetails.matchType
## 1 FALSE REGEX
## 2 NA <NA>
## 3 FALSE REGEX
## 4 FALSE REGEX
## urlDestinationDetails.firstStepRequired
## 1 TRUE
## 2 NA
## 3 FALSE
## 4 FALSE
## urlDestinationDetails.steps
## 1 1:4,c("Cart", "Billing and Shipping", "Payment", "Review"),c("/basket\\\\.html", "/yourinfo\\\\.html", "/payment\\\\.html", "/revieworder\\\\.html")
## 2
## 3
## 4
## visitNumPagesDetails.comparisonType visitNumPagesDetails.comparisonValue
## 1 <NA> <NA>
## 2 GREATER_THAN 10
## 3 <NA> <NA>
## 4 <NA> <NA>
3. Retrieve the accountId, webPropertyId and viewId from our destination GA view
We again filter the results from ga_account_list(), to get account information for our destination GA property. In this example, we filter for "Your_Account" and the view called "Filtered".
The dest_account dataframe should contain the same columns and data types as your source_account dataframe.
# Account info for destination account
dest_account <- ga_account_list() %>%
filter(str_detect(accountName, "Your_Account"), str_detect(viewName, "Filtered"))
4. Get the configuration details of a single goal to be copied.
For this first example, we add a single goal (ID 1) to the destination view, passed as a list to the ga_goal_add() function.
We now use the ga_goal() function to retrieve the goal specifications as a list. We use ga_goal() because it provides the goal configuration data in the necessary format for our goal_add() request.
In the example below, we retrieve the details for the goal in slot 1.
# Get goal ID 1
goal_one <- ga_goal(source_account$accountId, source_account$webPropertyId, source_account$viewId,
goalId = 1) %>%
# Strip out instance-specific metadata (property, profile, view data and original creation dates)
list.remove( c("accountId", "webPropertyId", "selfLink", "internalWebPropertyId", "profileId", "parentLink", "created", "updated"))
## Error in list.remove(., c("accountId", "webPropertyId", "selfLink", "internalWebPropertyId", : could not find function "list.remove"
goal_one
## Error in eval(expr, envir, enclos): object 'goal_one' not found
5. Submit the goal details to our destination property.
We can then use the goal details contained in goal_one to submit a ga_goal_add() request to our destination property.
ga_goal_add(goal_one, dest_account$accountId, dest_account$webPropertyId, dest_account$viewId)
All being well, you should see console output containing the details of your newly submitted goal in the destination property.
You can repeat the above steps for any goal IDs you'd like to copy over, and to one or multiple destination views.
Copying multiple goals
Using the map() function from the tidyverse, we can quickly scale up this approach. We can copy all goals from a view at once, rather than one at a time.
First, we define a function to copy a single goal, given a goal ID, details of the source view ID and destination view IDs. In effect, we make a function to perform steps 1-5 from the "Copying a single goal" section in this page.
# Takes a goal ID, source account information & dest account information, copies goal over
copy_goal <- function(goal_id, source_df, dest_df) {
source_goal <- ga_goal(source_df$accountId, source_df$webPropertyId, source_df$viewId, goal_id) %>%
list.remove( c("accountId", "webPropertyId", "selfLink", "internalWebPropertyId", "profileId", "parentLink", "created", "updated"))
ga_goal_add(source_goal, dest_df$accountId, dest_df$webPropertyId, dest_df$viewId)
}
Having our goal-copying function defined, it is a simple step to apply it across all goal IDs from the source view.
The example below re-uses the source_goals, source_account and dest_account objects which were created earlier.
# For each goal ID in our source view, apply the copy_goal function,
# using source_account and dest_account account parameters as fixed 2nd / third arguments
map(source_goals$id, copy_goal, source_account, dest_account)
Write request limits
At the time of writing, there is a daily limit of 50 write requests per day, per Google Cloud project, which restricts the number of times this can be performed. However, it is possible to request that this limit be increased.
For more information, check the Google documentation at https://developers.google.com/analytics/devguides/config/mgmt/v3/limits-quotas#write_requests.
Remarketing segments
Remarketing segments lets you target users in Google Ads from Google Analytics segments.
ga_remarketing_estimate() - Estimate number of users added to the segment yesterdayga_remarketing_get() - Get a remarketing audiencega_remarketing_list() - List remarketing audiencesga_remarketing_build() - Create definitions to be used within ga_remarketing_create()ga_remarketing_create() - Create a remarketing audience
A demo of how you could use this based on your existing GA segments is:
adword_list <- ga_adwords_list(123456, "UA-123456-1")
adword_link <- ga_adword(adword_list$id[[1]])
segment_list <- ga_segment_list()$items$definition
my_remarketing1 <- ga_remarketing_build(segment_list[[1]],
state_duration = "TEMPORARY",
membershipDurationDays = 90,
daysToLookBack = 14)
my_remarketing2 <- ga_remarketing_build(segment_list[[2]],
state_duration = "PERMANENT",
membershipDurationDays = 7,
daysToLookBack = 31)
# state based only can include exclusions
ga_remarketing_create(adwords_link = adword_link,
include = my_remarketing1, exclude = my_remarketing2,
audienceType = "STATE_BASED", name = "my_remarketing_seg1")
Unsampled reports
Available only for GA360 accounts, you will need to authenticate with the Google drive scope to get download access. The download functions are contributed by @j450h1
ga_unsampled() - Get Unsampled Report Meta Dataga_unsampled_download() - Download Unsampled Report from Google Drivega_unsampled_list() - List Unsampled Reports
Users
ga_clientid_hash() - Creates the clientID hash that is used in BigQuery GA360 exportsga_clientid_deletion() - Delete a website visitor from Google Analytics
MarkEdmondson1234/googleAnalyticsR documentation built on Oct. 13, 2023, 4:40 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
The Management API v3 covers all API calls that are not data reporting related and are for getting meta information about your Google Analytics account or to change account settings.
Account structure
Shows the details for your account structure (Accounts > Web-properties > Views (profiles)) including creation time, user permissions etc.
The most day-to-day useful function is ga_account_list() which summarises all account web properties and views available to your user.
ga_account_list()- Get account summary including the ViewIdga_accounts()- Get account metadata for your userga_webproperty()- Get web propertyga_webproperty_list()- List web properties for a particular accountIdga_view()- Get single View (Profile)ga_view_list()- List Views (Profile) for a particular accountId/webPropertyId
You can then also get the meta-data for that particular entity via ga_webproperty() or ga_view()
# ga_account_list is most commonly used # (restricted to top 10 with the head() function) head(ga_account_list(), n = 10)
## # A tibble: 10 x 11 ## accountId accountName internalWebProp… level websiteUrl type starred ## <chr> <chr> <chr> <chr> <chr> <chr> <lgl> ## 1 79301104 cloudyr 118427305 STAN… http://cl… WEB NA ## 2 54516992 Demo Accou… 87479473 STAN… https://s… WEB NA ## 3 54516992 Demo Accou… 87479473 STAN… https://s… WEB NA ## 4 54516992 Demo Accou… 87479473 STAN… https://s… WEB NA ## 5 47480439 MarkEdmond… 78737968 STAN… http://ma… WEB NA ## 6 47480439 MarkEdmond… 78742336 STAN… http://ma… WEB NA ## 7 54019251 Sunholo We… 86845257 STAN… https://g… WEB NA ## 8 54019251 Sunholo We… 86845257 STAN… https://g… WEB NA ## 9 54019251 Sunholo We… 94325478 PREM… http://ma… WEB NA ## 10 54019251 Sunholo We… 94325478 PREM… http://ma… WEB TRUE ## # … with 4 more variables: webPropertyId <chr>, webPropertyName <chr>, viewId <chr>, ## # viewName <chr>
# this only lists account meta-data ga_accounts()
## id name created updated ## 1 47480439 MarkEdmondson 2014-01-25 12:17:05 2019-06-01 08:09:58 ## 2 54019251 Sunholo Websites 2014-08-20 19:17:05 2019-07-31 18:51:55 ## 3 54516992 Demo Account 2014-09-06 17:53:33 2019-06-10 17:56:03 ## 4 79301104 cloudyr 2016-06-14 14:29:50 2016-06-14 14:29:50 ## permissions.effective ## 1 COLLABORATE,EDIT,MANAGE_USERS,READ_AND_ANALYZE ## 2 COLLABORATE,EDIT,MANAGE_USERS,READ_AND_ANALYZE ## 3 READ_AND_ANALYZE ## 4
# this gives meta-data for all web-properties for this accountId ga_webproperty_list(47480439)
## id accountId internalWebPropertyId name ## 1 UA-47480439-1 47480439 78742336 markedmondson.me ## 2 UA-47480439-2 47480439 78737968 MarkEdmondson oldGA ## websiteUrl level profileCount industryVertical dataRetentionTtl ## 1 http://markedmondson.me STANDARD 1 HOBBIES_AND_LEISURE MONTHS_26 ## 2 http://markedmondson.me STANDARD 1 HOBBIES_AND_LEISURE INDEFINITE ## dataRetentionResetOnNewActivity created updated ## 1 TRUE 2014-01-25 12:17:05 2014-01-25 12:17:06 ## 2 TRUE 2014-01-25 12:19:20 2018-04-12 11:37:38 ## defaultProfileId permissions.effective ## 1 <NA> COLLABORATE,EDIT,MANAGE_USERS,READ_AND_ANALYZE ## 2 81416156 COLLABORATE,EDIT,MANAGE_USERS,READ_AND_ANALYZE
# this is meta-data for one particular web-property ga_webproperty(accountId = 47480439, webPropertyId = "UA-47480439-1")
## $id ## [1] "UA-47480439-1" ## ## $kind ## [1] "analytics#webproperty" ## ## $selfLink ## [1] "https://www.googleapis.com/analytics/v3/management/accounts/47480439/webproperties/UA-47480439-1" ## ## $accountId ## [1] "47480439" ## ## $internalWebPropertyId ## [1] "78742336" ## ## $name ## [1] "markedmondson.me" ## ## $websiteUrl ## [1] "http://markedmondson.me" ## ## $level ## [1] "STANDARD" ## ## $profileCount ## [1] 1 ## ## $industryVertical ## [1] "HOBBIES_AND_LEISURE" ## ## $dataRetentionTtl ## [1] "MONTHS_26" ## ## $dataRetentionResetOnNewActivity ## [1] TRUE ## ## $permissions ## $permissions$effective ## [1] "COLLABORATE" "EDIT" "MANAGE_USERS" "READ_AND_ANALYZE" ## ## ## $created ## [1] "2014-01-25T12:17:05.492Z" ## ## $updated ## [1] "2014-01-25T12:17:06.625Z" ## ## $parentLink ## $parentLink$type ## [1] "analytics#account" ## ## $parentLink$href ## [1] "https://www.googleapis.com/analytics/v3/management/accounts/47480439" ## ## ## $childLink ## $childLink$type ## [1] "analytics#profiles" ## ## $childLink$href ## [1] "https://www.googleapis.com/analytics/v3/management/accounts/47480439/webproperties/UA-47480439-1/profiles"
# this is meta-data for the views under this accountId/webPropertyId ga_view_list(accountId = 47480439, webPropertyId = "UA-47480439-1")
## id accountId webPropertyId internalWebPropertyId name currency ## 1 81416941 47480439 UA-47480439-1 78742336 All Web Site Data USD ## timezone websiteUrl type created ## 1 Europe/Copenhagen http://markedmondson.me WEB 2014-01-25 12:17:05 ## updated eCommerceTracking ## 1 2015-06-14 13:18:53 FALSE ## permissions.effective ## 1 COLLABORATE,EDIT,MANAGE_USERS,READ_AND_ANALYZE
# this is meta-data for this particular viewId (profileId) ga_view(accountId = 47480439, webPropertyId = "UA-47480439-1", profileId = 81416941)
## $id ## [1] "81416941" ## ## $kind ## [1] "analytics#profile" ## ## $selfLink ## [1] "https://www.googleapis.com/analytics/v3/management/accounts/47480439/webproperties/UA-47480439-1/profiles/81416941" ## ## $accountId ## [1] "47480439" ## ## $webPropertyId ## [1] "UA-47480439-1" ## ## $internalWebPropertyId ## [1] "78742336" ## ## $name ## [1] "All Web Site Data" ## ## $currency ## [1] "USD" ## ## $timezone ## [1] "Europe/Copenhagen" ## ## $websiteUrl ## [1] "http://markedmondson.me" ## ## $type ## [1] "WEB" ## ## $permissions ## $permissions$effective ## [1] "COLLABORATE" "EDIT" "MANAGE_USERS" "READ_AND_ANALYZE" ## ## ## $created ## [1] "2014-01-25T12:17:05.492Z" ## ## $updated ## [1] "2015-06-14T13:18:53.638Z" ## ## $eCommerceTracking ## [1] FALSE ## ## $parentLink ## $parentLink$type ## [1] "analytics#webproperty" ## ## $parentLink$href ## [1] "https://www.googleapis.com/analytics/v3/management/accounts/47480439/webproperties/UA-47480439-1" ## ## ## $childLink ## $childLink$type ## [1] "analytics#goals" ## ## $childLink$href ## [1] "https://www.googleapis.com/analytics/v3/management/accounts/47480439/webproperties/UA-47480439-1/profiles/81416941/goals"
Helper functions
These are helper utility functions to work with Google Analytics data.
ga_meta()- get a data.frame of available dimensions and metrics, or just usemetadirectly to get the version that comes with the library.
# you can just use `meta` as is to get the available metrics, # here we just return the first 5 columns and rows for brevity head(meta[,1:5])
## name type dataType group status ## 1 ga:userType DIMENSION STRING User PUBLIC ## 2 ga:visitorType DIMENSION STRING User DEPRECATED ## 3 ga:sessionCount DIMENSION STRING User PUBLIC ## 4 ga:visitCount DIMENSION STRING User DEPRECATED ## 5 ga:daysSinceLastSession DIMENSION STRING User PUBLIC ## 6 ga:userDefinedValue DIMENSION STRING User PUBLIC
# or ensure an up to date version by calling the metadata API. head(ga_meta())[,1:5]
## name type dataType group status ## 1 ga:userType DIMENSION STRING User PUBLIC ## 2 ga:visitorType DIMENSION STRING User DEPRECATED ## 3 ga:sessionCount DIMENSION STRING User PUBLIC ## 4 ga:visitCount DIMENSION STRING User DEPRECATED ## 5 ga:daysSinceLastSession DIMENSION STRING User PUBLIC ## 6 ga:userDefinedValue DIMENSION STRING User PUBLIC
ga_aggregate- aggregate data down to the dimensions you specify, treating metrics correctly regarding if they should be averaged or summed. This is used internally when using anti-sampling, and is also useful to respond to dimension selections in Shiny dashboards, where you don't want to perform a new API call each time you need aggregated data. Download all data first in a more unaggregated form, then use this function.
# use `aggregateGAData` so you can on the fly create summary data ga_data <- google_analytics(81416156, date_range = c("10daysAgo", "yesterday"), metrics = c("sessions","bounceRate"), dimensions = c("hour","date"))
## [36mℹ[39m 2020-05-26 11:40:05 > Downloaded [ 240 ] rows from a total of [ 240 ].
head(ga_data)
## hour date sessions bounceRate ## 1 00 2020-05-16 10 60.00000 ## 2 00 2020-05-17 8 37.50000 ## 3 00 2020-05-18 9 44.44444 ## 4 00 2020-05-19 8 50.00000 ## 5 00 2020-05-20 11 63.63636 ## 6 00 2020-05-21 8 50.00000
# if we want totals per hour over the dates: ga_aggregate(ga_data[,c("hour","sessions")], agg_names = "hour")
## # A tibble: 24 x 2 ## hour sessions ## <chr> <dbl> ## 1 00 92 ## 2 01 64 ## 3 02 58 ## 4 03 64 ## 5 04 70 ## 6 05 74 ## 7 06 59 ## 8 07 66 ## 9 08 82 ## 10 09 89 ## # … with 14 more rows
# it knows not to sum metrics that are rates: ga_aggregate(ga_data[,c("hour","bounceRate")], agg_names = "hour")
## # A tibble: 24 x 2 ## hour bounceRate ## <chr> <dbl> ## 1 00 53.4 ## 2 01 65.1 ## 3 02 69.3 ## 4 03 60.5 ## 5 04 65.4 ## 6 05 79.9 ## 7 06 78.8 ## 8 07 66.1 ## 9 08 75.4 ## 10 09 66.3 ## # … with 14 more rows
ga_allowed_metric_dim()- Create named list of allowed GA metrics/dimensions. This is useful to have nice looking labels for dimension and metric selection pickers, or to create lookup tables.
amd <- ga_allowed_metric_dim() head(amd)
## Users New Users % New Sessions ## "ga:users" "ga:newUsers" "ga:percentNewSessions" ## 1 Day Active Users 7 Day Active Users 14 Day Active Users ## "ga:1dayUsers" "ga:7dayUsers" "ga:14dayUsers"
User management
ga_users_list()- list user access to your Google Analytics accounts, web properties or viewsga_users_delete()- delete user access via emailga_users_delete_linkid()- delete user access via the linkIdga_users_add()- add users to accountsga_users_update()- update a user
User management follows the recommended workflows outlined in this Google reference article.
The ga_users_* functions use the management APIs batching endpoint, to take advantage of the special increased quota limits when dealing with User management that API offers. For example, every 30 API calls that are batched that deal with user management only increments one API call against your management API quota. You can have 300 API calls in one batch (e.g. that costs 10 API calls against the quota), and googleAnalyticsR will split any list of emails into 300 emails per batch for you, so you can send in all emails you have.
By default, each Google Cloud Project gets 50 write requests per project per day, which if using batching means 1500 write/delete/update user operations per day.
You will need to use your own Google Cloud Platform project if using any write API operations seriously, otherwise you will be at the mercy of the shared googleAnalyticsR's quota limits. See the section in the Setup page on how to setup using your own Google Project
To see which users are attached, use ga_users_list(). Each user email is referenced under the userRef.email column, whereas the unique IDs used to see which user is linked to where is the linkId which is unique to each user and link to your account, web property or view/profile.
Users can be attached at various levels in the hierarchy so you can call the function at various viewing angles.
# default will list all users that match the id you supply ga_users_list(47480439) ga_users_list(47480439, webPropertyId = "UA-47480439-2") ga_users_list(47480439, webPropertyId = "UA-47480439-2", viewId = 81416156) # only list users who have account level access ga_users_list(47480439, webPropertyId = NULL, viewId = NULL) # only list users who have webProperty and above access ga_users_list(47480439, webPropertyId = "UA-47480439-2", viewId = NULL)
Users can be attached at account, web-property and view level.
Batching is incorporated for adding users, so you can pass in a vector of emails to add.
ga_users_add(c("the_email@company.com", "another_email@company.com"), permissions = "EDIT", accountId = 47480439)
To delete a user from all accounts, web-properties and views takes a few API calls to ga_users_list() to find all the possible links, which is done behind the scenes of ga_users_delete(). You can pass the function multiple emails to delete many at once, which is done via batching:
ga_users_list(47480439) ga_users_delete("the_email@company.com", 47480439) # delete many emails at once ga_users_delete(c("the_email@company.com", "another_email@company.com"), accountId = 47480439)
For more fine grained control, the below examples shows a workflow to list and delete user access for one user at View level using the linkId.
# get the linkId for the user you want to delete ga_users_list(47480439, webPropertyId = "UA-47480439-2", viewId = 81416156) ga_users_delete_linkid("81416156:114834495587136933146", 47480439, webPropertyId = "UA-47480439-2", viewId = 81416156) # check its gone ga_users_list(47480439, webPropertyId = "UA-47480439-2", viewId = 81416156) # can only delete at level user has access, the above deletion woud have failed if via: ga_users_delete_linkid("47480439:114834495587136933146", 47480439)
You can also modify existing users via ga_users_update() - the below modifies on the web property level:
# the update to perform o <- list(permissions = list(local = list("EDIT"))) ga_users_update("UA-123456-1:1111222233334444", update_object = o, accountId = 123456, webPropertyId = "UA-123456-1")
When using linkIds, make sure to use the correct linkId for the level of account, web-property or view you are operating on - the linkId starts with the accountId, UA code of web property, or ViewId number to distinguish between the various types.
Custom variables
Custom variable management for a Google Analytics property.
ga_custom_vars()- get meta data for a specific custom variablega_custom_vars_list()- list all custom dimensions or metricsga_custom_vars_create()- create a new custom variablega_custom_vars_patch()- update an existing custom variable
Using these you can scale setting up custom dimensions across your Google Analytics web properties. Custom dimension updates are governed by the same rules as when you create them in the web interface, as this Google article on custom dimensions and metrics explains.
You can't delete custom dimensions, you can only de-activate them for reuse later on.
A workflow is shown below that creates, then updates a custom variable to a new name.
# create custom var ga_custom_vars_create("my_custom_dim", index = 15, accountId = 54019251, webPropertyId = "UA-54019251-4", scope = "HIT", active = FALSE) # view custom dimension in list ga_custom_vars_list(54019251, webPropertyId = "UA-54019251-4", type = "customDimensions") # change a custom dimension ga_custom_vars_patch("ga:dimension7", accountId = 54019251, webPropertyId = "UA-54019251-4", name = "my_custom_dim2", scope = "SESSION", active = TRUE) # view custom dimensions again to see change ga_custom_vars_list(54019251, webPropertyId = "UA-54019251-4", type = "customDimensions")
AdWords
ga_adwords()Get Google Analytics - AdWords Link meta dataga_adwords_list()List AdWordsga_adwords_add_linkid()Create a link between and Adwords (Google ads) account and a Google Analytics propertyga_adwords_delete_linkid()Delete a Google Analytics webProperty-Google Ads link
For many websites Google search advertising, aka Adwords, is a key traffic channel which comes with its own reporting system.
Linking Google Analytics with Adwords makes sense as it allows Google ads data to be included in Analytics reports and vice versa.
For instance linking the two together can add to the Analytics reports more than 30 metrics and dimensions related to ads (such as the keywords that triggered an ad or the ad ROI)
You can use the function ga_adwords_list() to request a list of existing links between Google Analytics and Adwords associated with your user.
The ga_adwords() function provides meta data about a specific Google Analytics - Adwords link.
If a link between Analytics and Google ads has not been established, this can be done easily via the Management API using the ga_adwords_add_linkid() function.
A requirement is that your email has access rights to both the Analytics and Adwords account.
You will also need the Adwords customer ID which can be obtained via the Adwords API or UI.
Also required is the id of the Google Analytics account and associated GA property to which the Adwords account will be linked.
This operation will create a new entityAdWordsLinks resource.
To remove this entityAdWordsLinks later if needed the ga_adwords_delete_linkid() function can be used.
# Lists webProperty-Google Ads links ga_adwords_list(accountId = 65973592, webPropertyId = "UA-65973592-1") # Get information about a web property-Google Ads link ga_adwords(accountId = 65973592, webPropertyId = "UA-65973592-1", webPropertyAdWordsLinkId = "QrcfI2DTSMayqbrLiHYUqw") # establish a new link between GA and Adwords ga_adwords_add_linkid(adwordsAccountId = "280-234-7592", linkName = "Google Ads Link", accountId = "65973592", webPropertyId = "UA-65973592-1") #check that it has been added ga_adwords_list(accountId = 65973592, webPropertyId = "UA-65973592-1") # delete the link ga_adwords_delete_linkid(accountId = 65973592, webPropertyId ="UA-65973592-1", webPropertyAdWordsLinkId = "ezW2dyaiQcGheWRAo69nCw") #check that the link has been removed ga_adwords_list(accountId = 65973592, webPropertyId = "UA-65973592-1")
Custom Data Sources
See and upload custom data sources to Google Analytics
ga_custom_datasource()- List Custom Data Sourcesga_custom_upload()- Custom Data Source Upload Statusga_custom_upload_file()- Upload a file to GA custom uploadsga_custom_upload_list()- List the files in a GA custom upload
Experiments
ga_experiment()- Experiments Meta dataga_experiment_list()- List Experiments
View Filters
The filter edit functions are contributed by @zselinger which allow you to update filters for your Google Analytics views at scale.
ga_filter()- Get specific filter for accountga_filter_add()- Create a new filter and add it to the view (optional).ga_filter_apply_to_view()- Apply an existing filter to view.ga_filter_delete()- Delete a filter from account or remove from view.ga_filter_list()- List filters for accountga_filter_update()- Updates an existing filter.ga_filter_update_filter_link()- Update an existing profile filter link. Patch semantics supportedga_filter_view()- Get specific filter for view (profile)ga_filter_view_list()- List filters for view (profile)
Goals
ga_goal()- Get goalga_goal_list()- List goalsga_goal_add()- Create and add Goals to a web propertyga_goal_update()- Modify an existing goal
Example: Copying GA goals
We can programatically copy goal configurations from any GA view (the source) to another GA view (the destination) within an entirely separate property. This can be helpful to maintain standardised goal configurations for multiple properties, and also for GA migrations.
This example will first copy a single goal from one property to another, then use the map() function from purrr to copy multiple goals at once.
We use the Google Analytics demo account as the source view for this example - you can read more about the demo account here.
Auth and setup
We authenticate and load the necessary packages, following the instructions from the documentation.
library(tidyverse) library(stringr) library(rlist)
## Error in library(rlist): there is no package called 'rlist'
Copying a single goal
For this first section, we copy a single Goal from one view to another. To achieve this, we will:
- Retrieve the accountId, webPropertyId and viewId from our source GA view (the one where our goal to be copied over currently exists).
- Get a list of the goals currently configured in the source GA view.
- Retrieve the accountId, webPropertyId and viewId from our destination GA view (the one where our goal will be copied to).
- Get the configuration details of a single goal to be copied.
- Submit the goal details to our destination property.
1. Retrieve the accountId, webPropertyId and viewId from our source GA view
We use the ga_account_list() function, which returns an account summary for all available accounts as a dataframe.
Then, we filter the dataframe down to a specific account and view name using the filter() and str_detect() functions from the tidyverse.
You can supply your own filter conditions as necessary, to locate your desired view information.
# Account info and list of goals for source, to be copied source_account <- ga_account_list() %>% filter(str_detect(accountName, "Demo Account"), str_detect(viewName, "Master")) source_account
## # A tibble: 1 x 11 ## accountId accountName internalWebProp… level websiteUrl type starred webPropertyId ## <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> ## 1 54516992 Demo Accou… 87479473 STAN… https://s… WEB NA UA-54516992-1 ## # … with 3 more variables: webPropertyName <chr>, viewId <chr>, viewName <chr>
2. Get a list of the goals currently configured from our source GA view
Now that we have the account summary, we can use the view details to get a list of goal data, with each item in the list showing goal details.
source_goals <- ga_goal_list(source_account$accountId, source_account$webPropertyId, source_account$viewId) source_goals
## id accountId webPropertyId internalWebPropertyId profileId name
## 1 1 54516992 UA-54516992-1 87479473 92320289 Purchase Completed
## 2 2 54516992 UA-54516992-1 87479473 92320289 Engaged Users
## 3 3 54516992 UA-54516992-1 87479473 92320289 Registrations
## 4 4 54516992 UA-54516992-1 87479473 92320289 Entered Checkout
## value active type created updated
## 1 0 TRUE URL_DESTINATION 2014-10-10 20:13:47 2016-11-10 23:07:35
## 2 0 TRUE VISIT_NUM_PAGES 2015-02-23 16:18:06 2015-04-14 12:02:21
## 3 0 TRUE URL_DESTINATION 2015-02-27 12:15:01 2016-07-26 16:14:09
## 4 0 TRUE URL_DESTINATION 2015-07-20 23:57:22 2016-07-26 16:19:40
## urlDestinationDetails.url
## 1 /ordercompleted\\.html
## 2 <NA>
## 3 /registersuccess\\.html
## 4 /yourinfo\\.html|/guestregister\\.html|/shipping\\.html
## urlDestinationDetails.caseSensitive urlDestinationDetails.matchType
## 1 FALSE REGEX
## 2 NA <NA>
## 3 FALSE REGEX
## 4 FALSE REGEX
## urlDestinationDetails.firstStepRequired
## 1 TRUE
## 2 NA
## 3 FALSE
## 4 FALSE
## urlDestinationDetails.steps
## 1 1:4,c("Cart", "Billing and Shipping", "Payment", "Review"),c("/basket\\\\.html", "/yourinfo\\\\.html", "/payment\\\\.html", "/revieworder\\\\.html")
## 2
## 3
## 4
## visitNumPagesDetails.comparisonType visitNumPagesDetails.comparisonValue
## 1 <NA> <NA>
## 2 GREATER_THAN 10
## 3 <NA> <NA>
## 4 <NA> <NA>
3. Retrieve the accountId, webPropertyId and viewId from our destination GA view
We again filter the results from ga_account_list(), to get account information for our destination GA property. In this example, we filter for "Your_Account" and the view called "Filtered".
The dest_account dataframe should contain the same columns and data types as your source_account dataframe.
# Account info for destination account dest_account <- ga_account_list() %>% filter(str_detect(accountName, "Your_Account"), str_detect(viewName, "Filtered"))
4. Get the configuration details of a single goal to be copied.
For this first example, we add a single goal (ID 1) to the destination view, passed as a list to the ga_goal_add() function.
We now use the ga_goal() function to retrieve the goal specifications as a list. We use ga_goal() because it provides the goal configuration data in the necessary format for our goal_add() request.
In the example below, we retrieve the details for the goal in slot 1.
# Get goal ID 1 goal_one <- ga_goal(source_account$accountId, source_account$webPropertyId, source_account$viewId, goalId = 1) %>% # Strip out instance-specific metadata (property, profile, view data and original creation dates) list.remove( c("accountId", "webPropertyId", "selfLink", "internalWebPropertyId", "profileId", "parentLink", "created", "updated"))
## Error in list.remove(., c("accountId", "webPropertyId", "selfLink", "internalWebPropertyId", : could not find function "list.remove"
goal_one
## Error in eval(expr, envir, enclos): object 'goal_one' not found
5. Submit the goal details to our destination property.
We can then use the goal details contained in goal_one to submit a ga_goal_add() request to our destination property.
ga_goal_add(goal_one, dest_account$accountId, dest_account$webPropertyId, dest_account$viewId)
All being well, you should see console output containing the details of your newly submitted goal in the destination property.
You can repeat the above steps for any goal IDs you'd like to copy over, and to one or multiple destination views.
Copying multiple goals
Using the map() function from the tidyverse, we can quickly scale up this approach. We can copy all goals from a view at once, rather than one at a time.
First, we define a function to copy a single goal, given a goal ID, details of the source view ID and destination view IDs. In effect, we make a function to perform steps 1-5 from the "Copying a single goal" section in this page.
# Takes a goal ID, source account information & dest account information, copies goal over copy_goal <- function(goal_id, source_df, dest_df) { source_goal <- ga_goal(source_df$accountId, source_df$webPropertyId, source_df$viewId, goal_id) %>% list.remove( c("accountId", "webPropertyId", "selfLink", "internalWebPropertyId", "profileId", "parentLink", "created", "updated")) ga_goal_add(source_goal, dest_df$accountId, dest_df$webPropertyId, dest_df$viewId) }
Having our goal-copying function defined, it is a simple step to apply it across all goal IDs from the source view.
The example below re-uses the source_goals, source_account and dest_account objects which were created earlier.
# For each goal ID in our source view, apply the copy_goal function, # using source_account and dest_account account parameters as fixed 2nd / third arguments map(source_goals$id, copy_goal, source_account, dest_account)
Write request limits
At the time of writing, there is a daily limit of 50 write requests per day, per Google Cloud project, which restricts the number of times this can be performed. However, it is possible to request that this limit be increased.
For more information, check the Google documentation at https://developers.google.com/analytics/devguides/config/mgmt/v3/limits-quotas#write_requests.
Remarketing segments
Remarketing segments lets you target users in Google Ads from Google Analytics segments.
ga_remarketing_estimate()- Estimate number of users added to the segment yesterdayga_remarketing_get()- Get a remarketing audiencega_remarketing_list()- List remarketing audiencesga_remarketing_build()- Create definitions to be used withinga_remarketing_create()ga_remarketing_create()- Create a remarketing audience
A demo of how you could use this based on your existing GA segments is:
adword_list <- ga_adwords_list(123456, "UA-123456-1") adword_link <- ga_adword(adword_list$id[[1]]) segment_list <- ga_segment_list()$items$definition my_remarketing1 <- ga_remarketing_build(segment_list[[1]], state_duration = "TEMPORARY", membershipDurationDays = 90, daysToLookBack = 14) my_remarketing2 <- ga_remarketing_build(segment_list[[2]], state_duration = "PERMANENT", membershipDurationDays = 7, daysToLookBack = 31) # state based only can include exclusions ga_remarketing_create(adwords_link = adword_link, include = my_remarketing1, exclude = my_remarketing2, audienceType = "STATE_BASED", name = "my_remarketing_seg1")
Unsampled reports
Available only for GA360 accounts, you will need to authenticate with the Google drive scope to get download access. The download functions are contributed by @j450h1
ga_unsampled()- Get Unsampled Report Meta Dataga_unsampled_download()- Download Unsampled Report from Google Drivega_unsampled_list()- List Unsampled Reports
Users
ga_clientid_hash()- Creates the clientID hash that is used in BigQuery GA360 exportsga_clientid_deletion()- Delete a website visitor from Google Analytics
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