The segment filters can be of a segment_vector_simple type (where order doesn’t matter) or a segment_vector_sequence type (where order does matter.) You can also pass in a not_vector of the same length as the list of segment filters, which dictates if the segments you pass in are included (the default) or excluded.
segment_define() - this is where you define the types of segment filters that you are passing in - they are combined in a logical AND. The user and session scopes can have one or a list of segment_define() functions. Here you name the segment as it will appear in the segment dimension, and pass in segment definitions either via an existing segmentID or v3 definition via a user scoped level or via a session scoped level. You can pass one or a list of these into a google_analytics() segment argument. segment_ga4() - this is the top of the segment tree. The hierarachy of the segment elements you will need are: Its recommended you embrace the new v4 syntax, as its more flexible and powerful in the long run. Metrics = c ( 'visitors', 'bounces' ) ) # Request the two calls by wrapping them in a list() and passing to fetch_google_analytics() ga_data3 <- fetch_google_analytics ( list ( multidate_test, multi_test2 ) ) ga_data3 # ] # source medium sessions.d1 bounces.d1 sessions.d2 bounces.d2 # 1 baby.dk referral 3 2 6 3 # 2 bing organic 71 42 217 126 # 3 referral 7 7 0 0 # 4 referral 5 3 0 0 # 5 google organic 6 920 # 6 referral 3 2 12 9 # 7 referral 3 1 0 0 # 8 referral 35 35 0 0 # 9 referral 11 11 0 0 # 10 yahoo organic 66 43 236 178 # 11 referral 6 4 9 4 # ] # hour medium visitors.d1 bounces.d1 visitors.d2 bounces.d2 # 1 00 organic 28 16 85 59 # 2 00 referral 3 2 1 1 # 3 01 organic 43 28 93 66 Order = order_type ( "sessions", "DESCENDING", "DELTA" ) ) # Second request - same date ranges and ID required, but different dimensions/metrics/order. # First request we make via make_ga_4_req() multidate_test <- make_ga_4_req ( ga_id, # create filters on metrics mf <- met_filter ( "bounces", "GREATER_THAN", 0 ) mf2 <- met_filter ( "sessions", "GREATER", 2 ) # create filters on dimensions df <- dim_filter ( "source", "BEGINS_WITH", "1",not = TRUE ) df2 <- dim_filter ( "source", "BEGINS_WITH", "a",not = TRUE ) # construct filter objects fc2 <- filter_clause_ga4 ( list ( df, df2 ), operator = "AND" ) fc <- filter_clause_ga4 ( list ( mf, mf2 ), operator = "AND" ) # make v4 request ga_data1 <- google_analytics ( ga_id,įiltersExpression = "ga:source!=(direct)" ) ga_data1 # source medium sessions bounces # 1 baby.dk referral 3 2 # 2 bing organic 71 42 # 3 referral 7 7 # 4 referral 5 3 # 5 google organic 642 520 # 6 referral 3 2 # 7 referral 3 1 # 8 referral 35 35 # 9 referral 11 11 # 10 yahoo organic 66 43 # 11 referral 6 4 This method will adjust the time periods to have the batch sizes large enough to not take too long, but small enough to have unsampled data. The sequence involves a couple of exploratory API calls to determine the best split. #Finished unsampled data request, total rows #Successfully avoided sampling #Anti-sample call covering 21 days:, #Downloaded rows from a total of. #Anti-sample call covering 23 days:, #Downloaded rows from a total of. #Anti-sample call covering 128 days:, #Downloaded rows from a total of. R GA$GETDATA DIMENSIONS DOWNLOAD
#Calculated batches are needed to download rows unsampled. #Finding number of sessions for anti-sample calculations. #Data is sampled, based on 39.75% of visits. #Finding how much sampling in data request. Mitigating sampling via multiple API calls. Metrics = c ( "users", "sessions", "bounceRate" ),ĭimensions = c ( "date", "landingPagePath", "source" ),Īnti_sample = TRUE ) #anti_sample set to TRUE. Library ( googleAnalyticsR ) ga_auth ( ) unsampled_data_fetch <- google_analytics ( id,
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