| ga_data | R Documentation | 
Fetches Google Analytics from the Data API for Google Analytics 4 (Previously App+Web)
ga_data(
  propertyId,
  metrics,
  date_range = NULL,
  dimensions = NULL,
  dim_filters = NULL,
  dimensionDelimiter = "/",
  met_filters = NULL,
  orderBys = NULL,
  limit = 100,
  page_size = 100000L,
  realtime = FALSE,
  metricAggregations = NULL,
  raw_json = NULL
)
| propertyId | A GA4 property Id | 
| metrics | The metrics to request - see ga_meta - set to NULL to only see dimensions | 
| date_range | A vector with start and end dates in YYYY-MM-DD format - can send in up to four date ranges at once | 
| dimensions | The dimensions to request - see ga_meta | 
| dim_filters | Filter on the dimensions of the request - a filter object created by ga_data_filter | 
| dimensionDelimiter | If combining dimensions in one column, the delimiter for the value field | 
| met_filters | Filter on the metrics of the request - a filter object created by ga_data_filter | 
| orderBys | How to order the response - an order object created by ga_data_order | 
| limit | The number of rows to return - use -1 to return all rows | 
| page_size | The size of API pages - default is 100000L rows | 
| realtime | If TRUE then will call the real-time reports, that have a more limited set of dimensions/metrics - see valid real-time dimensions | 
| metricAggregations | Default NULL, pass in character vector of one or multiple of  | 
| raw_json | You can send in the raw JSON string for a Data API request which will skip all checks | 
This is the main function to call the Google Analytics 4 Data API.
A data.frame tibble, including attributes metadata, metricAggregations and rowCount. Use ga_data_aggregations to extract the data.frames of metricAggregations
Other GA4 functions: 
ga_data_filter(),
ga_data_order()
## Not run: 
# send up to 4 date ranges
multi_date <- ga_data(
  206670707,
  metrics = c("activeUsers","sessions"),
  dimensions = c("date","city","dayOfWeek"),
  date_range = c("2020-03-31", "2020-04-27", "2020-04-30", "2020-05-27"),
  dim_filters = ga_data_filter("city"=="Copenhagen"),
  limit = 100
  )
# metric and dimension expressions
# create your own named metrics
met_expression <- ga_data(
  206670707,
  metrics = c("activeUsers","sessions",sessionsPerUser = "sessions/activeUsers"),
  dimensions = c("date","city","dayOfWeek"),
  date_range = c("2020-03-31", "2020-04-27"),
  limit = 100
  )
# create your own aggregation dimensions
dim_expression <- ga_data(
  206670707,
  metrics = c("activeUsers","sessions"),
  dimensions = c("date","city","dayOfWeek", cdow = "city/dayOfWeek"),
  date_range = c("2020-03-31", "2020-04-27"),
  limit = 100
  )
  
# run a real-time report (no date dimension allowed) 
# includes metricAggregation metadata
realtime <- ga_data(
  206670707,
  metrics = "activeUsers",
  dimensions = c("city","unifiedScreenName"),
  limit = 100,
  realtime = TRUE,
  metricAggregations = c("TOTAL","MAXIMUM","MINIMUM"))
# extract meta data from the table
ga_data_aggregations(realtime)
# add ordering
a <- ga_data_order(-sessions)
b <- ga_data_order(-dayOfWeek, type = "NUMERIC")
ga_data(
  206670707,
  metrics = c("activeUsers","sessions"),
  dimensions = c("date","city","dayOfWeek"),
  date_range = c("2020-03-31", "2020-04-27"),
  orderBys = c(a, b)
  )
## End(Not run)
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