google_analytics_3 | R Documentation |
Legacy v3 API, for more modern API use google_analytics.
google_analytics_3(
id,
start,
end,
metrics = c("sessions", "bounceRate"),
dimensions = NULL,
sort = NULL,
filters = NULL,
segment = NULL,
samplingLevel = c("DEFAULT", "FASTER", "HIGHER_PRECISION"),
max_results = 100,
type = c("ga", "mcf")
)
id |
A character vector of View Ids to fetch from. |
start |
Start date in YYY-MM-DD format. |
end |
End date in YYY-MM-DD format. |
metrics |
A character vector of metrics. With or without ga: prefix. |
dimensions |
A character vector of dimensions. With or without ga: prefix. |
sort |
How to sort the results, in form 'ga:sessions,-ga:bounceRate' |
filters |
Filters for the result, in form 'ga:sessions>0;ga:pagePath=~blah' |
segment |
How to segment. |
samplingLevel |
Level of precision of the API requests |
max_results |
Default 100. If greater than 10,000 then will batch GA calls. |
type |
ga = Google Analytics v3; mcf = Multi-Channel Funels. |
For one id a data.frame of data, with meta-data in attributes.
https://developers.google.com/analytics/devguides/reporting/core/v3/
## Not run:
library(googleAnalyticsR)
## Authenticate in Google OAuth2
## this also sets options
ga_auth()
## if you need to re-authenticate use ga_auth(new_user=TRUE)
## if you have your own Google Dev console project keys,
## then don't run ga_auth() as that will set to the defaults.
## instead put your options here, and run googleAuthR::gar_auth()
## get account info, including View Ids
account_list <- ga_account_list()
ga_id <- account_list$viewId[1]
## get a list of what metrics and dimensions you can use
meta <- ga_meta()
head(meta)
## pick the account_list$viewId you want to see data for.
## metrics and dimensions can have or have not "ga:" prefix
gadata <- google_analytics_3(id = ga_id,
start="2015-08-01", end="2015-08-02",
metrics = c("sessions", "bounceRate"),
dimensions = c("source", "medium"))
## if more than 10000 rows in results, auto batching
## example is setting lots of dimensions to try and create big sampled data
batch_gadata <- google_analytics_3(id = ga_id,
start="2014-08-01", end="2015-08-02",
metrics = c("sessions", "bounceRate"),
dimensions = c("source", "medium",
"landingPagePath",
"hour","minute"),
max=99999999)
## mitigate sampling by setting samplingLevel="WALK"
## this will send lots and lots of calls to the Google API limits, beware
walk_gadata <- google_analytics_3(id = ga_id,
start="2014-08-01", end="2015-08-02",
metrics = c("sessions", "bounceRate"),
dimensions = c("source", "medium", "landingPagePath"),
max=99999999, samplingLevel="WALK")
## multi-channel funnels set type="mcf"
mcf_gadata <- google_analytics_3(id = ga_id,
start="2015-08-01", end="2015-08-02",
metrics = c("totalConversions"),
dimensions = c("sourcePath"),
type="mcf")
## reach meta-data via attr()
attr(gadata, "profileInfo")
attr(gadata, "dateRange")
## End(Not run)
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