With several hundreds of metrics and dimensions available in GA it can be handy to have a reference of what’s what within easy reach. The
ga_meta() function returns a table containing a list of all GA metrics and dimensions along with their definitions and other useful meta data.
If offline, this data is also available by typing
meta, although it won’t be quite as up to date.
(The meta data does not include multi-channel as they are not available, for those see the online version. )
In a similar fashion, using the function
ga_account_list() summarises all account web properties and views available to your user for a handy reference.
There is detailed information about authentication options in the setup section, but as a rule of thumb:
if you are experimenting or just making casual use of the library, then use the shared project option, which essentially requires no special setup.
if you use the API consistently and your work depends on it, then its preferable to set up your own Google Cloud Platform project.
If you are a developer working with machine to machine applications, then the service account is probably the most suitable option for you.
To make the process of working with segments easier,
googleAnalyticsR provides several options. These are examined in more detail in the API v4 section Sometimes it is also helpful to create a segment within the UI first and then refer to it by ID when working with the
googleAnalyticsR library -especially when some quick experimentation is needed before deciding on the exact segment definition.
Several types of statistical analysis require access to more fine grained data than what’s typically available via the Google Analytics standard version. The release of the user API has removed this constraint, so if you wish to explore a broader range of analysis methods you can now access the Google Analytics equivalent of a clickstream.