Downloading Google Analytics data is all well and good, but the reason most users want to use the API is to then operate and act upon that data.

There are several tutorials linked from the homepage that demonstrate various applications of using googleAnalyticsR to analyse data, but they can still be intimidating for new users of R.

To make it easier to distribute these analysis, googleAnalyticsR now includes the ga_model_* functions to help end users get to insights more quickly.

ga_model objects can be saved to the new .gamr format. Loading these files using ga_model() and specifying your own Google Analytics viewId, all the data processing, modelling and visualisation steps can be encapsulated to give you the output.

.gamr files also includes the ability to create Shiny modules which you can use to quickly create online Shiny dashboards displaying the model results, and which can switch to users own Google Analytics data using the multi-user login capabilities.

The .gamr file format can be shared with others online or within your organisation. A new R package that includes some more advanced examples is available from IronistM/googleAnalyticsModelR/

Pre-packaged examples

A ready-made model is available in the googleAnalyticsModelR package. This function loads the pre-made model object created by the ga_model_* functions in googleAnalyticsR.

Some examples below (the graphs are interactive when looking on a webpage):


ga_auth(email = Sys.getenv("GARGLE_EMAIL"))
my_view_id <- 81416156

forecast_data <- ga_model_prophet(my_view_id, interactive_plot = TRUE)

Time-normalised visits to your website:

output <- ga_time_normalised(my_view_id, interactive_plot = TRUE)

As we have a standardised model structure, it makes it easier to use the output in other functions. For example, you can make a webpage suitable for embedding in Twitter tweets and upload via googleCloudStorageR using ga_model_tweet():

Which embeds in a tweet (you have to click the play icon to activate the visualisation):

A simple example loading a model directly

The above loads models within a wrapper function, but you can also load model objects yourself via ga_model_load(). Included within the package are some simple models to demonstrate its use which use ga_model_load() to load from the package examples, via ga_model_example()

The example below performs decomposition on the sessions to your website. Behind the scenes the model downloads your data with the right columns, applies the decomposition model then plots it, returning the data plotted for you to work with later:

This model allows you to alter the date range of the data fetched:

# change default date range to 20 days ago to yesterday
d2 <- ga_model(my_viewid, model = decomp_ga, date_range = c("20daysAgo","yesterday"))

You can examine the properties of the model and the arguments it was sent via its print method:

## ==ga_model object==
## Description:  Performs decomposition and creates a plot 
## Data args:    viewId date_range 
## Input data:   date sessions 
## Model args:   df 
## Output args:  x y 
## Packages:

You can also see an overview on how a particular call to a model was created by printing out the model’s result directly to console:

## ==ga_model_result object==
## Input names:        date sessions 
## Input dimensions:   20 2 
## Output names:       x seasonal trend random figure type 
## Plot class:         NULL 
## Model args passed:  date_range = c("20daysAgo", "yesterday") 
## ==ga_model object==
## Description:  Performs decomposition and creates a plot 
## Data args:    viewId date_range 
## Input data:   date sessions 
## Model args:   df 
## Output args:  x y 
## Packages:

And if you want to review the code of the model, use ga_model_write() to write the functions out to a file.

ga_model_write(decomp_ga, "my_model.R")

Shiny modules

The model can also be used to create a Shiny app, as it creates a Shiny module of the model. It is suggested this is the easiest route to turn your local GA analysis into Shiny apps.

An example is shown below, which allows whoever logs in to apply the model to their own Google Analytics data. You can run the app yourself via:

shiny::runApp(system.file("shiny/hello-world-models", package="googleAnalyticsR"))

The model code is encapsulated in the Shiny module so the actual Shiny app is relatively simple:


gar_set_client(web_json = "ga-web-client.json",
               scopes = "")

# loads a pre-existing model
model <- ga_model_example("decomp_ga.gamr", location = "googleAnalyticsR")

## ui.R
ui <- fluidPage(title = "googleAnalyticsR Test Deployment",
                h2("Model Description"),
                h2("Model Output"),

## server.R
server <- function(input, output, session){
  al <- reactive(ga_account_list())
  # module for authentication
  view_id <- callModule(authDropdown, "auth_menu", ga.table = al)
  output$model_description <- renderText(model$description)
  # module to display model results
  callModule(model$shiny_module$server, "model1", view_id = view_id)

shinyApp(gar_shiny_ui(ui, login_ui = silent_auth), server)

If you want to pass reactive input objects to your model’s module, do so by wrapping them in shiny::reactive() e.g.

             view_id = view_id,
             reactive_var = reactive(input$my_option))

Creating model .gamr objects

To create your own models, you need to predefine all the functions to look after the fetching, modelling and viewing of the data. You then pass those functions to the ga_model_make() function.

The functions need to follow these specifications:

  • data_f - A function to collect the data you will need. The first argument should be the view_id which will be pass the viewId of Google Analytics property to fetch data from.
  • model_f - A function to work with the data you have fetched. The first argument should be the data.frame that is produced by the data fetching function, data_f().
  • output_f - A function to plot the data. The first argument should be the data.frame that is produced by the model function, model_f().
  • All functions you create must include ... as an argument.
  • Take care if you use the same argument name that it is consistent with all functions as it will be passed to all of them.

If you want to also create the Shiny modules, then you also need to specify:

  • outputShiny - the output function for the UI, such as plotOutput
  • renderShiny - the render function for the server, such as renderPlot

You then supply supporting information to make sure the user can run the model:

  • required_columns - Specification of which columns the data will fetch. It will fail if they are not present.
  • required_packages - The packages the end user needs to have installed to run your functions.
  • description - A sentence on what the model is so they can be distinguished.

To create the decomposition example model, this was applied as shown below:

Advanced use

The more arguments you provide to the model creation functions, the more complicated it is for the end user, but the more flexible the model. It is suggested making several narrow usage models is better than one complicated one.

For instance, you could modify the above model to allow the end user to specify the metric, timespan and seasonality of the decomposition:

It would then be used via:

result <- ga_model(81416156, decomp_ga_advanced, metric="users", frequency = 30)

Working with the model object

The model objects prints to console in a friendly manner:

## ==ga_model object==
## Description:  Performs decomposition and creates a plot 
## Data args:    viewId date_range metric 
## Input data:   date 
## Model args:   df frequency 
## Output args:  x y 
## Packages:

You can save and load model objects from a file. It is suggested to save them with the .gamr suffix.

# save model to a file
ga_model_save(decomp_ga_advanced, filename = "my_model.gamr")

# load model again

You can use models directly from the file:

ga_model(81416156, "my_model.gamr")

If you need to change parts of a model, ga_model_edit() lets you change individual aspects:

ga_model_edit(decomp_ga_advanced, description = "New description")
## ==ga_model object==
## Description:  New description 
## Data args:    viewId date_range metric 
## Input data:   date 
## Model args:   df frequency 
## Packages:

You can also pass it the filename, which will load, make the edit, then save the model to disk again:

ga_model_edit("my_model.gamr", description = "New description")

If you want to examine or change the functions in a model, you can use ga_model_write() to write them to a file, or examine them directly from the model object. The structure of the model object can be examined using str():

str(decomp_ga_advanced, give.attr = FALSE)
## List of 7
##  $ data_f           :function (viewId, date_range = c(Sys.Date() - 300, Sys.Date()), metric, 
##     ...)  
##  $ required_columns : chr "date"
##  $ model_f          :function (df, frequency, ...)  
##  $ output_f         :function (x, y, ...)  
##  $ required_packages: NULL
##  $ description      : chr "Performs decomposition and creates a plot"
##  $ shiny_module     :List of 2
##   ..$ ui    :function (id, ...)  
##   ..$ server:function (input, output, session, view_id, ...)

And you can access various elements by the usual list methods:

## function(viewId,
##                            date_range = c(Sys.Date()- 300, Sys.Date()),
##                            metric,
##                            ...){
##    o <- google_analytics(viewId,
##                     date_range = date_range,
##                     metrics = metric,
##                     dimensions = "date",
##                     max = -1)
##     # rename the metric column so its found for modelling
##     o$the_metric <- o[, metric]
##     o
##  }
## [1] "Performs decomposition and creates a plot"

GA Effect with ga_models

To make your own portable GA Effect, this model uses the CausalImpact and dygraphs libraries to make a plot of your GA data.

This example model is available via ga_model_example("ga-effect.gamr")

Get data

The data will focus on sessions per channel grouping. For this example the end user can select the date range, but we set a default of the last 600 days.

The modelling step is copied over from the time-services example.

The function transforms the data into the right shape, and performs the CausalImpact model. The user can select the event date to examine, the channel to test (response) and possible predictors to help the model.

Finally the CausalImpact model is sent into Dygraphs for interactive visualisation. The event date is the same as the one sent to the modelling step, and used to indicate it on the plot:

The main functions done, we now specify which R packages the model needs the user to load.

req_packs <- c("CausalImpact", "xts", "tidyr", "googleAnalyticsR", "assertthat", "dygraphs")

Finally we make the model, specifying which columns we expect the data to fetch, a description and specifying which Shiny functions are needed to show the dygraph if the model is used in a Shiny app.

## ==ga_model object==
## Description:  Causal Impact on channelGrouping data 
## Data args:    viewId date_range 
## Input data:   date channelGrouping sessions 
## Model args:   df event_date response predictors 
## Output args:  impact event_date 
## Packages:     CausalImpact xts tidyr googleAnalyticsR assertthat dygraphs
# save it to a file for use later
ga_model_save(ci_model, "causalImpact_model.gamr")

To use and make an interactive plot:


ga_auth(email = Sys.getenv("GARGLE_EMAIL"))

ci <- ga_model(81416156, ci_model, event_date = as.Date("2019-01-01"))

# print to show the plot object

That can display interactively in a tweet:

Similarly, you can launch this in a Shiny app by slightly modifying the Shiny example used previously.

This is available within the package via shiny::runApp(system.file("shiny/models-ga-effect", package="googleAnalyticsR"))

Dress it up in a nice theme and add some more inputs and outputs and you are close to the finished result.