Building your own Google R library

Mark Edmondson


Build a Google API library for R

Generating your function

Creating your own API should be a matter of consulting the Google API documentation, and filling in the required details.

gar_api_generator() has these components:

Example below for generating a function:

  f <- gar_api_generator("",
                         data_parse_function = function(x) x$id)

Using your generated function

The function generated uses path_args and pars_args to create a template, but when the function is called you will want to pass dynamic data to them. This is done via the path_arguments and pars_arguments parameters.

path_args and pars_args and path_arguments and pars_arguments all accept named lists.

If a name in path_args is present in path_arguments, then it is substituted in. This way you can pass dynamic parameters to the constructed function. Likewise for pars_args and pars_arguments.

## Create a function that requires a path argument /accounts/{accountId}
  f <- gar_api_generator("",
                         path_args = list(accounts = "defaultAccountId")
                         data_parse_function = function(x) x$id)
## When using f(), pass the path_arguments function to it 
## with the same name to modify "defaultAccountId":
  result <- f(path_arguments = list(accounts = "myAccountId"))

Body data

A lot of Google APIs look for you to send data in the Body of the request. This is done after you construct the function. googleAuthR uses httr’s JSON parsing via jsonlite to construct JSON from R lists.

Construct your list, then use jsonlite::toJSON to check if its in the correct format as specified by the Google documentation. This is often the hardest part using the API.

To aid debugging use the options(googleAuthR.verbose = 0) to see all the sent and recieved HTTP requests, and also write what was sent as JSON in the body is written to a file called request_debug.rds in the working directory.


options(googleAuthR.verbose = 0)

blah <- google_analytics_4(1212121, date_range = c(Sys.Date() - 7, Sys.Date()), metrics = "sessions")
Calling APIv4....
Single v4 batch
Token exists.
Valid local token
Body JSON parsed to: {"reportRequests":[{"viewId":"ga:121211","dateRanges":[{"startDate":"2017-01-06","endDate":"2017-01-13"}],"samplingLevel":"DEFAULT","metrics":[{"expression":"ga:sessions","alias":"sessions","formattingType":"METRIC_TYPE_UNSPECIFIED"}],"pageToken":"0","pageSize":1000,"includeEmptyRows":true}]}
-> POST /v4/reports:batchGet/ HTTP/1.1
-> Host:
-> User-Agent: googleAuthR/ (gzip)
-> Accept: application/json, text/xml, application/xml, */*
-> Content-Type: application/json
-> Accept-Encoding: gzip
-> Authorization: Bearer ya29XXXXX_EhpEot1ZPNP28MUmSz5EyQ7lY3kgNCFEefYv-Zof3a1RSwezgMJ5llCO44TA9iHi51c
-> Content-Length: 295
>> {"reportRequests":[{"viewId":"ga:1212121","dateRanges":[{"startDate":"2017-01-06","endDate":"2017-01-13"}],"samplingLevel":"DEFAULT","metrics":[{"expression":"ga:sessions","alias":"sessions","formattingType":"METRIC_TYPE_UNSPECIFIED"}],"pageToken":"0","pageSize":1000,"includeEmptyRows":true}]}

<- HTTP/1.1 200 OK
<- Content-Type: application/json; charset=UTF-8
<- Vary: Origin
<- Vary: X-Origin
<- Vary: Referer
<- Content-Encoding: gzip
<- Date: Fri, 13 Jan 2017 10:45:38 GMT
<- Server: ESF
<- Cache-Control: private
<- X-XSS-Protection: 1; mode=block
<- X-Frame-Options: SAMEORIGIN
<- X-Content-Type-Options: nosniff
<- Alt-Svc: quic=":443"; ma=2592000; v="35,34"
<- Transfer-Encoding: chunked
Downloaded [1] rows from a total of [1].

> readRDS("request_debug.rds")
[1] ""

[1] "POST"


Parsing data

Not all API calls return data, but if they do:

If you have no data_parse_function then the function returns the whole request object. The content is available in $content. You can then parse this yourself, or pass a function in to do it for you.

If you parse in a function into data_parse_function, it works on the response’s $content.

Example below of the differences between having a data parsing function and not:

  ## the body object that will be passed in
  body = list(
    longUrl = ""
  ## no data parsing function
  f <- gar_api_generator("",
  no_parse <- f(the_body = body)
  ## parsed data, only taking request$content$id
  f2 <- gar_api_generator("",
                          data_parse_function = function(x) x$id)
  parsed <- f2(the_body = body)
  ## str(no_parse) has full details of API response.
  ## just looking at no_parse$content as this is what API returns
  > str(no_parse$content)
  List of 3
   $ kind   : chr "urlshortener#url"
   $ id     : chr ""
   $ longUrl: chr ""
  ## compare to the above - equivalent to no_parse$content$id 
  > str(parsed)
   chr ""

The response is turned from JSON to a dataframe if possible, via jsonlite::fromJSON

Auto-build libraries

From 0.4 is helper functions that use Google’s API Discovery service.

This is a meta-API which holds all the necessary details to build a supported Google API, which is all modern Google APIs. At the time of writing this is 152 libraries.

These libraries aren’t intended to be submitted to CRAN or used straight away, but should take away a lot of documentation and function building work so you can concentrate on tests, examples and helper functions for your users.

Get a list of the current APIs via gar_discovery_apis_list()

all_apis <- gar_discovery_apis_list()

To get details of a particular API, use its name and version in the gar_discovery_api() function:

a_api <- gar_discovery_api("urlshortener", "v1")

You can then pass this list to gar_create_package() along with a folder path to create all the files necessary for an R library. There are arguments to set it up with RStudio project files, do a CRAN CMD check and upload it to Github.

vision_api <- gar_discovery_api("vision", "v1")
                   rstudio = FALSE,
                   github = FALSE)

Auto-build all libraries

A loop to build all the Google libraries is shown below, the results of which is available in this Github repo.


api_df <- gar_discovery_apis_list()

api_json_list <- mapply(gar_discovery_api, api_df$name, api_df$version)

## WARNING: this takes a couple of hours
check_results <- lapply(api_json_list, 
                        directory = "/Users/mark/dev/R/autoGoogleAPI",
                        github = FALSE)

Creating an API library from scratch - Example with

Below is an example building a link shortner R package using googleAuthR. It was done referring to the documentation for Google URL shortener. Note the help docs specifies the steps outlined above. These are in general the steps for every Google API.

  1. Creating a project
  2. Activate API
  3. Provide scope
  4. Specify the base URL (in this case it was
  5. Specify the httr request type e.g. POST
  6. Constructing a body request
  7. Giving the response format

Example R library

## change the native googleAuthR scopes to the one needed.
options("googleAuthR.scopes.selected" = 
#' Shortens a url using
#' @param url URl to shorten with
#' @return a string of the short URL
shorten_url <- function(url){
  body = list(
    longUrl = url
  f <- gar_api_generator("",
                         data_parse_function = function(x) x$id)
  f(the_body = body)
#' Expands a url that has used
#' @param shortUrl Url that was shortened with
#' @return a string of the expanded URL
expand_url <- function(shortUrl){
  f <- gar_api_generator("",
                         pars_args = list(shortUrl = "shortUrl"),
                         data_parse_function = function(x) x)
  f(pars_arguments = list(shortUrl = shortUrl))
#' Get analytics of a url that has used
#' @param shortUrl Url that was shortened with
#' @param timespan The time period for the analytics data
#' @return a dataframe of the Url analytics
analytics_url <- function(shortUrl, 
                          timespan = c("allTime", "month", "week","day","twoHours")){
  timespan <- match.arg(timespan)
  f <- gar_api_generator("",
                         pars_args = list(shortUrl = "shortUrl",
                                          projection = "FULL"),
                         data_parse_function = function(x) { 
                                    a <- x$analytics 
  f(pars_arguments = list(shortUrl = shortUrl))
#' Get the history of the authenticated user
#' @return a dataframe of the user's history
user_history <- function(){
  f <- gar_api_generator("",
                         data_parse_function = function(x) x$items)

To use the above functions:

# go through authentication flow
s <- shorten_url("")
analytics_url(s, timespan = "month")

Creating a Google Calendar list

Another example is shown below to fetch your Google calendar entries. The below uses the API skeletons functions that are auto generated via the gar_discovery_api functions.

Make sure the calendar API is activated for your Google Project at below URL:

#' Gets a list of events for calendarId
#' @param calendarId The calendar to get.  Default is primary for authenticated user
#' @return a big list of JSON
events.list <- function(calendarId = "primary") {
  url <- sprintf("", 

  f <- googleAuthR::gar_api_generator(url, "GET", 
                                      data_parse_function = function(x) x)

To use your function:

## set scopes for calendar
options(googleAuthR.scopes.selected = "",
        googleAuthR.client_id = "XXXX",  ## add your Google project client Id
        googleAuthR.client_secret = "XXXX")  ## add your Google project client secret
## authenticate with email that has access to the calendar

## should kick you out to Google OAuth2 flow.  Come back here when done....

## get default (primary) calendar list
events <- events.list()

## events is raw JSON response, 
## parse down to items by modifying the data_parse_function in events.list()
## or operating afterwards in code like below