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:
baseURI - all APIs have a base for every API callhttp_header - what type of request, most common are GET and POSTpath_args - some APIs need you to alter the URL folder structure when calling, e.g. /account/{accountId}/ where accountId is variable.pars_args - other APIS require you to send URL parameters e.g. ?account={accountId} where accountId is variable.data_parse_function - [optional] If the API call returns data, it will be available in $content. You can create a parsing function that transforms it in to something you can work with (for instance, a dataframe)Example below for generating a function:
f <- gar_api_generator("https://www.googleapis.com/urlshortener/v1/url",
"POST",
data_parse_function = function(x) x$id)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("https://www.googleapis.com/example",
"POST",
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"))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 option(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.
Example:
library(googleAuthR)
library(googleAnalyticsR)
options(googleAuthR.verbose = 0)
ga_auth()
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
Request: https://analyticsreporting.googleapis.com/v4/reports:batchGet/
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: analyticsreporting.googleapis.com
-> User-Agent: googleAuthR/0.4.0.9000 (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")
$url
[1] "https://analyticsreporting.googleapis.com/v4/reports:batchGet/"
$request_type
[1] "POST"
$body_json
{"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}]}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 $content.
Example below of the differences between having a data parsing function and not:
## the body object that will be passed in
body = list(
longUrl = "http://www.google.com"
)
## no data parsing function
f <- gar_api_generator("https://www.googleapis.com/urlshortener/v1/url",
"POST")
no_parse <- f(the_body = body)
## parsed data, only taking request$content$id
f2 <- gar_api_generator("https://www.googleapis.com/urlshortener/v1/url",
"POST",
data_parse_function = function(x) x$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 "http://goo.gl/ZwT9pG"
$ longUrl: chr "http://www.google.com/"
## compare to the above - equivalent to no_parse$content$id
> str(parsed)
chr "http://goo.gl/mCYw2i"
The response is turned from JSON to a dataframe if possible, via jsonlite::fromJSON
In some cases you may want to skip all parsing of API content, perhaps if it is not JSON or some other reason. For these cases, you can use the option option("googleAuthR.rawResponse" = TRUE) to skip all tests and return the raw response. Here is an example of this from the googleCloudStorageR library:
gcs_get_object <- function(bucket,
object_name){
## skip JSON parsing on output as we epxect a CSV
options(googleAuthR.rawResponse = TRUE)
## do the request
ob <- googleAuthR::gar_api_generator("https://www.googleapis.com/storage/v1/",
path_args = list(b = bucket,
o = object_name),
pars_args = list(alt = "media"))
req <- ob()
## set it back to FALSE for other API calls.
options(googleAuthR.rawResponse = FALSE)
req
}If you are doing many API calls, you can speed this up a lot by using the batch option. This takes the API functions you have created and wraps them in the gar_batch function to request them all in one POST call. You then recieve the responses in a list. Note that this does not count as one call for API limits purposes, it just speeds up the processing. The example below queries from two different APIs and returns them in a list: It lists websites in your Google Search Console, and shows your goo.gl link history.
## from search console API
list_websites <- function() {
l <- gar_api_generator("https://www.googleapis.com/webmasters/v3/sites",
"GET",
data_parse_function = function(x) x$siteEntry)
l()
}
## from goo.gl API
user_history <- function(){
f <- gar_api_generator("https://www.googleapis.com/urlshortener/v1/url/history",
"GET",
data_parse_function = function(x) x$items)
f()
}
googleAuthR::gar_auth(new_user=T)
ggg <- gar_batch(list(list_websites(), user_history()))A common batch task is to walk through the same API call, modifying only one parameter. An example includes walking through Google Analytics API calls by date to avoid sampling. A function to enable this is implemented at gar_batch_walk, with an example below:
walkData <- function(ga, ga_pars, start, end){
dates <- as.character(
seq(as.Date(start, format="%Y-%m-%d"),
as.Date(end, format="%Y-%m-%d"),
by=1))
ga_pars$samplingLevel <- "HIGHER_PRECISION"
anyBatchSampled <- FALSE
samplePercent <- 0
## this is applied to each batch to keep tally of meta data
bf <- function(batch_data){
lapply(batch_data, function(the_data) {
if(attr(the_data, 'containsSampledData')) anyBatchSampled <<- TRUE
samplePercent <<- samplePercent + attr(the_data, "samplePercent")
})
batch_data
}
## the walk through batch function.
## In this case both start-date and end-date are set to the date iteration
## if the output is parsed as a dataframe, it also includes a rbind function
## otherwise, it will return a list of lists
walked_data <- googleAuthR::gar_batch_walk(ga,
dates,
gar_pars = ga_pars,
pars_walk = c("start-date", "end-date"),
batch_function = bf,
data_frame_output = TRUE)
message("Walked through all dates. Total Results: [", NROW(walked_data), "]")
attr(walked_data, "dateRange") <- list(startDate = start, endDate = end)
attr(walked_data, "totalResults") <- NROW(walked_data)
attr(walked_data, "samplingLevel") <- "HIGHER_PRECISION, WALKED"
attr(walked_data, "containsSampledData") <- anyBatchSampled
attr(walked_data, "samplePercent") <- samplePercent / length(dates)
walked_data
}New in 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")
gar_create_package(vision_api,
"/Users/mark/dev/R/autoGoogleAPI/",
rstudio = FALSE,
github = FALSE)A loop to build all the Google libraries is shown below, the results of which is available in this Github repo.
library(googleAuthR)
api_df <- gar_discovery_apis_list()
api_json_list <- mapply(gar_discovery_api, api_df$name, api_df$version)
## WARNING: this takes a couple of hours
check_results <- lapply(api_json_list,
gar_create_package,
directory = "/Users/mark/dev/R/autoGoogleAPI",
github = FALSE)Below is an example building a link shortner R package using googleAuthR. It was done referring to the documentation for Google URL shortener. Note the help docs specifies the steps outlined above. These are in general the steps for every Google API.
https://www.googleapis.com/urlshortener/v1/url)POSTlibrary(googleAuthR)
## change the native googleAuthR scopes to the one needed.
options("googleAuthR.scopes.selected" =
c("https://www.googleapis.com/auth/urlshortener"))
#' Shortens a url using goo.gl
#'
#' @param url URl to shorten with goo.gl
#'
#' @return a string of the short URL
shorten_url <- function(url){
body = list(
longUrl = url
)
f <- gar_api_generator("https://www.googleapis.com/urlshortener/v1/url",
"POST",
data_parse_function = function(x) x$id)
f(the_body = body)
}
#' Expands a url that has used goo.gl
#'
#' @param shortUrl Url that was shortened with goo.gl
#'
#' @return a string of the expanded URL
expand_url <- function(shortUrl){
f <- gar_api_generator("https://www.googleapis.com/urlshortener/v1/url",
"GET",
pars_args = list(shortUrl = "shortUrl"),
data_parse_function = function(x) x)
f(pars_arguments = list(shortUrl = shortUrl))
}
#' Get analyitcs of a url that has used goo.gl
#'
#' @param shortUrl Url that was shortened with goo.gl
#' @param timespan The time period for the analytics data
#'
#' @return a dataframe of the goo.gl Url analytics
analytics_url <- function(shortUrl,
timespan = c("allTime", "month", "week","day","twoHours")){
timespan <- match.arg(timespan)
f <- gar_api_generator("https://www.googleapis.com/urlshortener/v1/url",
"GET",
pars_args = list(shortUrl = "shortUrl",
projection = "FULL"),
data_parse_function = function(x) {
a <- x$analytics
return(a[timespan][[1]])
})
f(pars_arguments = list(shortUrl = shortUrl))
}
#' Get the history of the authenticated user
#'
#' @return a dataframe of the goo.gl user's history
user_history <- function(){
f <- gar_api_generator("https://www.googleapis.com/urlshortener/v1/url/history",
"GET",
data_parse_function = function(x) x$items)
f()
}To use the above functions:
library(googleAuthR)
# go through authentication flow
gar_auth()
s <- shorten_url("http://markedmondson.me")
s
expand_url(s)
analytics_url(s, timespan = "month")
user_history()