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Wrapping APIs

Source: vignettes/articles/wrapping-apis.Rmd
wrapping-apis.Rmd

A common use for httr2 is wrapping up a useful API and exposing it in an R package where each API endpoint (i.e. a URL with parameters) becomes an R function with documented arguments. This vignette will show you how, starting with a very simple API that doesn’t need authentication, then slowly working up in complexity. Along the way, you’ll learn about how to:

  • Expose important details from HTTP errors in R errors.

  • Handle various types of authentication.

  • Consistently throttle the rate of requests or dynamically respond to rate limiting headers sent by the server.

I assume you’re familiar with the basics of building a package. If not, you might want to read the “The Whole Game” chapter of R packages first.

Faker API

We’ll start with a very simple API, faker API, which provides a collection of techniques for generating fake data. Before we start writing the sort of functions that you might put in a package, we’ll perform a request just to see how the basics work:

# We start by creating a request that uses the base API url
req <- request("https://fakerapi.it/api/v1")
resp <- req |>
  # Then we add on the images path
  req_url_path_append("images") |>
  # Add query parameters _width and _quantity
  req_url_query(`_width` = 380, `_quantity` = 1) |>
  req_perform()

# The result comes back as JSON
resp |> resp_body_json() |> str()
#> List of 6
#>  $ status: chr "OK"
#>  $ code  : int 200
#>  $ locale: chr "en_US"
#>  $ seed  : NULL
#>  $ total : int 1
#>  $ data  :List of 1
#>   ..$ :List of 3
#>   .. ..$ title      : chr "Doloribus quae odio est."
#>   .. ..$ description: chr "Vero et et voluptatem modi id. Adipisci sed ipsa nemo suscipit itaque molestiae impedit. Sapiente velit aut mol"| __truncated__
#>   .. ..$ url        : chr "https://picsum.photos/380/480"

Errors

It’s always worth a little early experimentation to see if we get any useful information from errors. The httr2 defaults get in your way here, because if you retrieve an unsuccessful HTTP response, you automatically get an error that prevents you from further inspecting the body:

req |>
  req_url_path_append("invalid") |>
  req_perform()
#> Error in `req_perform()`:
#> ! HTTP 400 Bad Request.

However, you can access the last response (successful or not) with last_response():

resp <- last_response()
resp |> resp_body_json()
#> $message
#> [1] "Resource invalid not supported in version v1"

It doesn’t look like there’s anything useful there. Sometimes useful info is returned in the headers, so let’s check:

resp |> resp_headers()
#> <httr2_headers>
#> Server: nginx
#> Content-Type: application/json
#> Transfer-Encoding: chunked
#> Connection: keep-alive
#> X-Powered-By: PHP/8.3.8
#> Cache-Control: no-cache, private
#> Date: Tue, 19 Nov 2024 07:54:03 GMT
#> X-RateLimit-Limit: 60
#> X-RateLimit-Remaining: 56
#> Access-Control-Allow-Origin: *

It doesn’t look like we’re getting any more useful information, so we can leave the req_error() default as is. We’ll have another go later with an API that does provide more details.

User agent

If you’re wrapping this code into a package, it’s considered polite to set a user agent, so that, if your package accidentally does something horribly wrong, the developers of the website can figure out who to reach out to. You can do this with the req_user_agent() function:

req |>
  req_user_agent("my_package_name (http://my.package.web.site)") |>
  req_dry_run()
#> GET /api/v1 HTTP/1.1
#> Host: fakerapi.it
#> User-Agent: my_package_name (http://my.package.web.site)
#> Accept: */*
#> Accept-Encoding: deflate, gzip, br, zstd

Core request function

Once you’ve made a few successful requests, it’s worth seeing if you can figure out the general pattern so you can wrap it up into a function that will become the core of your package.

For faker, I spent a little time with the documentation noting some commonalities:

  • Every URL is of the form https://fakerapi.it/api/v1/{resource}, and data is passed to the resource with query parameters. All parameters start with _.

  • Every resource has three common query parameters: _locale, _quantity, and _seed.

  • All endpoints return JSON data.

This led me to construct the following function:

faker <- function(resource, ..., quantity = 1, locale = "en_US", seed = NULL) {
  params <- list(
    ...,
    quantity = quantity,
    locale = locale,
    seed = seed
  )
  names(params) <- paste0("_", names(params))

  request("https://fakerapi.it/api/v1") |>
    req_url_path_append(resource) |>
    req_url_query(!!!params) |>
    req_user_agent("my_package_name (http://my.package.web.site)") |>
    req_perform() |>
    resp_body_json()
}

str(faker("images", width = 300))
#> List of 6
#>  $ status: chr "OK"
#>  $ code  : int 200
#>  $ locale: chr "en_US"
#>  $ seed  : NULL
#>  $ total : int 1
#>  $ data  :List of 1
#>   ..$ :List of 3
#>   .. ..$ title      : chr "Aliquam sed enim recusandae."
#>   .. ..$ description: chr "Inventore at minima placeat itaque rerum voluptatum sed. Labore architecto debitis fugit vel omnis. Exercitatio"| __truncated__
#>   .. ..$ url        : chr "https://picsum.photos/300/480"

I’ve made a few important choices here:

  • I’ve decided to supply default values for the quantity and locale parameters. This makes my function easier to demo in this vignette.

  • I’ve used a default of NULL for the seed argument. req_url_query() will automatically drop NULL arguments so this means that no default value is sent to the API, but when you read the function definition you can still see that seed is accepted.

  • I automatically prefix all query parameters with _ because argument names starting with _ are hard to type in R.

  • My function generates the request, performs it, and extracts the body of the response. This works well for simple cases, but for more complex APIs you might want to return a request object that can be modified before being performed.

I also used one cool trick: req_url_query() uses dynamic dots, so we can use !!! to convert (e.g.) req_url_query(req, !!!list(`_quantity` = 1, `_locale` = "en_US")) into req_url_query(req, `_quantity` = 1, `_locale` = "en_US").

Wrapping individual endpoints

faker() is quite general — it’s a good tool for the package developer because you can read the faker documentation and translate it to a function call. But it’s not very friendly for the package user who might not know anything about web APIs. So typically the next step in the process is to wrap up some individual endpoints with their own functions.

For example, let’s take the persons endpoint which has three additional parameters: gender (male or female), birthday_start, and birthday_end. A simple wrapper would start something like this:

faker_person <- function(gender = NULL, birthday_start = NULL, birthday_end = NULL, quantity = 1, locale = "en_US", seed = NULL) {
  faker(
    "persons",
    gender = gender,
    birthday_start = birthday_start,
    birthday_end = birthday_end,
    quantity = quantity,
    locale = locale,
    seed = seed
  )
}
str(faker_person("male"))
#> List of 6
#>  $ status: chr "OK"
#>  $ code  : int 200
#>  $ locale: chr "en_US"
#>  $ seed  : NULL
#>  $ total : int 1
#>  $ data  :List of 1
#>   ..$ :List of 10
#>   .. ..$ id       : int 1
#>   .. ..$ firstname: chr "Giovanny"
#>   .. ..$ lastname : chr "Corwin"
#>   .. ..$ email    : chr "cecilia65@tremblay.com"
#>   .. ..$ phone    : chr "+18452268359"
#>   .. ..$ birthday : chr "1935-10-07"
#>   .. ..$ gender   : chr "male"
#>   .. ..$ address  :List of 10
#>   .. .. ..$ id            : int 1
#>   .. .. ..$ street        : chr "7161 Hickle Terrace"
#>   .. .. ..$ streetName    : chr "Goldner Islands"
#>   .. .. ..$ buildingNumber: chr "75360"
#>   .. .. ..$ city          : chr "Williamsonshire"
#>   .. .. ..$ zipcode       : chr "32372"
#>   .. .. ..$ country       : chr "Nauru"
#>   .. .. ..$ country_code  : chr "NR"
#>   .. .. ..$ latitude      : num -23.4
#>   .. .. ..$ longitude     : num 174
#>   .. ..$ website  : chr "http://runolfsson.com"
#>   .. ..$ image    : chr "http://placeimg.com/640/480/people"

We could make it more user friendly by checking the input types, and returning the result as a tibble. I did a quick and dirty conversion using purrr; depending on your needs you could use base R code or tidyr::hoist().

library(purrr)

faker_person <- function(gender = NULL, birthday_start = NULL, birthday_end = NULL, quantity = 1, locale = "en_US", seed = NULL) {
  if (!is.null(gender)) {
    gender <- match.arg(gender, c("male", "female"))
  }
  if (!is.null(birthday_start)) {
    if (!inherits(birthday_start, "Date")) {
      stop("`birthday_start` must be a date")
    }
    birthday_start <- format(birthday_start, "%Y-%m-%d")
  }
  if (!is.null(birthday_end)) {
    if (!inherits(birthday_end, "Date")) {
      stop("`birthday_end` must be a date")
    }
    birthday_end <- format(birthday_end, "%Y-%m-%d")
  }

  json <- faker(
    "persons",
    gender = gender,
    birthday_start = birthday_start,
    birthday_end = birthday_end,
    quantity = quantity,
    locale = locale,
    seed = seed
  )

  tibble::tibble(
    firstname = map_chr(json$data, "firstname"),
    lastname = map_chr(json$data, "lastname"),
    email = map_chr(json$data, "email"),
    gender = map_chr(json$data, "gender")
  )
}
faker_person("male", quantity = 5)
#> # A tibble: 5 × 4
#>   firstname lastname email                         gender
#>   <chr>     <chr>    <chr>                         <chr> 
#> 1 Jairo     Murazik  vandervort.eleonore@gmail.com male  
#> 2 Neil      Bernhard ipollich@yahoo.com            male  
#> 3 Rashawn   Langosh  anderson.upton@gmail.com      male  
#> 4 Stone     Kautzer  rherzog@hyatt.com             male  
#> 5 Melany    Wunsch   winona.lueilwitz@beier.com    male

The next steps would be to export and document this function; I’ll leave that up to you.

Secret management

We need to take a quick break from APIs to talk about secrets. Secrets are important, because every API (except for very simple APIs like faker) is going to require that you identify yourself in some way, typically with an API key or a token. And even if you plan to require your users to supply this information, you’ll still need to record your own credentials in order to test your own package.

This system described below is likely to be overkill if you have one secret that only needs to be shared in a couple of places: you can just put it in your .Renviron and access it with Sys.getenv(). But you will probably accumulate more secrets over time, and you’ll need to figure out how to share them with other people and other computers, so I think spending a little time to understand this system and set up it for your package will pay off in the long term.

Basics

httr2 provides secret_encrypt() and secret_decrypt() to scramble secrets so that you can include them in your public source code without worrying that others can read them. There are three basic steps to this process:

  1. You create an encryption key with secret_make_key() that is used to scramble and descramble secrets using symmetric cryptography:

    key <- secret_make_key()
key
#> [1] "opwZVMBZCvqvSwJEMnC2Nw"

    (Note that secret_make_key() uses a cryptographically secure random number generator provided by OpenSSL; it is not affected by R’s RNG settings, and there’s no way to make it reproducible.)

  2. You scramble your secrets with secret_encrypt() and store the resulting text directly in the source code of your package:

    secret_scrambled <- secret_encrypt("secret I need to work with an API", key)
secret_scrambled
#> [1] "S-LHGrAYMOzJ2P98oNAoz7a1A28mUrm-jMgNvLs1WBne1Bl55JADH8kofR-x7fomrQ"

  3. When needed, you descramble the secret using secret_decrypt():

    secret_decrypt(secret_scrambled, key)
#> [1] "secret I need to work with an API"

Package keys and secrets

You can create any number of encryption keys, but I highly recommend that you create one key per package, which I’ll call the package key. In this section, I’ll show you how to store that key so that you (and your automated tests) can use it, but no one else can.

httr2 is built around the notion that this key should live in an environment variable. So the first step is to make your package key available on your local development machine by adding a line to your your user-level .Renviron (which you can easily open with usethis::edit_r_environ()):

YOURPACKAGE_KEY=key_you_generated_with_secret_make_key

Now (after you restart R), you’ll be able to take advantage of a special secret_encrypt() and secret_decrypt() feature: the key argument can be the name of an environment variable, instead of the encryption key itself. In fact, this is most natural usage.

secret_scrambled <- secret_encrypt("secret I need to work with an API", "YOURPACKAGE_KEY")
secret_scrambled
#> [1] "55QkxyG_FhAqcD_hKLjeFIudWyjLwoBiTD1dlRhcK-xotEXhgyGEJJnMJ1OTjtGTwA"
secret_decrypt(secret_scrambled, "YOURPACKAGE_KEY")
#> [1] "secret I need to work with an API"

You’ll also need to make the key available in your GitHub Actions (both check and pkgdown) so your automated tests can use it. This requires two steps:

  1. Add the key to your repository secrets.

  2. Share the key with the workflows that need it by adding a line to the appropriate workflow:

        env:
      YOURPACKAGE_KEY: ${{ secrets.YOURPACKAGE_KEY }}

    You can see how httr2 does it in its GitHub workflow.

Other continuous integration platforms will offer similar ways to make a key available as a secure environment variable.

When the package key isn’t available

There are a few important cases where your code won’t have access to your package key: on CRAN, on the personal machines of external contributors, and in automated checks on their PRs. So if you want to share your package on CRAN or make it easy for others to contribute, you need to make sure that your examples, vignettes, and tests all work without error:

  • In vignettes, you can run knitr::opts_chunk(eval = secret_has_key("YOURPACKAGE_KEY")) so that chunks are only evaluated if your key is available.

  • In examples, you can surround code blocks that require your key with if (httr2::secret_has_key("YOURPACKAGE_KEY")) {}

  • You don’t need to do anything in tests because when secret_decrypt() is run by testthat, it will automatically skip() the test if the key isn’t available.

NYTimes Books API

Next we’ll take a look at the NYTimes Books API. It requires a very simple authentication with an API key that’s included in every request. When you’re wrapping an API that has a key you’re going to face two struggles:

  • How do you test your package without sharing your key with the whole world?

  • How do you allow your users to supply their own key, without having to pass it to every function?

So now you can understand how the following code works to get my NYTimes Book API key:

my_key <- secret_decrypt("4Nx84VPa83dMt3X6bv0fNBlLbv3U4D1kHM76YisKEfpCarBm1UHJHARwJHCFXQSV", "HTTR2_KEY")

I’ll start by tackling the first problem because otherwise there’s no way for me to show how the API works in this vignette 😃. We’ll come back to the second at the very end of this section, because it’s easiest to tackle once we have a function in place.

Security considerations

Note that including an API key as a query parameter is relatively insecure; if an API uses this method of auth, it’s typically because the key is relatively easy to create or gives relatively few privileges. Here it only takes a couple of minutes to generate your own NYTimes API key, so there’s little incentive for someone to try and steal yours.

The main problem of conveying credentials via the url is that it’s easily exposed, because httr2 makes no efforts to redact confidential information stored in query parameters. This means it’s relatively easy to leak your key if you use req_perform(verbosity = 1), req_dry_run(), or even just print the request object. And indeed, you’ll see that in the examples below — this is bad practice for a real package, but I think it’s ok here because the key doesn’t allow you to do anything valuable and it makes teaching APIs so much easier.

Basic request

Now let’s perform a test request and look at the response:

resp <- request("https://api.nytimes.com/svc/books/v3") |>
  req_url_path_append("/reviews.json") |>
  req_url_query(`api-key` = my_key, isbn = 9780307476463) |>
  req_perform()
resp

Like most modern APIs, this one returns the results as JSON:

resp |>
  resp_body_json() |>
  str()

Before we start wrapping this up into a function, let’s consider what happens with errors.

Error handling

What happens if there’s an error? For example, if we deliberately supply an invalid key:

resp <- request("https://api.nytimes.com/svc/books/v3") |>
  req_url_path_append("/reviews.json") |>
  req_url_query(`api-key` = "invalid", isbn = 9780307476463) |>
  req_perform()

To see if there’s any extra useful information we can again look at last_response():

resp <- last_response()
resp
resp |> resp_body_json()

It looks like there’s some useful additional info in the faultstring:

resp |> resp_body_json() |> _$fault |> _$faultstring

To add that information to future errors we can use the body argument to req_error(). This should be a function that takes a response and returns a character vector of additional information to include in the error. Once we do that and re-fetch the request, we see the additional information displayed in the R error:

nytimes_error_body <- function(resp) {
  resp |> resp_body_json() |> _$fault |> _$faultstring
}

resp <- request("https://api.nytimes.com/svc/books/v3") |>
  req_url_path_append("/reviews.json") |>
  req_url_query(`api-key` = "invalid", isbn = 9780307476463) |>
  req_error(body = nytimes_error_body) |>
  req_perform()

Rate limits

Another common source of errors is rate-limiting — this is used by many servers to prevent one unruly client consuming too many resources. The frequently asked questions page describes the rate limits for the NYT APIs:

Yes, there are two rate limits per API: 4,000 requests per day and 10 requests per minute. You should sleep 6 seconds between calls to avoid hitting the per minute rate limit. If you need a higher rate limit, please contact us at .

Many APIs return additional information about how long to wait when the rate limit is exceeded (often using the Retry-After header). So I deliberately violated the rate limit by quickly making 11 requests; unfortunately while the response was a standard 429 (Too many requests), it did not include any information about how long to wait in either the response body or the headers. That means we can’t use req_retry(), which automatically waits the amount of time the server requests. Instead, we’ll use req_throttle() to ensure we don’t make more than 10 requests every 60 seconds:

req <- request("https://api.nytimes.com/svc/books/v3") |>
  req_url_path_append("/reviews.json") |>
  req_url_query(`api-key` = "invalid", isbn = 9780307476463) |>
  req_throttle(10 / 60)

By default, req_throttle() shares the limit across all requests made to the host (i.e. api.nytimes.com). Since the docs suggest the rate limit applies per API, you might want to use the realm argument to be a bit more specific:

req <- request("https://api.nytimes.com/svc/books/v3") |>
  req_url_path_append("/reviews.json") |>
  req_url_query(`api-key` = "invalid", isbn = 9780307476463) |>
  req_throttle(10 / 60, realm = "https://api.nytimes.com/svc/books")

Wrapping it up

Putting together all the pieces above yields a function something like this:

nytimes_books <- function(api_key, path, ...) {
  request("https://api.nytimes.com/svc/books/v3") |>
    req_url_path_append(path) |>
    req_url_query(..., `api-key` = api_key) |>
    req_error(body = nytimes_error_body) |>
    req_throttle(10 / 60, realm = "https://api.nytimes.com/svc/books") |>
    req_perform() |>
    resp_body_json()
}

drunk <- nytimes_books(my_key, "/reviews.json", isbn = "0316453382")
drunk$results[[1]]$summary

To finish this up for a real package, you’d want to:

  • Add explicit arguments and check that they have the correct type.

  • Export and document the function.

  • Convert the nested list into a more user-friendly data structure (probably a data frame with one row per review).

You’d also want to provide some convenient way for the user to supply their own API key.

User-supplied key

A good place to start is an environment variable, because environment variables are easy to set without typing anything in the console (which can get accidentally shared via your .Rhistory) and are easily set in automated processes. Then you’d write a function to retrieve the API key, returning a helpful message if it’s not found:

get_api_key <- function() {
  key <- Sys.getenv("NYTIMES_KEY")
  if (identical(key, "")) {
    stop("No API key found, please supply with `api_key` argument or with NYTIMES_KEY env var")
  }
  key
}

Then you could modify nytimes_books() to use get_api_key() as the default value for api_key. Since the argument is now optional, we can move it to end of the argument list, since it’ll only be needed in exceptional circumstances.

nytimes_books <- function(path, ..., api_key = get_api_key()) {
  ...
}

You can make this approach a little more user friendly by providing a helper that sets the environment variable:

set_api_key <- function(key = NULL) {
  if (is.null(key)) {
    key <- askpass::askpass("Please enter your API key")
  }
  Sys.setenv("NYTIMES_KEY" = key)
}

Using askpass (or similar) here is good practice since you don’t want to encourage the user to type their secret key into the console, as mentioned above.

It’s a good idea to extend get_api_key() to automatically use your encrypted key to make it easier to write tests:

get_api_key <- function() {
  key <- Sys.getenv("NYTIMES_KEY")
  if (!identical(key, "")) {
    return(key)
  }

  if (is_testing()) {
    return(testing_key())
  } else {
    stop("No API key found, please supply with `api_key` argument or with NYTIMES_KEY env var")
  }
}

is_testing <- function() {
  identical(Sys.getenv("TESTTHAT"), "true")
}

testing_key <- function() {
  secret_decrypt("4Nx84VPa83dMt3X6bv0fNBlLbv3U4D1kHM76YisKEfpCarBm1UHJHARwJHCFXQSV", "HTTR2_KEY")
}

Github Gists API

Next we’ll take a look at an API that can make changes on behalf of a user, not just retrieve data: GitHub’s gist API. This uses different HTTP methods to perform different actions, like creating, updating, and deleting gists. But before we can get to those, let’s handle authentication, rate-limiting, and errors.

Authentication

The easiest way to authenticate with a GitHub API is to use a personal access token. A token is an alternative to a username and password. You have one username + password per site; you can have one token per use case. This lets each use case have a minimal set of permissions, and you can easily revoke one token without affecting any other use case.

I created a personal access token specifically for this vignette that can only access gists, and, as in the last example, stored an encrypted version in this vignette:

token <- secret_decrypt("Guz59woxKoIO_JVtp2IzU3mFIU3ULtaUEa8xvvpYUBdVthR8jhxzc3bMZFhA9HL-ZK6YZudOI6g", "HTTR2_KEY")

If you want to run this vignette yourself, you’ll need to create a new token in your GitHub settings; just make sure it includes the “gist” scope. It’s also a good idea to give every token a descriptive name, that reminds you of its motivating use case, and of where to update it when you have to re-generate it because it expired.

To authenticate a request with the token, we need to put it in the Authorization header with a “token” prefix:

req <- request("https://api.github.com/gists") |>
  req_headers(Authorization = paste("token", token))

req |> req_perform()

Because the authorization header usually contains secret information, httr2 automatically redacts it1:

req
req |> req_dry_run()

Errors

Once you’ve got authentication working, it’s always a good idea to work on errors next, since that will help you debug any failed requests. In my experience APIs rarely do a good job of documenting their errors, so you’ll often have to do a little experimentation. To add to the pain, in large APIs different endpoints often return different amounts of information in different forms. You’ll typically need to tackle your error handling iteratively, improving your code each time you encounter a new problem.

While GitHub does document its errors, I’m sufficiently distrustful that I still want to construct a deliberately malformed query and see what happens:

resp <- request("https://api.github.com/gists") |>
  req_url_query(since = "abcdef") |>
  req_headers(Authorization = paste("token", token)) |>
  req_perform()

As documented I get a 422 “Unprocessable Entity” error. But the response is rather different to documentation which suggests there should be a string message and a list of errors:

resp <- last_response()
resp
resp |> resp_body_json()

I’ll proceed anyway, writing a function that extracts the data and formats it for presentation to the user:

gist_error_body <- function(resp) {
  body <- resp_body_json(resp)

  message <- body$message
  if (!is.null(body$documentation_url)) {
    message <- c(message, paste0("See docs at <", body$documentation_url, ">"))
  }
  message
}
gist_error_body(resp)

Now I can pass this function to the body argument of req_error() and it will be automatically included in the error when a request fails:

request("https://api.github.com/gists") |>
  req_url_query(since = "yesterday") |>
  req_headers(Authorization = paste("token", token)) |>
  req_error(body = gist_error_body) |>
  req_perform()

Notice that each element of the character vector produced by gh_error_body() becomes a bullet in the resulting error.

Rate-limiting

While we’re thinking about errors, it’s useful to look at what happens if the requests are rate limited. Luckily, GitHub consistently uses response headers to provide information about the remaining rate limits.

resp <- req |> req_perform()
resp |> resp_headers("ratelimit")

We can teach httr2 about this so it can automatically wait for a reset if the rate limit is hit. We need to define two functions. The first tells us whether or not a response has a transient error, i.e. it’s worth waiting and trying again. For GitHub, when the rate limit is exceeded, the response has a 403 status and a X-RateLimit-Remaining: 0 header:

gist_is_transient <- function(resp) {
  resp_status(resp) == 403 &&
    resp_header(resp, "X-RateLimit-Remaining") == "0"
}
gist_is_transient(resp)

Then we need a function tells how long to wait. GitHub tells us when the rate limit resets (as number of seconds since 1970-01-01) in the X-RateLimit-Reset header. To convert that to a number of seconds to wait we first convert it to a number (since HTTP headers are always strings), then subtract off the current time (in number of seconds since 1970-01-01):

gist_after <- function(resp) {
  time <- as.numeric(resp_header(resp, "X-RateLimit-Reset"))
  time - unclass(Sys.time())
}
gist_after(resp)

We then pass functions to req_retry() so httr2 has all the information it needs to handle rate-limiting automatically:

request("http://api.github.com") |>
  req_retry(
    is_transient = gist_is_transient,
    after = gist_after,
    max_seconds = 60
  )

You also need to supply either max_tries or max_seconds in order to activate req_retry().

Wrapping it all up

Let’s wrap up everything we’ve learned so far into a single function that creates a request:

req_gist <- function(token) {
  request("https://api.github.com/gists") |>
    req_headers(Authorization = paste("token", token)) |>
    req_error(body = gist_error_body) |>
    req_retry(
      is_transient = gist_is_transient,
      after = gist_after
    )
}

# Check it works:
req_gist(token) |>
  req_perform()

We’ll use this as the basis to solve the next challenge: uploading a gist.

Sending data

To create a gist we need to change the method to POST and add a body that contains data encoded as JSON. httr2 provides one function that does both of these things: req_body_json():

req <- req_gist(token) |>
  req_body_json(list(
    description = "This is my cool gist!",
    files = list(test.R = list(content = "print('Hi!')")),
    public = FALSE
  ))
req |> req_dry_run()

Depending on the API you’re wrapping, you might need to send data in a different way. req_body_form() and req_body_multipart() make it easier to encode data in two other common forms. If the API requires something different you can use req_body_raw().

Typically, the API will return some useful data about the resource you’ve just created. Here I’ll extract the gist ID so we can use it in the next examples, culminating with deleting the gist so I don’t end up with a bunch of duplicated gists 😃.

resp <- req |> req_perform()
id <- resp |> resp_body_json() |> _$id
id

Changing a gist

Actually, that description wasn’t very true and I want to change it. To do so, I need to again send JSON encoded data, but this time I need to use the PATCH verb. So after adding the data to request, I use req_method() to override the default method:

req <- req_gist(token) |>
  req_url_path_append(id) |>
  req_body_json(list(description = "This is a simple gist")) |>
  req_method("PATCH")
req |> req_dry_run()

Deleting a gist

Deleting a gist is similar, except we don’t send any data, we just need to adjust the default method from GET to DELETE.

req <- req_gist(token) |>
  req_url_path_append(id) |>
  req_method("DELETE")
req |> req_dry_run()
req |> req_perform()