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It can be tricky to deal with auth in a non-interactive setting on a remote machine. Specifically, we’re thinking about running tests on a continuous integration (CI) service, such as GitHub Actions, or deploying a data product, such as a Shiny app.

This article documents a token management approach for packages and apps that use gargle, which includes packages like googledrive, googlesheets4, bigrquery, and gmailr. We want it to be relatively easy to have a secret, such as a service account token, that we can:

  • Use locally.
  • Use with CI services, such as GitHub Actions.
  • Use with R-hub.
  • Use in deployed settings, such as Posit Connect.

all while keeping the secret secure.

The approach uses symmetric encryption, where the shared key is stored in an environment variable. Why? This works well with existing conventions for local R usage. Most CI or hosting services offer support for secure environment variables. And R-hub accepts environment variables via the env_vars argument of rhub::check().

This mostly uses functions inlined from the httr2 ( package, which gargle does not (yet) depend on.

Overview of the approach

  1. Generate an encryption key (basically a password) and give it a self-documenting name, e.g. GARGLE_KEY. Store as an environment variable.
  2. Identify a secret file of interest, such as the JSON representing a service account token. This is presumably stored outside your package.
  3. Use the key to apply a method for symmetric encryption to the target file. Store the resulting encrypted file in a designated location within your package.
  4. Store or pass the key as an environment variable everywhere you’ll need to decrypt the secret.
    • Check that the platform has support for keeping the key concealed.
    • Make sure you don’t do anything in your own code that would dump it to a log file, such as printing all environment variables.

Annotated code-through

Choose a name for the encryption key

Pick a name for the encryption key. I recommend that it be clearly associated with whatever package or data product you plan to use it with. For example, gargle’s testing credentials are encrypted with a key named GARGLE_KEY.

You don’t need to store this name as a variable. We’re only doing so because it makes this exposition easier.

key_name <- "SOMETHING_KEY"

Generate the encryption key

In real life, you should keep the output of secret_make_key() to yourself! We reveal it here as part of the exposition.

key <- secret_make_key()
#> [1] "JTyTkH6fwW1xbMINZX6qww"

gargle::secret_make_key() is a copy of httr2::secret_make_key().

Define environment variable in local .Renviron

Combine the key name and value to form a line like this in your user-level .Renviron file:


usethis::edit_r_environ() can help create or open this file. I strongly recommend using the user-level .Renviron, as opposed to project-level, because this makes it less likely you will share sensitive information by mistake. If for some reason you choose to store the key in a file inside a Git repo, you must make sure that file is listed in .gitignore. This still would not prevent leaking your secret if, for example, that project is in a directory that syncs to DropBox or Google Drive (i.e. any service that has no real notion of an “ignore” file).

Remember you’ll need to restart R (or call readRenviron("~/.Renviron")) for the newly defined environment variable to take effect.

In an interactive session, you can call Sys.getenv() to do a quick check that the key is setup correctly locally:

#> [1] "JTyTkH6fwW1xbMINZX6qww"

This Sys.getenv() call is exactly the sort of thing you should be very careful about doing in a deployed setting, where the result could up in a (semi-)public log file.

Encrypt credentials

The Google auth ecosystem involves different types of secrets, which require slightly different handling when you’re placing an encrypted version inside your project.

Encrypt a JSON file

secret_encrypt_json() is a gargle-specific function, built on top of httr2’s secret management machinery. This is because JSON files and strings are especially relevant to auth in the Google ecosystem. You will be interested in secret_encrypt_json() if you want to encrypt a service account key (or, even, an OAuth client).

secret_encrypt_json() takes 3 arguments:

  • json: probably the path to a JSON file, but a JSON string is also acceptable.
  • path: The path to write the encrypted JSON to. Technically this is optional, but this function mostly exists to write to file.
  • key: The name of the environment variable that holds the encryption key.

This example shows how googledrive’s testing credentials are placed inside the package source. googledrive-testing.json is a JSON file downloaded for a service account managed via the Google API / Cloud Platform console:

  json = "~/some/place/where/I/keep/secret/stuff/googledrive-testing.json",
  path = "inst/secret/googledrive-testing.json",

This writes an encrypted version of googledrive-testing.json to inst/secret/googledrive-testing.json relative to the current working directory, which is presumably the top-level directory of googledrive’s source. This encrypted file should be committed and pushed.

Later we show how to use secret_decypt_json() to decrypt this token.

Encrypt an R object

gargle::secret_write_rds() is a copy of httr2::secret_write_rds(), exported by gargle for convenience. If you must encrypt an R object, such as a Gargle2.0 user token, this is the function you need. But note that it should be quite rare to encrypt a user token. If at all possible, use a service account instead.

secret_write_rds() takes 3 arguments:

  • x: The R object to encrypt. In the gargle context, this is usually a token. After a successful OAuth dance, wrapper packages often provide access to the token with a function like googledrive::drive_token(), googlesheets4::gs4_token(), bigrquery::bq_token(), or gmailr::gm_token().
  • path: The path to write the encrypted object to. Technically this is optional, but this function mostly exists to write to file.
  • key: The name of the environment variable that holds the encryption key.

This example shows how an encrypted googlesheets4 user token could be placed inside the .secrets/ directory of a project, e.g. a Shiny app intended for deployment.



# get a token and DO NOT CACHE IT
gs4_auth("", cache = FALSE)

# encrypt the token and write to file

This writes an encrypted version of the token to .secrets/gs4-token.rds. This encrypted file should be committed and pushed/deployed.

Later we show how to use gargle::secret_read_rds() to decrypt this token.

Provide environment variable to other services

Here’s how you make the encryption key available when your code is running elsewhere.

GitHub Actions:

Define the environment variable as an encrypted secret in your repo:

Use the secrets context to expose a secret as an environment variable in your workflows. That will look like like so, in some appropriate place in your workflow file:


The secret, and therefore the associated environment variable, is not available when workflows are triggered via an external pull request.


Send the environment variable in your calls to rhub::check() and friends:

rhub::check(env_vars = Sys.getenv("SOMETHING_KEY", names = TRUE))

Posit Connect

Define the environment variable in the {X} Vars pane of the dashboard for your content:

Using encrypted credentials

It should come as no surprise that secret_encrypt_json() and secret_write_rds() each have a companion function for decryption: secret_decrypt_json() and secret_read_rds(), respectively.

Decrypt a JSON file

Recall that in the example above we encrypted the JSON specifying a service account token, for use in CI by googledrive. Here’s how you would use secret_decrypt_json() to decrypt that token and direct googledrive to use it:


  path = gargle::secret_decrypt_json(
    system.file("secret", "googledrive-testing.json", package = "googledrive"),

Decrypt a user token

Recall that in the example above we encrypted a googlesheets4 user token, for use inside something like a deployed Shiny app. Here’s how you would use secret_read_rds() to decrypt that token and direct googlesheets4 to use it:


gs4_auth(token = gargle::secret_read_rds(

Anticipating decryption failure

The snippets above are great when they work, i.e. when "SOMETHING_KEY" is available for decryption. But what about when the key isn’t available?

You do want to rig things for graceful, informative failure in this case.

  • If you’re using encrypted testing credentials, CRAN is not going to be able to decrypt them. So you want affected tests to be skipped in that case, not to error. Likewise, an external pull request won’t be able to use the testing credentials, so you also want test skipping there.
  • If you’re using encrypted credentials in a Shiny app, you might want to make some provision for when the encryption key is unavailable. The person most likely to benefit from this is you, i.e. when you’re trying to figure out why your app isn’t working. It’s nice to have a clear signal that the encryption key is unavailable instead of some mysterious deployment failure.

Condition on key availability

secret_has_key("SOMETHING_KEY") reports whether the "SOMETHING_KEY" environment variable is defined. In a deployed data product, you might want to call secret_has_key() before any attempt to decrypt a secret. If the encryption key is not available, report that finding and arrange to do something graceful instead of erroring, especially in some cryptic, difficult-to-debug way.

Automatic skips

The secret_* functions have a built-in feature such that, if they are called during testing, when the encryption key is unavailable, that test is skipped. That behaviour is implemented in the internal helper secret_get_key(), which looks something like this:

secret_get_key <- function(envvar) {
  key <- Sys.getenv(envvar)
  if (identical(key, "")) {
    if (is_testing()) {
      msg <- glue("Env var {envvar} not defined.")
    } else {
      # error
  # return the key

If envvar (presumably, SOMETHING_KEY or the like) is undefined, during tests, that test is just skipped. Note that “during tests” is defined as when is_testing() returns TRUE. The is_testing() helper is defined like so:

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

Therefore automatic skipping will happen during automated testing, including on CRAN, and for external contributors. The automatic skips won’t kick in when you’re just, e.g., running a single test “by hand”. The "TESTTHAT" environment variable is set by functions like devtools::test() or testthat::test_file().

I will also point out that this is not how test skipping is achieved in packages like googledrive, googlesheets4, bigrquery, and gmailr. Those packages are all designed to load a token into an internal auth state, then use that token in downstream requests. This means that individual requests or tests won’t ever call secret_decrypt_json() or secret_read_rds(), so the automatic skips aren’t relevant. These packages make different arrangements for skipping auth-requiring tests when the testing credentials are unavailable. The source code for those packages is the best place to learn more. Start by consulting the package’s tests/testthat/helper.R file.

CI configuration

I recommend that you actively check your package under the “no decryption, no token” scenario, so that you discover problems before CRAN or your contributors do. In fact, this should probably be the default situation for your R CMD check workflow.

In auth-requiring package, we usually have two R CMD check workflows:

  • R-CMD-check.yaml is the main workflow, which tests the package against a relatively large matrix of operating systems and R versions. This workflow does not have access to the encryption key.
  • with-auth.yaml is another R CMD check workflow that only checks with the released version of R, on ubuntu-latest. This workflow does have access to the encryption key. Here’s the bit of the .yaml file where that happens:
      - uses: r-lib/actions/check-r-package@v2
          SOMETHING_KEY: ${{ secrets.SOMETHING_KEY }}

Look at the GitHub Actions workflow configurations for googledrive, googlesheets4, bigrquery, and gmailr, to learn see some concrete examples.