Delete target output values.

Delete the output values of targets in _targets/objects/ (or the cloud if applicable) but keep the records in _targets/meta/meta.

Usage

tar_delete(names, cloud = TRUE, store = targets::tar_config_get("store"))

Arguments

names

Names of the targets to remove from _targets/objects/. You can supply symbols or tidyselect helpers like any_of() and starts_with().

cloud

Logical of length 1, whether to delete objects from the cloud if applicable (e.g. AWS, GCP). If FALSE, files are not deleted from the cloud.

store

Character of length 1, path to the targets data store. Defaults to tar_config_get("store"), which in turn defaults to _targets/. When you set this argument, the value of tar_config_get("store") is temporarily changed for the current function call. See tar_config_get() and tar_config_set() for details about how to set the data store path persistently for a project.

Details

If you have a small number of data-heavy targets you need to discard to conserve storage, this function can help. Local external files files (i.e. format = "file" and repository = "local") are not deleted. For targets with repository not equal "local", tar_delete() attempts to delete the file and errors out if the deletion is unsuccessful. If deletion fails, either log into the cloud platform and manually delete the file (e.g. the AWS web console in the case of repository = "aws") or call tar_invalidate() on that target so that targets does not try to delete the object. For patterns recorded in the metadata, all the branches will be deleted. For patterns no longer in the metadata, branches are left alone.

If you plan to delete cloud targets, you may need to set the resources argument of tar_option_set() accordingly. If your _targets.R file already sets this option, tar_load_globals() with no arguments is a convenient way to set resources for your interactive R session.

Storage access

Several functions like tar_make(), tar_read(), tar_load(), tar_meta(), and tar_progress() read or modify the local data store of the pipeline. The local data store is in flux while a pipeline is running, and depending on how distributed computing or cloud computing is set up, not all targets can even reach it. So please do not call these functions from inside a target as part of a running pipeline. The only exception is literate programming target factories in the tarchetypes package such as tar_render() and tar_quarto().

Several functions like tar_make(), tar_read(), tar_load(), tar_meta(), and tar_progress() read or modify the local data store of the pipeline. The local data store is in flux while a pipeline is running, and depending on how distributed computing or cloud computing is set up, not all targets can even reach it. So please do not call these functions from inside a target as part of a running pipeline. The only exception is literate programming target factories in the tarchetypes package such as tar_render() and tar_quarto().

See also

Other clean: tar_destroy(), tar_invalidate(), tar_prune_list(), tar_prune()

Examples

if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) { # for CRAN
tar_dir({ # tar_dir() runs code from a temp dir for CRAN.
tar_script({
  list(
    tar_target(y1, 1 + 1),
    tar_target(y2, 1 + 1),
    tar_target(z, y1 + y2)
  )
}, ask = FALSE)
tar_make()
tar_delete(starts_with("y")) # Only deletes y1 and y2.
tar_make() # y1 and y2 rebuild but return same values, so z is up to date.
})
}