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Remove target values from _targets/objects/ and the cloud and remove target metadata from _targets/meta/meta for targets that are no longer part of the pipeline.


  cloud = TRUE,
  batch_size = 1000L,
  verbose = TRUE,
  callr_function = callr::r,
  callr_arguments = targets::tar_callr_args_default(callr_function),
  envir = parent.frame(),
  script = targets::tar_config_get("script"),
  store = targets::tar_config_get("store")



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.


Positive integer between 1 and 1000, number of target objects to delete from the cloud with each HTTP API request. Currently only supported for AWS. Cannot be more than 1000.


Logical of length 1, whether to print console messages to show progress when deleting each batch of targets from each cloud bucket. Batched deletion with verbosity is currently only supported for AWS.


A function from callr to start a fresh clean R process to do the work. Set to NULL to run in the current session instead of an external process (but restart your R session just before you do in order to clear debris out of the global environment). callr_function needs to be NULL for interactive debugging, e.g. tar_option_set(debug = "your_target"). However, callr_function should not be NULL for serious reproducible work.


A list of arguments to callr_function.


An environment, where to run the target R script (default: _targets.R) if callr_function is NULL. Ignored if callr_function is anything other than NULL. callr_function should only be NULL for debugging and testing purposes, not for serious runs of a pipeline, etc.

The envir argument of tar_make() and related functions always overrides the current value of tar_option_get("envir") in the current R session just before running the target script file, so whenever you need to set an alternative envir, you should always set it with tar_option_set() from within the target script file. In other words, if you call tar_option_set(envir = envir1) in an interactive session and then tar_make(envir = envir2, callr_function = NULL), then envir2 will be used.


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


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.


NULL except if callr_function is callr::r_bg, in which case a handle to the callr background process is returned. Either way, the value is invisibly returned.


tar_prune() is useful if you recently worked through multiple changes to your project and are now trying to discard irrelevant data while keeping the results that still matter. Global objects and local files with format = "file" outside the data store are unaffected. Also removes _targets/scratch/, which is only needed while tar_make(), tar_make_clustermq(), or tar_make_future() is running. To list the targets that will be pruned without actually removing anything, use tar_prune_list().

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().

Cloud target data versioning

Some buckets in Amazon S3 or Google Cloud Storage are "versioned", which means they track historical versions of each data object. If you use targets with cloud storage ( and versioning is turned on, then targets will record each version of each target in its metadata.

Functions like tar_read() and tar_load() load the version recorded in the local metadata, which may not be the same as the "current" version of the object in the bucket. Likewise, functions tar_delete() and tar_destroy() only remove the version ID of each target as recorded in the local metadata.

If you want to interact with the latest version of an object instead of the version ID recorded in the local metadata, then you will need to delete the object from the metadata.

  1. Make sure your local copy of the metadata is current and up to date. You may need to run tar_meta_download() or tar_meta_sync() first.

  2. Run tar_unversion() to remove the recorded version IDs of your targets in the local metadata.

  3. With the version IDs gone from the local metadata, functions like tar_read() and tar_destroy() will use the latest version of each target data object.

  4. Optional: to back up the local metadata file with the version IDs deleted, use tar_meta_upload().

See also


Other clean: tar_delete(), tar_destroy(), tar_invalidate(), tar_prune_list(), tar_unversion()


if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) { # for CRAN
tar_dir({ # tar_dir() runs code from a temp dir for CRAN.
    tar_target(y1, 1 + 1),
    tar_target(y2, 1 + 1),
    tar_target(z, y1 + y2)
}, ask = FALSE)
# Remove some targets from the pipeline.
tar_script(list(tar_target(y1, 1 + 1)), ask = FALSE)
# Keep only the remaining targets in the data store.