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Set configuration settings.

Source: R/tar_config_set.R
tar_config_set.Rd

tar_config_set() writes special custom settings for the current project to an optional YAML configuration file.

Usage

tar_config_set(
  inherits = NULL,
  garbage_collection = NULL,
  label = NULL,
  level_separation = NULL,
  reporter_make = NULL,
  reporter_outdated = NULL,
  script = NULL,
  seconds_meta_append = NULL,
  seconds_meta_upload = NULL,
  seconds_reporter = NULL,
  seconds_interval = NULL,
  store = NULL,
  shortcut = NULL,
  use_crew = NULL,
  workers = NULL,
  config = Sys.getenv("TAR_CONFIG", "_targets.yaml"),
  project = Sys.getenv("TAR_PROJECT", "main")
)

Arguments

inherits

Character of length 1, name of the project from which the current project should inherit configuration settings. The current project is the project argument, which defaults to Sys.getenv("TAR_PROJECT", "main"). If the inherits argument NULL, the inherits setting is not modified. Use tar_config_unset() to delete a setting.

garbage_collection

Logical of length 1, garbage_collection argument of tar_make() (if crew is enabled), tar_make_clustermq(), and tar_make_future(). Whether to run garbage collection on the main process before sending a target to a worker. For tar_make(), this argument is ignored if tar_option_get("controller") is NULL. Independent from the garbage_collection argument of tar_target(), which controls garbage collection on the worker.

label

Argument of tar_visnetwork() to control node labels.

level_separation

Argument of tar_visnetwork() and tar_glimpse() to control the space between hierarchical levels.

reporter_make

Character of length 1, reporter argument to tar_make() and related functions that run the pipeline. If the argument NULL, the setting is not modified. Use tar_config_unset() to delete a setting.

reporter_outdated

Character of length 1, reporter argument to tar_outdated() and related functions that do not run the pipeline. If the argument NULL, the setting is not modified. Use tar_config_unset() to delete a setting.

script

Character of length 1, path to the target script file that defines the pipeline (_targets.R by default). This path should be either an absolute path or a path relative to the project root where you will call tar_make() and other functions. When tar_make() and friends run the script from the current working directory. If the argument NULL, the setting is not modified. Use tar_config_unset() to delete a setting.

seconds_meta_append

Argument of tar_make(), tar_make_clustermq(), and tar_make_future(). Positive numeric of length 1 with the minimum number of seconds between saves to the local metadata and progress files in the data store. Higher values generally make the pipeline run faster, but unsaved work (in the event of a crash) is not up to date. When the pipeline ends, all the metadata and progress data is saved immediately, regardless of seconds_meta_append.

seconds_meta_upload

Argument of tar_make(), tar_make_clustermq(), and tar_make_future(). Positive numeric of length 1 with the minimum number of seconds between uploads of the metadata and progress data to the cloud (see https://books.ropensci.org/targets/cloud-storage.html). Higher values generally make the pipeline run faster, but unsaved work (in the event of a crash) may not be backed up to the cloud. When the pipeline ends, all the metadata and progress data is uploaded immediately, regardless of seconds_meta_upload.

seconds_reporter

Argument of tar_make(), tar_make_clustermq(), and tar_make_future(). Positive numeric of length 1 with the minimum number of seconds between times when the reporter prints progress messages to the R console.

seconds_interval

Deprecated on 2023-08-24 (version 1.2.2.9001). Use seconds_meta_append, seconds_meta_upload, and seconds_reporter instead.

store

Character of length 1, path to the data store of the pipeline. If NULL, the store setting is left unchanged in the YAML configuration file (default: _targets.yaml). Usually, the data store lives at _targets. Set store to a custom directory to specify a path other than _targets/. The path need not exist before the pipeline begins, and it need not end with "_targets", but it must be writeable. For optimal performance, choose a storage location with fast read/write access. If the argument NULL, the setting is not modified. Use tar_config_unset() to delete a setting.

shortcut

logical of length 1, default shortcut argument to tar_make() and related functions. If the argument NULL, the setting is not modified. Use tar_config_unset() to delete a setting.

use_crew

Logical of length 1, whether to use crew in tar_make() if the controller option is set in tar_option_set() in the target script (_targets.R). See https://books.ropensci.org/targets/crew.html for details.

workers

Positive numeric of length 1, workers argument of tar_make_clustermq() and related functions that run the pipeline with parallel computing among targets. If the argument NULL, the setting is not modified. Use tar_config_unset() to delete a setting.

config

Character of length 1, file path of the YAML configuration file with targets project settings. The config argument specifies which YAML configuration file that tar_config_get() reads from or tar_config_set() writes to in a single function call. It does not globally change which configuration file is used in subsequent function calls. The default file path of the YAML file is always _targets.yaml unless you set another default path using the TAR_CONFIG environment variable, e.g. Sys.setenv(TAR_CONFIG = "custom.yaml"). This also has the effect of temporarily modifying the default arguments to other functions such as tar_make() because the default arguments to those functions are controlled by tar_config_get().

project

Character of length 1, name of the current targets project. Thanks to the config R package, targets YAML configuration files can store multiple sets of configuration settings, with each set corresponding to its own project. The project argument allows you to set or get a configuration setting for a specific project for a given call to tar_config_set() or tar_config_get(). The default project is always called "main" unless you set another default project using the TAR_PROJECT environment variable, e.g. Sys.setenv(tar_project = "custom"). This also has the effect of temporarily modifying the default arguments to other functions such as tar_make() because the default arguments to those functions are controlled by tar_config_get().

Value

NULL (invisibly)

Configuration

For several key functions like tar_make(), the default values of arguments are controlled though tar_config_get(). tar_config_get() retrieves data from an optional YAML configuration file. You can control the settings in the YAML file programmatically with tar_config_set(). The default file path of this YAML file is _targets.yaml, and you can set another path globally using the TAR_CONFIG environment variable. The YAML file can store configuration settings for multiple projects, and you can globally set the default project with the TAR_PROJECT environment variable. The structure of the YAML file follows rules similar to the config R package, e.g. projects can inherit settings from one another using the inherits field. Exceptions include:

  1. There is no requirement to have a configuration named "default".

  2. Other projects do not inherit from the default project` automatically.

  3. Not all fields need values because targets already has defaults.

targets does not actually invoke the config package. The implementation in targets was written from scratch without viewing or copying any part of the source code of config.

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 configuration: tar_config_get(), tar_config_unset(), tar_envvars(), tar_option_get(), tar_option_reset(), tar_option_set()

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(x, 1 + 1)))
tar_config_get("store") # NULL (data store defaults to "_targets/")
store_path <- tempfile()
tar_config_set(store = store_path)
tar_config_get("store") # Shows a temp file.
tar_make() # Writes to the custom data store identified in _targets.yaml.
tar_read(x) # tar_read() knows about _targets.yaml too.
file.exists("_targets") # FALSE
file.exists(store_path) # TRUE
})
}