tar_target_archive: Declare a target to read an archive.

In tarchives: Make Your 'targets' Pipelines into a Package

Declare a target to read an archive.

Description

Declare a target to read an archive.

Usage

tar_target_archive(
  name,
  package,
  pipeline,
  name_archive = NULL,
  ...,
  pattern = NULL,
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  deps = NULL,
  string = NULL,
  format = targets::tar_option_get("format"),
  repository = targets::tar_option_get("repository"),
  iteration = targets::tar_option_get("iteration"),
  error = targets::tar_option_get("error"),
  memory = targets::tar_option_get("memory"),
  garbage_collection = isTRUE(targets::tar_option_get("garbage_collection")),
  deployment = targets::tar_option_get("deployment"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  storage = targets::tar_option_get("storage"),
  retrieval = targets::tar_option_get("retrieval"),
  cue = targets::tar_option_get("cue"),
  description = targets::tar_option_get("description")
)

tar_target_archive_raw(
  name,
  package,
  pipeline,
  name_archive = name,
  ...,
  pattern = NULL,
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  deps = NULL,
  string = NULL,
  format = targets::tar_option_get("format"),
  repository = targets::tar_option_get("repository"),
  iteration = targets::tar_option_get("iteration"),
  error = targets::tar_option_get("error"),
  memory = targets::tar_option_get("memory"),
  garbage_collection = isTRUE(targets::tar_option_get("garbage_collection")),
  deployment = targets::tar_option_get("deployment"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  storage = targets::tar_option_get("storage"),
  retrieval = targets::tar_option_get("retrieval"),
  cue = targets::tar_option_get("cue"),
  description = targets::tar_option_get("description")
)

Arguments

name	Symbol, name of the target. In tar_target(), name is an unevaluated symbol, e.g. tar_target(name = data). In tar_target_raw(), name is a character string, e.g. tar_target_raw(name = "data"). A target name must be a valid name for a symbol in R, and it must not start with a dot. Subsequent targets can refer to this name symbolically to induce a dependency relationship: e.g. tar_target(downstream_target, f(upstream_target)) is a target named downstream_target which depends on a target upstream_target and a function f(). In most cases, The target name is the name of its local data file in storage. Some file systems are not case sensitive, which means converting a name to a different case may overwrite a different target. Please ensure all target names have unique names when converted to lower case. In addition, a target's name determines its random number generator seed. In this way, each target runs with a reproducible seed so someone else running the same pipeline should get the same results, and no two targets in the same pipeline share the same seed. (Even dynamic branches have different names and thus different seeds.) You can recover the seed of a completed target with tar_meta(your_target, seed) and run tar_seed_set() on the result to locally recreate the target's initial RNG state.
package	A scalar character of the package name.
pipeline	A scalar character of the pipeline name.
name_archive	A scalar character of a name of archived target. If NULL, the name of the target is used. By default, NULL.
...	Arguments to pass to targets::tar_outdated, targets::tar_make or tar_read_archive_raw.
pattern	Code to define a dynamic branching branching for a target. In tar_target(), pattern is an unevaluated expression, e.g. tar_target(pattern = map(data)). In tar_target_raw(), command is an evaluated expression, e.g. tar_target_raw(pattern = quote(map(data))). To demonstrate dynamic branching patterns, suppose we have a pipeline with numeric vector targets x and y. Then, tar_target(z, x + y, pattern = map(x, y)) implicitly defines branches of z that each compute x[1] + y[1], x[2] + y[2], and so on. See the user manual for details.
packages	Character vector of packages to load right before the target runs or the output data is reloaded for downstream targets. Use tar_option_set() to set packages globally for all subsequent targets you define.
library	Character vector of library paths to try when loading packages.
deps	Optional character vector of the adjacent upstream dependencies of the target, including targets and global objects. If NULL, dependencies are resolved automatically as usual. The deps argument is only for developers of extension packages such as tarchetypes, not for end users, and it should almost never be used at all. In scenarios that at first appear to requires deps, there is almost always a simpler and more robust workaround that avoids setting deps.
string	Optional string representation of the command. Internally, the string gets hashed to check if the command changed since last run, which helps targets decide whether the target is up to date. External interfaces can take control of string to ignore changes in certain parts of the command. If NULL, the strings is just deparsed from command (default).
format	Optional storage format for the target's return value. With the exception of format = "file", each target gets a file in ⁠_targets/objects⁠, and each format is a different way to save and load this file. See the "Storage formats" section for a detailed list of possible data storage formats.
repository	Character of length 1, remote repository for target storage. Choices: "local": file system of the local machine. "aws": Amazon Web Services (AWS) S3 bucket. Can be configured with a non-AWS S3 bucket using the endpoint argument of tar_resources_aws(), but versioning capabilities may be lost in doing so. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions. "gcp": Google Cloud Platform storage bucket. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions. A character string from tar_repository_cas() for content-addressable storage. Note: if repository is not "local" and format is "file" then the target should create a single output file. That output file is uploaded to the cloud and tracked for changes where it exists in the cloud. As of targets version 1.11.0 and higher, the local file is no longer deleted after the target runs.
iteration	Character of length 1, name of the iteration mode of the target. Choices: "vector": branching happens with vctrs::vec_slice() and aggregation happens with vctrs::vec_c(). "list", branching happens with ⁠[[]]⁠ and aggregation happens with list(). "group": dplyr::group_by()-like functionality to branch over subsets of a non-dynamic data frame. For iteration = "group", the target must not by dynamic (the pattern argument of tar_target() must be left NULL). The target's return value must be a data frame with a special tar_group column of consecutive integers from 1 through the number of groups. Each integer designates a group, and a branch is created for each collection of rows in a group. See the tar_group() function to see how you can create the special tar_group column with dplyr::group_by().
error	Character of length 1, what to do if the target stops and throws an error. Options: "stop": the whole pipeline stops and throws an error. "continue": the whole pipeline keeps going. "null": The errored target continues and returns NULL. The data hash is deliberately wrong so the target is not up to date for the next run of the pipeline. In addition, as of targets version 1.8.0.9011, a value of NULL is given to upstream dependencies with error = "null" if loading fails. "abridge": any currently running targets keep running, but no new targets launch after that. "trim": all currently running targets stay running. A queued target is allowed to start if: It is not downstream of the error, and It is not a sibling branch from the same tar_target() call (if the error happened in a dynamic branch). The idea is to avoid starting any new work that the immediate error impacts. error = "trim" is just like error = "abridge", but it allows potentially healthy regions of the dependency graph to begin running. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.)
memory	Character of length 1, memory strategy. Possible values: "auto" (default): equivalent to memory = "transient" in almost all cases. But to avoid superfluous reads from disk, memory = "auto" is equivalent to memory = "persistent" for for non-dynamically-branched targets that other targets dynamically branch over. For example: if your pipeline has tar_target(name = y, command = x, pattern = map(x)), then tar_target(name = x, command = f(), memory = "auto") will use persistent memory in order to avoid rereading all of x for every branch of y. "transient": the target gets unloaded after every new target completes. Either way, the target gets automatically loaded into memory whenever another target needs the value. "persistent": the target stays in memory until the end of the pipeline (unless storage is "worker", in which case targets unloads the value from memory right after storing it in order to avoid sending copious data over a network). For cloud-based file targets (e.g. format = "file" with repository = "aws"), the memory option applies to the temporary local copy of the file: "persistent" means it remains until the end of the pipeline and is then deleted, and "transient" means it gets deleted as soon as possible. The former conserves bandwidth, and the latter conserves local storage.
garbage_collection	Logical: TRUE to run base::gc() just before the target runs, in whatever R process it is about to run (which could be a parallel worker). FALSE to omit garbage collection. Numeric values get converted to FALSE. The garbage_collection option in tar_option_set() is independent of the argument of the same name in tar_target().
deployment	Character of length 1. If deployment is "main", then the target will run on the central controlling R process. Otherwise, if deployment is "worker" and you set up the pipeline with distributed/parallel computing, then the target runs on a parallel worker. For more on distributed/parallel computing in targets, please visit https://books.ropensci.org/targets/crew.html.
priority	Deprecated on 2025-04-08 (targets version 1.10.1.9013). targets has moved to a more efficient scheduling algorithm (https://github.com/ropensci/targets/issues/1458) which cannot support priorities. The priority argument of tar_target() no longer has a reliable effect on execution order.
resources	Object returned by tar_resources() with optional settings for high-performance computing functionality, alternative data storage formats, and other optional capabilities of targets. See tar_resources() for details.
storage	Character string to control when the output of the target is saved to storage. Only relevant when using targets with parallel workers (https://books.ropensci.org/targets/crew.html). Must be one of the following values: "worker" (default): the worker saves/uploads the value. "main": the target's return value is sent back to the host machine and saved/uploaded locally. "none": targets makes no attempt to save the result of the target to storage in the location where targets expects it to be. Saving to storage is the responsibility of the user. Use with caution.
retrieval	Character string to control when the current target loads its dependencies into memory before running. (Here, a "dependency" is another target upstream that the current one depends on.) Only relevant when using targets with parallel workers (https://books.ropensci.org/targets/crew.html). Must be one of the following values: "auto" (default): equivalent to retrieval = "worker" in almost all cases. But to avoid unnecessary reads from disk, retrieval = "auto" is equivalent to retrieval = "main" for dynamic branches that branch over non-dynamic targets. For example: if your pipeline has tar_target(x, command = f()), then tar_target(y, command = x, pattern = map(x), retrieval = "auto") will use "main" retrieval in order to avoid rereading all of x for every branch of y. "worker": the worker loads the target's dependencies. "main": the target's dependencies are loaded on the host machine and sent to the worker before the target runs. "none": targets makes no attempt to load its dependencies. With retrieval = "none", loading dependencies is the responsibility of the user. Use with caution.
cue	An optional object from tar_cue() to customize the rules that decide whether the target is up to date.
description	Character of length 1, a custom free-form human-readable text description of the target. Descriptions appear as target labels in functions like tar_manifest() and tar_visnetwork(), and they let you select subsets of targets for the names argument of functions like tar_make(). For example, tar_manifest(names = tar_described_as(starts_with("survival model"))) lists all the targets whose descriptions start with the character string "survival model".

Value

A target object. Users should not modify these directly, just feed them to list() in your target script file (default: ⁠_targets.R⁠).

tarchives documentation built on June 22, 2025, 9:06 a.m.