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Batched replication with dynamic branching.

Source: R/tar_rep.R, R/tar_rep_raw.R
tar_rep.Rd

Batching is important for optimizing the efficiency of heavily dynamically-branched workflows: https://books.ropensci.org/targets/dynamic.html#batching. tar_rep() replicates a command in strategically sized batches.

tar_rep() expects unevaluated name and command arguments (e.g. tar_rep(name = sim, command = simulate())) whereas tar_rep_raw() expects an evaluated string for name and an evaluated expression object for command (e.g. tar_rep_raw(name = "sim", command = quote(simulate()))).

Usage

tar_rep(
  name,
  command,
  batches = 1,
  reps = 1,
  rep_workers = 1,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  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 = 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_rep_raw(
  name,
  command,
  batches = 1,
  reps = 1,
  rep_workers = 1,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  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 = 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

Name of the target. tar_rep() expects unevaluated name and command arguments (e.g. tar_rep(name = sim, command = simulate())) whereas tar_rep_raw() expects an evaluated string for name and an evaluated expression object for command (e.g. tar_rep_raw(name = "sim", command = quote(simulate()))).

command

R code to run multiple times. Must return a list or data frame because tar_rep() will try to append new elements/columns tar_batch and tar_rep to the output to denote the batch and rep-within-batch IDs, respectively.

tar_rep() expects unevaluated name and command arguments (e.g. tar_rep(name = sim, command = simulate())) whereas tar_rep_raw() expects an evaluated string for name and an evaluated expression object for command (e.g. tar_rep_raw(name = "sim", command = quote(simulate()))).

batches

Number of batches. This is also the number of dynamic branches created during tar_make().

reps

Number of replications in each batch. The total number of replications is batches * reps.

rep_workers

Positive integer of length 1, number of local R processes to use to run reps within batches in parallel. If 1, then reps are run sequentially within each batch. If greater than 1, then reps within batch are run in parallel using a PSOCK cluster.

tidy_eval

Whether to invoke tidy evaluation (e.g. the !! operator from rlang) as soon as the target is defined (before tar_make()). Applies to the command argument.

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.

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:

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 vectors::vec_slice() and aggregation happens with vctrs::vec_c().

  • "list", branching happens with [[]] and aggregation happens with list(). In the case of list iteration, tar_read(your_target) will return a list of lists, where the outer list has one element per batch and each inner list has one element per rep within batch. To un-batch this nested list, call tar_read(your_target, recursive = FALSE).

  • "group": dplyr::group_by()-like functionality to branch over subsets of a data frame. 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 in targets 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:

    1. It is not downstream of the error, and

    2. 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 for x 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 list of two targets, one upstream and one downstream. The upstream target returns a numeric index of batch ids, and the downstream one dynamically maps over the batch ids to run the command multiple times. If the command returns a list or data frame, then the targets from tar_rep() will try to append new elements/columns tar_batch, tar_rep, and tar_seed to the output to denote the batch, rep-within-batch ID, and random number generator seed, respectively.

tar_read(your_target) (on the downstream target with the actual work) will return a list of lists, where the outer list has one element per batch and each inner list has one element per rep within batch. To un-batch this nested list, call tar_read(your_target, recursive = FALSE).

Details

tar_rep() and tar_rep_raw() each create two targets: an upstream local stem with an integer vector of batch ids, and a downstream pattern that maps over the batch ids. (Thus, each batch is a branch.) Each batch/branch replicates the command a certain number of times. If the command returns a list or data frame, then the targets from tar_rep() will try to append new elements/columns tar_batch, tar_rep, and tar_seed to the output to denote the batch, rep-within-batch index, and rep-specific seed, respectively.

Both batches and reps within each batch are aggregated according to the method you specify in the iteration argument. If "list", reps and batches are aggregated with list(). If "vector", then vctrs::vec_c(). If "group", then vctrs::vec_rbind().

Replicate-specific seeds

In ordinary pipelines, each target has its own unique deterministic pseudo-random number generator seed derived from its target name. In batched replicate, however, each batch is a target with multiple replicate within that batch. That is why tar_rep() and friends give each replicate its own unique seed. Each replicate-specific seed is created based on the dynamic parent target name, tar_option_get("seed") (for targets version 0.13.5.9000 and above), batch index, and rep-within-batch index. The seed is set just before the replicate runs. Replicate-specific seeds are invariant to batching structure. In other words, tar_rep(name = x, command = rnorm(1), batches = 100, reps = 1, ...) produces the same numerical output as tar_rep(name = x, command = rnorm(1), batches = 10, reps = 10, ...) (but with different batch names). Other target factories with this seed scheme are tar_rep2(), tar_map_rep(), tar_map2_count(), tar_map2_size(), and tar_render_rep(). For the tar_map2_*() functions, it is possible to manually supply your own seeds through the command1 argument and then invoke them in your custom code for command2 (set.seed(), withr::with_seed, or withr::local_seed()). For tar_render_rep(), custom seeds can be supplied to the params argument and then invoked in the individual R Markdown reports. Likewise with tar_quarto_rep() and the execute_params argument.

Target objects

Most tarchetypes functions are target factories, which means they return target objects or lists of target objects. Target objects represent skippable steps of the analysis pipeline as described at https://books.ropensci.org/targets/. Please read the walkthrough at https://books.ropensci.org/targets/walkthrough.html to understand the role of target objects in analysis pipelines.

For developers, https://wlandau.github.io/targetopia/contributing.html#target-factories explains target factories (functions like this one which generate targets) and the design specification at https://books.ropensci.org/targets-design/ details the structure and composition of target objects.

See also

Other branching: tar_map2(), tar_map2_count(), tar_map2_size(), tar_map_rep(), tar_rep2(), tar_rep_map(), tar_rep_map_raw()

Examples

if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
  list(
    tarchetypes::tar_rep(
      x,
      data.frame(x = sample.int(1e4, 2)),
      batches = 2,
      reps = 3
    )
  )
})
targets::tar_make()
targets::tar_read(x)
targets::tar_script({
  list(
    tarchetypes::tar_rep_raw(
      "x",
      quote(data.frame(x = sample.int(1e4, 2))),
      batches = 2,
      reps = 3
    )
  )
})
targets::tar_make()
targets::tar_read(x)
})
}