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Dynamic batched replication within static branches for data frames.

Source: R/tar_map_rep.R, R/tar_map_rep_raw.R
tar_map_rep.Rd

Define targets for batched replication within static branches for data frames.

tar_map_rep() expects an unevaluated symbol for the name argument and an unevaluated expression for command, whereas tar_map_rep_raw() expects a character string for name and an evaluated expression object for command.

Usage

tar_map_rep(
  name,
  command,
  values = NULL,
  names = NULL,
  descriptions = tidyselect::everything(),
  columns = tidyselect::everything(),
  batches = 1,
  reps = 1,
  rep_workers = 1,
  combine = TRUE,
  delimiter = "_",
  unlist = FALSE,
  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"),
  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_map_rep_raw(
  name,
  command,
  values = NULL,
  names = NULL,
  descriptions = quote(tidyselect::everything()),
  columns = quote(tidyselect::everything()),
  batches = 1,
  reps = 1,
  rep_workers = 1,
  combine = TRUE,
  delimiter = "_",
  unlist = FALSE,
  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"),
  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_map_rep() expects an unevaluated symbol for the name argument, whereas tar_map_rep_raw() expects a character string for name.

command

R code for a single replicate. Must return a data frame when run. tar_map_rep() expects an unevaluated expression for command, whereas tar_map_rep_raw() expects an evaluated expression object for command.

values

Named list or data frame with values to iterate over. The names are the names of symbols in the commands and pattern statements, and the elements are values that get substituted in place of those symbols. tar_map() uses these elements to create new R code, so they should be basic types, symbols, or R expressions. For objects even a little bit complicated, especially objects with attributes, it is not obvious how to convert the object into code that generates it. For complicated objects, consider using quote() when you define values, as shown at https://github.com/ropensci/tarchetypes/discussions/105.

names

Subset of names(values) used to generate the suffixes in the names of the new targets. The value of names should be a tidyselect expression such as a call to any_of() or starts_with().

descriptions

Names of a column in values to append to the custom description of each generated target. The value of descriptions should be a tidyselect expression such as a call to any_of() or starts_with().

columns

A tidyselect expression to select which columns of values to append to the output. Columns already in the target output are not appended.

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.

combine

Logical of length 1, whether to statically combine all the results into a single target downstream.

delimiter

Character of length 1, string to insert between other strings when creating names of targets.

unlist

Logical, whether to flatten the returned list of targets. If unlist = FALSE, the list is nested and sub-lists are named and grouped by the original input targets. If unlist = TRUE, the return value is a flat list of targets named by the new target names.

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. The local file is deleted after the target runs.

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 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": new in targets version 1.8.0.9011, memory = "auto" is equivalent to memory = "transient" for dynamic branching (a non-null pattern argument) and memory = "persistent" for targets that do not use dynamic branching.

  • "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).

  • "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.

For cloud-based dynamic files (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, FALSE to omit garbage collection. In the case of high-performance computing, gc() runs both locally and on the parallel worker. All this garbage collection is skipped if the actual target is skipped in the pipeline. Non-logical values of garbage_collection are converted to TRUE or FALSE using isTRUE(). In other words, non-logical values are converted FALSE. For example, garbage_collection = 2 is equivalent to garbage_collection = FALSE.

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

Numeric of length 1 between 0 and 1. Controls which targets get deployed first when multiple competing targets are ready simultaneously. Targets with priorities closer to 1 get dispatched earlier (and polled earlier in tar_make_future()).

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:

  • "main": the target's return value is sent back to the host machine and saved/uploaded locally.

  • "worker": the worker saves/uploads the value.

  • "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:

  • "main": the target's dependencies are loaded on the host machine and sent to the worker before the target runs.

  • "worker": the worker loads the target's dependencies.

  • "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 new target objects. See the "Target objects" section for background.

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.

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.

See also

Other branching: tar_map2(), tar_map2_count(), tar_map2_size(), tar_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({
  library(tarchetypes)
  # Just a sketch of a Bayesian sensitivity analysis of hyperparameters:
  assess_hyperparameters <- function(sigma1, sigma2) {
    # data <- simulate_random_data() # user-defined function
    # run_model(data, sigma1, sigma2) # user-defined function
    # Mock output from the model:
    posterior_samples <- stats::rnorm(1000, 0, sigma1 + sigma2)
    tibble::tibble(
      posterior_median = median(posterior_samples),
      posterior_quantile_0.025 = quantile(posterior_samples, 0.025),
      posterior_quantile_0.975 = quantile(posterior_samples, 0.975)
    )
  }
  hyperparameters <- tibble::tibble(
    scenario = c("tight", "medium", "diffuse"),
    sigma1 = c(10, 50, 50),
    sigma2 = c(10, 5, 10)
  )
  list(
    tar_map_rep(
      name = sensitivity_analysis,
      command = assess_hyperparameters(sigma1, sigma2),
      values = hyperparameters,
      names = tidyselect::any_of("scenario"),
      batches = 2,
      reps = 3
    ),
    tar_map_rep_raw(
      name = "sensitivity_analysis2",
      command = quote(assess_hyperparameters(sigma1, sigma2)),
      values = hyperparameters,
      names = tidyselect::any_of("scenario"),
      batches = 2,
      reps = 3
    )
  )
})
targets::tar_make()
targets::tar_read(sensitivity_analysis)
})
}