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


  values = NULL,
  names = NULL,
  descriptions = tidyselect::everything(),
  columns = tidyselect::everything(),
  batches = 1,
  reps = 1,
  rep_workers = 1,
  combine = TRUE,
  delimiter = "_",
  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")



Symbol, name of the target. 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 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.


R code for a single replicate. Must return a data frame.


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


Language object with a tidyselect expression to select which columns of values to use to construct statically branched target names. If NULL, then short names are automatically generated.


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().


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


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


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


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.


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


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


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.


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.


Character vector of library paths to try when loading packages.


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.


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.


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.

  • "abridge": any currently running targets keep running, but no new targets launch after that. (Visit to learn how to debug targets using saved workspaces.)

  • "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.


Character of length 1, memory strategy. If "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). If "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"), this memory strategy 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.


Logical, whether to run base::gc() just before the target runs.


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


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()).


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.


Character of length 1, only relevant to tar_make_clustermq() and tar_make_future(). 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": almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language. If you do use it, then the return value of the target is totally ignored when the target ends, but each downstream target still attempts to load the data file (except when retrieval = "none").

    If you select storage = "none", then the return value of the target's command is ignored, and the data is not saved automatically. As with dynamic files (format = "file") it is the responsibility of the user to write to the data store from inside the target.

    The distinguishing feature of storage = "none" (as opposed to format = "file") is that in the general case, downstream targets will automatically try to load the data from the data store as a dependency. As a corollary, storage = "none" is completely unnecessary if format is "file".


Character of length 1, only relevant to tar_make_clustermq() and tar_make_future(). 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 targets dependencies.

  • "none": the dependencies are not loaded at all. This choice is almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language.


An optional object from tar_cue() to customize the rules that decide whether the target is up to date.


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".


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 Please read the walkthrough at to understand the role of target objects in analysis pipelines.

For developers, explains target factories (functions like this one which generate targets) and the design specification at 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 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.


if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
  # 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)
      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)
    command = assess_hyperparameters(sigma1, sigma2),
    values = hyperparameters,
    names = tidyselect::any_of("scenario"),
    batches = 2,
    reps = 3