tar_combine: Static aggregation.

View source: R/tar_combine.R

tar_combineR Documentation

Static aggregation.


Aggregate the results of upstream targets into a new target.


  command = vctrs::vec_c(!!!.x),
  use_names = TRUE,
  pattern = NULL,
  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")



Symbol, name of the new target.


One or more target objects or list of target objects. Lists can be arbitrarily nested, as in list().


R command to aggregate the targets. Must contain !!!.x where the arguments are to be inserted, where !!! is the unquote splice operator from rlang.


Logical, whether to insert the names of the targets into the command when splicing.


Language to define branching for a target. For example, in a pipeline with numeric vector targets x and y, 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.


Character vector of packages to load right before the target builds 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:

  • "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.

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, 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 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 to see how you can create the special tar_group column with dplyr::group_by().


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 https://books.ropensci.org/targets/debugging.html 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, only relevant to tar_make_clustermq() and tar_make_future(). If "worker", the target builds on a parallel worker. If "main", the target builds on the host machine / process managing the pipeline.


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 built 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 builds.

  • "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.


A new target object to combine the return values from the upstream targets. 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.

See Also

Other branching: tar_combine_raw(), tar_map2_count_raw(), tar_map2_count(), tar_map2_raw(), tar_map2_size_raw(), tar_map2_size(), tar_map2(), tar_map_rep_raw(), tar_map_rep(), tar_map(), tar_rep2_raw(), tar_rep2(), tar_rep_map_raw(), tar_rep_map(), tar_rep_raw(), tar_rep()


if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
  target1 <- targets::tar_target(x, head(mtcars))
  target2 <- targets::tar_target(y, tail(mtcars))
  target3 <- tarchetypes::tar_combine(
    command = bind_rows(!!!.x)
  list(target1, target2, target3)

tarchetypes documentation built on Dec. 1, 2022, 1:31 a.m.