tar_files_input: Dynamic branching over input files or URLs

View source: R/tar_files_input.R

tar_files_inputR Documentation

Dynamic branching over input files or URLs


Dynamic branching over input files or URLs.


  batches = length(files),
  format = c("file", "url", "aws_file"),
  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"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  cue = targets::tar_option_get("cue")



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 set.seed() on the result to locally recreate the target's initial RNG state.


Nonempty character vector of known existing input files to track for changes.


Positive integer of length 1, number of batches to partition the files. The default is one file per batch (maximum number of batches) which is simplest to handle but could cause a lot of overhead and consume a lot of computing resources. Consider reducing the number of batches below the number of files for heavy workloads.


Character, either "file" or "url". See the format argument of targets::tar_target() for details.


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, iteration method. Must be a method supported by the iteration argument of targets::tar_target(). The iteration method for the upstream target is always "list" in order to support batching.


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.


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.


An optional object from tar_cue() to customize the rules that decide whether the target is up to date. Only applies to the downstream target. The upstream target always runs.


tar_files_input() is like tar_files() but more convenient when the files in question already exist and are known in advance. Whereas tar_files() always appears outdated (e.g. with tar_outdated()) because it always needs to check which files it needs to branch over, tar_files_input() will appear up to date if the files have not changed since last tar_make(). In addition, tar_files_input() automatically groups input files into batches to reduce overhead and increase the efficiency of parallel processing.

tar_files_input() creates a pair of targets, one upstream and one downstream. The upstream target does some work and returns some file paths, and the downstream target is a pattern that applies format = "file" or format = "url". This is the correct way to dynamically iterate over file/url targets. It makes sure any downstream patterns only rerun some of their branches if the files/urls change. For more information, visit https://github.com/ropensci/targets/issues/136 and https://github.com/ropensci/drake/issues/1302.


A list of two targets, one upstream and one downstream. The upstream one does some work and returns some file paths, and the downstream target is a pattern that applies format = "file" or format = "url". 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 Dynamic branching over files: tar_files_input_raw(), tar_files_raw(), tar_files()


if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
  # Do not use temp files in real projects
  # or else your targets will always rerun.
  paths <- unlist(replicate(4, tempfile()))
      batches = 2
targets::tar_read(x, branches = 1)

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