tar_files: Dynamic branching over output or input files.

In tarchetypes: Archetypes for Targets

Dynamic branching over output or input files.

Description

Dynamic branching over output or input files.

Usage

tar_files(
  name,
  command,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  format = c("file", "file_fast", "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"),
  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")
)

Arguments

name	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.
command	R code to run the target.
tidy_eval	Logical, whether to enable tidy evaluation when interpreting command and pattern. If TRUE, you can use the "bang-bang" operator ⁠!!⁠ to programmatically insert the values of global objects.
packages	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.
library	Character vector of library paths to try when loading packages.
format	Character of length 1. Must be "file", "url", or "aws_file". See the format argument of targets::tar_target() for details.
repository	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.
iteration	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().
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. "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.
memory	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.
garbage_collection	Logical, whether to run base::gc() just before the target runs.
deployment	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.
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 built 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 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".
retrieval	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.
cue	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.

Details

tar_files() 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", format = "file_fast", or format = "url". (URLs are input-only, they must already exist beforehand.) 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.

Value

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", format = "file_fast", 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_input(), tar_files_raw()

Examples

if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
  # Do not use temp files in real projects
  # or else your targets will always rerun.
  paths <- unlist(replicate(2, tempfile()))
  file.create(paths)
  list(
    tarchetypes::tar_files(x, paths)
  )
})
targets::tar_make()
targets::tar_read(x)
})
}

tarchetypes documentation built on Oct. 4, 2023, 5:08 p.m.