tar_knit_raw: Target with a knitr document (raw version).

View source: R/tar_knit_raw.R

tar_knit_rawR Documentation

Target with a knitr document (raw version).


Shorthand to include a knitr document in a targets pipeline (raw version)


  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  error = targets::tar_option_get("error"),
  memory = targets::tar_option_get("memory"),
  garbage_collection = targets::tar_option_get("garbage_collection"),
  deployment = "main",
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  retrieval = targets::tar_option_get("retrieval"),
  cue = targets::tar_option_get("cue"),
  quiet = TRUE,
  knit_arguments = quote(list())



Character of length 1, name of the target.


Character string, file path to the knitr source file. Must have length 1.


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.


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 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.


Boolean; suppress the progress bar and messages?


Optional language object with a list of named arguments to knitr::knit(). Cannot be an expression object. (Use quote(), not expression().) The reason for quoting is that these arguments may depend on upstream targets whose values are not available at the time the target is defined, and because tar_knit_raw() is the "raw" version of a function, we want to avoid all non-standard evaluation.


tar_knit_raw() is just like tar_knit() except that it uses standard evaluation. The name argument is a character vector, and the knit_arguments argument is a language object.


A tar_target() object with format = "file". When this target runs, it returns a character vector of file paths. The first file paths are the output files (returned by knitr::knit()) and the knitr source file is last. But unlike knitr::knit(), all returned paths are relative paths to ensure portability (so that the project can be moved from one file system to another without invalidating the target). 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 Literate programming targets: tar_knit(), tar_quarto_raw(), tar_quarto_rep_raw(), tar_quarto_rep(), tar_quarto(), tar_render_raw(), tar_render_rep_raw(), tar_render_rep(), tar_render()


if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
  # Ordinarily, you should create the report outside
  # tar_script() and avoid temporary files.
  lines <- c(
    "title: report",
    "output_format: html_document",
  path <- tempfile()
  writeLines(lines, path)
    targets::tar_target(data, data.frame(x = seq_len(26), y = letters)),
    tarchetypes::tar_knit_raw("report", path)

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