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


  output_file = NULL,
  working_directory = NULL,
  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"),
  description = targets::tar_option_get("description"),
  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 string, file path to the rendered output file.


Optional character string, path to the working directory to temporarily set when running the report. The default is NULL, which runs the report from the current working directory at the time the pipeline is run. This default is recommended in the vast majority of cases. To use anything other than NULL, you must manually set the value of the store argument relative to the working directory in all calls to tar_read() and tar_load() in the report. Otherwise, these functions will not know where to find the data.


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.


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 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".


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.


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.


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.

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.

See also


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)