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Shorthand to include an R Markdown document in a targets pipeline.


  output_file = NULL,
  working_directory = NULL,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  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,



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


Character string, file path to the R Markdown 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.


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.


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".


An option to suppress printing during rendering from knitr, pandoc command line and others. To only suppress printing of the last "Output created: " message, you can set rmarkdown.render.message to FALSE


Named arguments to rmarkdown::render(). These arguments are evaluated when the target actually runs in tar_make(), not when the target is defined. That means, for example, you can use upstream targets as parameters of parameterized R Markdown reports. tar_render(your_target, "your_report.Rmd", params = list(your_param = your_target)) # nolint will run rmarkdown::render("your_report.Rmd", params = list(your_param = your_target)). # nolint For parameterized reports, it is recommended to supply a distinct output_file argument to each tar_render() call and set useful defaults for parameters in the R Markdown source. See the examples section for a demonstration.


A target object with format = "file". When this target runs, it returns a character vector of file paths: the rendered document, the source file, and then the *_files/ directory if it exists. Unlike rmarkdown::render(), 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_render() is an alternative to tar_target() for R Markdown reports that depend on other targets. The R Markdown source should mention dependency targets with tar_load() and tar_read() in the active code chunks (which also allows you to render the report outside the pipeline if the _targets/ data store already exists). (Do not use tar_load_raw() or tar_read_raw() for this.) Then, tar_render() defines a special kind of target. It 1. Finds all the tar_load()/tar_read() dependencies in the report and inserts them into the target's command. This enforces the proper dependency relationships. (Do not use tar_load_raw() or tar_read_raw() for this.) 2. Sets format = "file" (see tar_target()) so targets watches the files at the returned paths and reruns the report if those files change. 3. Configures the target's command to return both the output report files and the input source file. All these file paths are relative paths so the project stays portable. 4. Forces the report to run in the user's current working directory instead of the working directory of the report. 5. Sets convenient default options such as deployment = "main" in the target and quiet = TRUE in rmarkdown::render().

Literate programming limitations

Literate programming files are messy and variable, so functions like tar_render() have limitations: * Child documents are not tracked for changes. * Upstream target dependencies are not detected if tar_read() and/or tar_load() are called from a user-defined function. In addition, single target names must be mentioned and they must be symbols. tar_load("x") and tar_load(contains("x")) may not detect target x. * Special/optional input/output files may not be detected in all cases. * tar_render() and friends are for local files only. They do not integrate with the cloud storage capabilities of targets.

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.


if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({  # tar_dir() runs code from a temporary directory.
# Unparameterized R Markdown:
lines <- c(
  "title: report.Rmd source file",
  "output_format: html_document",
  "Assume these lines are in report.Rmd.",
# Include the report in a pipeline as follows.
    tar_target(data, data.frame(x = seq_len(26), y = letters)),
    tar_render(report, "report.Rmd")
}, ask = FALSE)
# Then, run the targets pipeline as usual.

# Parameterized R Markdown:
lines <- c(
  "title: 'report.Rmd source file with parameters'",
  "output_format: html_document",
  "  your_param: \"default value\"",
  "Assume these lines are in report.Rmd.",
# Include the report in the pipeline as follows.
    tar_target(data, data.frame(x = seq_len(26), y = letters)),
    tar_render(report, "report.Rmd", params = list(your_param = data))
}, ask = FALSE)
# Then, run the targets pipeline as usual.