tar_render_raw {tarchetypes}

Target with an R Markdown document (raw version).

Description

Shorthand to include an R Markdown document in a targets pipeline (raw version)

Usage

tar_render_raw(
  name,
  path,
  output_file = NULL,
  working_directory = NULL,
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  error = targets::tar_option_get("error"),
  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,
  render_arguments = quote(list())
)

Arguments

name

Character of length 1, name of the target.

path

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

output_file

Character string, file path to the rendered output file.

working_directory

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.

packages

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.

library

Character vector of library paths to try when loading packages.

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.

deployment

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 https://books.ropensci.org/targets/crew.html.

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 dispatched 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.

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 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.

cue

An optional object from tar_cue() to customize the rules that decide whether the target is up to date.

description

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".

quiet

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

render_arguments

Optional language object with a list of named arguments to rmarkdown::render(). 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_render_raw() is the "raw" version of a function, we want to avoid all non-standard evaluation.

Details

tar_render_raw() is just like tar_render() except that it uses standard evaluation. The name argument is a character vector, and the render_arguments argument is a language object.

Value

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.

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.

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.

See Also

Other Literate programming targets: tar_knit(), tar_knit_raw(), tar_quarto(), tar_quarto_raw(), tar_quarto_rep(), tar_quarto_rep_raw(), tar_render(), tar_render_rep(), tar_render_rep_raw()

Examples

if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
# Unparameterized R Markdown report:
lines <- c(
  "---",
  "title: 'report.Rmd source file'",
  "output_format: html_document",
  "---",
  "Assume these lines are in report.Rmd.",
  "```{r}",
  "targets::tar_read(data)",
  "```"
)
# Include the report in the pipeline as follows:
targets::tar_script({
  library(tarchetypes)
  list(
    tar_target(data, data.frame(x = seq_len(26), y = letters)),
    tar_render_raw("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",
  "params:",
  "  your_param: \"default value\"",
  "---",
  "Assume these lines are in report.Rmd.",
  "```{r}",
  "print(params$your_param)",
  "```"
)
# Include this parameterized report in the pipeline as follows.
targets::tar_script({
  library(tarchetypes)
  list(
    tar_target(data, data.frame(x = seq_len(26), y = letters)),
    tar_render_raw(
      "report",
      "report.Rmd",
      render_arguments = quote(list(params = list(your_param = data)))
    )
  )
}, ask = FALSE)
# Then, run the targets pipeline as usual.
})
}