Parameterized R Markdown with dynamic branching (raw version).

Targets to render a parameterized R Markdown report with multiple sets of parameters (raw version). Same as tar_render_rep() except name is a character string, params is an expression object, and extra arguments to rmarkdown::render() are passed through the args argument instead of ....

tar_render_rep_raw(
  name,
  path,
  params = expression(NULL),
  batches = NULL,
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  format = targets::tar_option_get("format"),
  iteration = targets::tar_option_get("iteration"),
  error = targets::tar_option_get("error"),
  deployment = targets::tar_option_get("deployment"),
  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,
  args = list()
)

Arguments

name	Symbol, name of the target. 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.
path	Character string, file path to the R Markdown source file. Must have length 1.
params	Expression object with code to generate a data frame or tibble with one row per rendered report and one column per R Markdown parameter. You may also include an output_file column to specify the path of each rendered report. R Markdown parameters must not be named tar_group or output_file.
batches	Number of batches to group the R Markdown files. For a large number of reports, increase the number of batches to decrease target-level overhead. Defaults to the number of reports to render (1 report per batch).
packages	Character vector of packages to load right before the target builds. 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, format argument to tar_target() to store the data frame of R Markdown parameters.
iteration	Character of length 1, iteration argument to tar_target() for the R Markdown documents. Does not apply to the target with R Markdown parameters (whose iteration is always "group").
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.)
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.
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.
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
args	Named list of other arguments to rmarkdown::render(). Must not include params or output_file. Evaluated when the target is defined.

Value

A list of target objects to render the R Markdown reports. Changes to the parameters, source file, dependencies, etc. will cause the appropriate targets to rerun during tar_make(). See the "Target objects" section for background.

Details

tar_render_rep_raw() is an alternative to tar_target_raw() for parameterized R Markdown reports that depend on other targets. Parameters must be given as a data frame with one row per rendered report and one column per parameter. An optional output_file column may be included to set the output file path of each rendered report. The R Markdown source should mention other dependency targets 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 and appropriate defaults are specified for the parameters). (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 the output report files: the rendered document, the source file, and then the *_files/ directory if it exists. 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().

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_raw(), tar_knit(), tar_render_raw(), tar_render_rep(), tar_render()

Examples

if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
# Parameterized R Markdown:
lines <- c(
  "---",
  "title: 'report.Rmd source file'",
  "output_format: html_document",
  "params:",
  "  par: \"default value\"",
  "---",
  "Assume these lines are in a file called report.Rmd.",
  "```{r}",
  "print(params$par)",
  "```"
)
# The following pipeline will run the report for each row of params.
targets::tar_script({
  library(tarchetypes)
  list(
    tar_render_rep_raw(
      "report",
      "report.Rmd",
      params = quote(tibble::tibble(par = c(1, 2)))
    )
  )
}, ask = FALSE)
# Then, run the targets pipeline as usual.
})
}