tar_quarto_rep_raw: Parameterized Quarto with dynamic branching (raw version).

In tarchetypes: Archetypes for Targets

Parameterized Quarto with dynamic branching (raw version).

Description

Targets to render a parameterized Quarto document with multiple sets of parameters (raw version). Same as tar_quarto_rep() except name is a character string, params is an expression object, and extra arguments to quarto::quarto_render() are passed through the args argument instead of ....

Usage

tar_quarto_rep_raw(
  name,
  path,
  execute_params = expression(NULL),
  batches = NULL,
  extra_files = character(0),
  execute = TRUE,
  cache = NULL,
  cache_refresh = FALSE,
  debug = FALSE,
  quiet = TRUE,
  pandoc_args = NULL,
  rep_workers = 1,
  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"),
  memory = targets::tar_option_get("memory"),
  garbage_collection = targets::tar_option_get("garbage_collection"),
  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")
)

Arguments

name	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 set.seed() on the result to locally recreate the target's initial RNG state.
path	Character string, file path to the Quarto source file. Must have length 1.
execute_params	Expression object with code to generate a data frame or tibble with one row per rendered report and one column per Quarto parameter. You may also include an output_file column to specify the path of each rendered report. If included, the output_file column must be a character vector with one and only one output file for each row of parameters. If an output_file column is not included, then the output files are automatically determined using the parameters, and the default file format is determined by the YAML front-matter of the Quarto source document. Only the first file format is used, the others are not generated. Quarto parameters must not be named tar_group or output_file. This execute_params argument is converted into the command for a target that supplies the Quarto parameters.
batches	Number of batches to group the Quarto 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).
extra_files	Character vector of extra files that targets should track for changes. If the content of one of these files changes, then the report will rerun over all the parameters on the next tar_make(). These files are extra files, and they do not include the Quarto source document or rendered output document, which are already tracked for changes. Examples include bibliographies, style sheets, and supporting image files.
execute	Whether to execute embedded code chunks.
cache	Cache execution output (uses knitr cache and jupyter-cache respectively for Rmd and Jupyter input files).
cache_refresh	Force refresh of execution cache.
debug	Leave intermediate files in place after render.
quiet	Suppress warning and other messages.
pandoc_args	Additional command line options to pass to pandoc.
rep_workers	Positive integer of length 1, number of local R processes to use to run reps within batches in parallel. If 1, then reps are run sequentially within each batch. If greater than 1, then reps within batch are run in parallel using a PSOCK cluster.
packages	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.
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 Quarto parameters.
iteration	Character of length 1, iteration argument to tar_target() for the Quarto documents. Does not apply to the target with Quarto 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.) "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.
memory	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.
garbage_collection	Logical, whether to run base::gc() just before the target runs.
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.

Details

tar_quarto_rep_raw() is an alternative to tar_target_raw() for parameterized Quarto 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. (See the execute_params argument for details.)

The Quarto 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_quarto() 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 quarto::quarto_render().

Value

A list of target objects to render the Quarto 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.

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.

Replicate-specific seeds

In ordinary pipelines, each target has its own unique deterministic pseudo-random number generator seed derived from its target name. In batched replicate, however, each batch is a target with multiple replicate within that batch. That is why tar_rep() and friends give each replicate its own unique seed. Each replicate-specific seed is created based on the dynamic parent target name, tar_option_get("seed") (for targets version 0.13.5.9000 and above), batch index, and rep-within-batch index. The seed is set just before the replicate runs. Replicate-specific seeds are invariant to batching structure. In other words, tar_rep(name = x, command = rnorm(1), batches = 100, reps = 1, ...) produces the same numerical output as tar_rep(name = x, command = rnorm(1), batches = 10, reps = 10, ...) (but with different batch names). Other target factories with this seed scheme are tar_rep2(), tar_map_rep(), tar_map2_count(), tar_map2_size(), and tar_render_rep(). For the ⁠tar_map2_*()⁠ functions, it is possible to manually supply your own seeds through the command1 argument and then invoke them in your custom code for command2 (set.seed(), withr::with_seed, or withr::local_seed()). For tar_render_rep(), custom seeds can be supplied to the params argument and then invoked in the individual R Markdown reports. Likewise with tar_quarto_rep() and the execute_params argument.

Quarto troubleshooting

If you encounter difficult errors, please read https://github.com/quarto-dev/quarto-r/issues/16. In addition, please try to reproduce the error using quarto::quarto_render("your_report.qmd", execute_dir = getwd()) without using targets at all. Isolating errors this way makes them much easier to solve.

See Also

Other Literate programming targets: tar_knit_raw(), tar_knit(), tar_quarto_raw(), tar_quarto_rep(), tar_quarto(), tar_render_raw(), tar_render_rep_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 Quarto:
lines <- c(
  "---",
  "title: 'report.qmd source file'",
  "output_format: html_document",
  "params:",
  "  par: \"default value\"",
  "---",
  "Assume these lines are in a file called report.qmd.",
  "```{r}",
  "print(params$par)",
  "```"
)
writeLines(lines, "report.qmd") # In tar_dir(), not the user's file space.
# The following pipeline will run the report for each row of params.
targets::tar_script({
  library(tarchetypes)
  list(
    tar_quarto_rep_raw(
      "report",
      path = "report.qmd",
      execute_params = quote(tibble::tibble(par = c(1, 2)))
    )
  )
}, ask = FALSE)
# Then, run the targets pipeline as usual.
})
}

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