tar_quarto_rep {tarchetypes}

Parameterized Quarto with dynamic branching.

Description

Targets to render a parameterized Quarto document with multiple sets of parameters.

Usage

tar_quarto_rep(
  name,
  path,
  working_directory = NULL,
  execute_params = data.frame(),
  batches = NULL,
  extra_files = character(0),
  execute = TRUE,
  cache = NULL,
  cache_refresh = FALSE,
  debug = FALSE,
  quiet = TRUE,
  quarto_args = NULL,
  pandoc_args = NULL,
  rep_workers = 1,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  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"),
  description = targets::tar_option_get("description")
)

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 tar_seed_set() 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.

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.

execute_params

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. This is also the number of dynamic branches created during tar_make().

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.

quarto_args

Character vector of other quarto CLI flag pass to the command. This is mainly for advanced usage, e.g it can be useful for new options added to quarto CLI and not yet supported as function argument.

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.

tidy_eval

Logical of length 1, whether to use tidy evaluation to resolve execute_params. Similar to the tidy_eval argument of targets::tar_target().

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.

format

Optional storage format for the target's return value. With the exception of format = "file", each target gets a file in ⁠_targets/objects⁠, and each format is a different way to save and load this file. See the "Storage formats" section for a detailed list of possible data storage formats.

iteration

Character of length 1, name of the iteration mode of the target. Choices:

• "vector": branching happens with vectors::vec_slice() and aggregation happens with vctrs::vec_c().

• "list", branching happens with ⁠[[]]⁠ and aggregation happens with list(). In the case of list iteration, tar_read(your_target) will return a list of lists, where the outer list has one element per batch and each inner list has one element per rep within batch. To un-batch this nested list, call tar_read(your_target, recursive = FALSE).

• "group": dplyr::group_by()-like functionality to branch over subsets of a data frame. The target's return value must be a data frame with a special tar_group column of consecutive integers from 1 through the number of groups. Each integer designates a group, and a branch is created for each collection of rows in a group. See the tar_group() function to see how you can create the special tar_group column with dplyr::group_by().

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. 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".

Details

tar_quarto_rep() is an alternative to tar_target() for a parameterized Quarto document that depends 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 file paths mentioned in files. 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.

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.

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(), tar_knit_raw(), tar_quarto(), tar_quarto_raw(), tar_quarto_rep_raw(), tar_render(), tar_render_raw(), 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.
# Parameterized Quarto:
lines <- c(
  "---",
  "title: 'report.qmd 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(
      report,
      path = "report.qmd",
      execute_params = tibble::tibble(par = c(1, 2))
    )
  )
}, ask = FALSE)
# Then, run the targets pipeline as usual.
})
}