tar_render_rep: Parameterized R Markdown with dynamic branching.

View source: R/tar_render_rep.R

tar_render_repR Documentation

Parameterized R Markdown with dynamic branching.


Targets to render a parameterized R Markdown report with multiple sets of parameters.


  params = data.frame(),
  batches = 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"),
  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 set.seed() 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.


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. This params argument is converted into the command for a target that supplies the R Markdown parameters.


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


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.


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.


Character vector of library paths to try when loading packages.


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.


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().


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.


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, 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.


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()).


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 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.


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


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


Other named arguments to rmarkdown::render(). Unlike tar_render(), these arguments are evaluated when the target is defined, not when it is run. (The only reason to delay evaluation in tar_render() was to handle R Markdown parameters, and tar_render_rep() handles them differently.)


tar_render_rep() is an alternative to tar_target() 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().


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.

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 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.

See Also

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


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 file'",
  "output_format: html_document",
  "  par: \"default value\"",
  "Assume these lines are in a file called report.Rmd.",
# The following pipeline will run the report for each row of params.
      params = 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.