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

  params = data.frame(),
  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,



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.


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.


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).


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.


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 vctrs::vec_slice() and aggregation happens with vctrs::vec_c().

  • "list", branching happens with [[]] and aggregation happens with list().

  • "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 runs into an error. If "stop", the whole pipeline stops and throws an error. If "continue", the error is recorded, but the pipeline keeps going. error = "workspace" is just like error = "stop" except targets saves a special workspace file to support interactive debugging outside the pipeline. (Visit to learn how to debug targets using saved workspaces.)


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(). If "main", the target's dependencies are loaded on the host machine and sent to the worker before the target builds. If "worker", the worker loads the targets dependencies.


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


An option to suppress printing of the pandoc command line.


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.)


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.


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().

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 Please read the walkthrough at to understand the role of target objects in analysis pipelines.

For developers, explains target factories (functions like this one which generate targets) and the design specification at 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_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", "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( report, "report.Rmd", params = tibble::tibble(par = c(1, 2)) ) ) }, ask = FALSE) # Then, run the targets pipeline as usual. }) }