Skip to contents

Produce a data frame of information about your targets.

Source: R/tar_manifest.R
tar_manifest.Rd

Along with tar_visnetwork() and tar_glimpse(), tar_manifest() helps check that you constructed your pipeline correctly.

Usage

tar_manifest(
  names = NULL,
  fields = tidyselect::any_of(c("name", "command", "pattern", "description")),
  drop_missing = TRUE,
  callr_function = callr::r,
  callr_arguments = targets::tar_callr_args_default(callr_function),
  envir = parent.frame(),
  script = targets::tar_config_get("script")
)

Arguments

names

Names of the targets to show. Set to NULL to show all the targets (default). Otherwise, the object supplied to names should be a tidyselect expression like any_of() or starts_with() from tidyselect itself, or tar_described_as() to select target names based on their descriptions.

fields

Names of the fields, or columns, to show. Set to NULL to show all the fields (default). Otherwise, the value of fields should be a tidyselect expression like starts_with() to select the columns to show in the output. Possible fields are below. All of them can be set in tar_target(), tar_target_raw(), or tar_option_set().

  • name: Name of the target.

  • command: the R command that runs when the target runs.

  • description: custom free-form text description of the target, if available.

  • pattern: branching pattern of the target, if applicable.

  • format: Storage format.

  • repository: Storage repository.

  • iteration: Iteration mode for branching.

  • error: Error mode, what to do when the target fails.

  • memory: Memory mode, when to keep targets in memory.

  • storage: Storage mode for high-performance computing scenarios.

  • retrieval: Retrieval mode for high-performance computing scenarios.

  • deployment: Where/whether to deploy the target in high-performance computing scenarios.

  • 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: A list of target-specific resource requirements for tar_make_future().

  • cue_mode: Cue mode from tar_cue().

  • cue_depend: Depend cue from tar_cue().

  • cue_expr: Command cue from tar_cue().

  • cue_file: File cue from tar_cue().

  • cue_format: Format cue from tar_cue().

  • cue_repository: Repository cue from tar_cue().

  • cue_iteration: Iteration cue from tar_cue().

  • packages: List columns of packages loaded before running the target.

  • library: List column of library paths to load the packages.

drop_missing

Logical of length 1, whether to automatically omit empty columns and columns with all missing values.

callr_function

A function from callr to start a fresh clean R process to do the work. Set to NULL to run in the current session instead of an external process (but restart your R session just before you do in order to clear debris out of the global environment). callr_function needs to be NULL for interactive debugging, e.g. tar_option_set(debug = "your_target"). However, callr_function should not be NULL for serious reproducible work.

callr_arguments

A list of arguments to callr_function.

envir

An environment, where to run the target R script (default: _targets.R) if callr_function is NULL. Ignored if callr_function is anything other than NULL. callr_function should only be NULL for debugging and testing purposes, not for serious runs of a pipeline, etc.

The envir argument of tar_make() and related functions always overrides the current value of tar_option_get("envir") in the current R session just before running the target script file, so whenever you need to set an alternative envir, you should always set it with tar_option_set() from within the target script file. In other words, if you call tar_option_set(envir = envir1) in an interactive session and then tar_make(envir = envir2, callr_function = NULL), then envir2 will be used.

script

Character of length 1, path to the target script file. Defaults to tar_config_get("script"), which in turn defaults to _targets.R. When you set this argument, the value of tar_config_get("script") is temporarily changed for the current function call. See tar_script(), tar_config_get(), and tar_config_set() for details about the target script file and how to set it persistently for a project.

Value

A data frame of information about the targets in the pipeline. Rows appear in topological order (the order they will run without any influence from parallel computing or priorities).

Storage access

Several functions like tar_make(), tar_read(), tar_load(), tar_meta(), and tar_progress() read or modify the local data store of the pipeline. The local data store is in flux while a pipeline is running, and depending on how distributed computing or cloud computing is set up, not all targets can even reach it. So please do not call these functions from inside a target as part of a running pipeline. The only exception is literate programming target factories in the tarchetypes package such as tar_render() and tar_quarto().

See also

Other inspect: tar_deps(), tar_network(), tar_outdated(), tar_sitrep(), tar_validate()

Examples

if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) { # for CRAN
tar_dir({ # tar_dir() runs code from a temp dir for CRAN.
tar_script({
  library(targets)
  library(tarchetypes)
  tar_option_set()
  list(
    tar_target(y1, 1 + 1),
    tar_target(y2, 1 + 1),
    tar_target(z, y1 + y2),
    tar_target(m, z, pattern = map(z), description = "branching over z"),
    tar_target(c, z, pattern = cross(z))
  )
}, ask = FALSE)
tar_manifest()
tar_manifest(fields = any_of(c("name", "command")))
tar_manifest(fields = any_of("command"))
tar_manifest(fields = starts_with("cue"))
})
}