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Analyze the pipeline defined in the target script file (default: _targets.R) and return the vertices and edges of the directed acyclic graph of dependency relationships.


  targets_only = FALSE,
  names = NULL,
  shortcut = FALSE,
  allow = NULL,
  exclude = NULL,
  outdated = TRUE,
  reporter = targets::tar_config_get("reporter_outdated"),
  seconds_reporter = targets::tar_config_get("seconds_reporter"),
  callr_function = callr::r,
  callr_arguments = targets::tar_callr_args_default(callr_function, reporter),
  envir = parent.frame(),
  script = targets::tar_config_get("script"),
  store = targets::tar_config_get("store")



Logical, whether to restrict the output to just targets (FALSE) or to also include imported global functions and objects.


Names of targets. The graph visualization will operate only on these targets (and unless shortcut is TRUE, all the targets upstream as well). Selecting a small subgraph using names could speed up the load time of the visualization. Unlike allow, names is invoked before the graph is generated. Set to NULL to check/run 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.


Logical of length 1, how to interpret the names argument. If shortcut is FALSE (default) then the function checks all targets upstream of names as far back as the dependency graph goes. If TRUE, then the function only checks the targets in names and uses stored metadata for information about upstream dependencies as needed. shortcut = TRUE increases speed if there are a lot of up-to-date targets, but it assumes all the dependencies are up to date, so please use with caution. Also, shortcut = TRUE only works if you set names.


Optional, define the set of allowable vertices in the graph. Unlike names, allow is invoked only after the graph is mostly resolved, so it will not speed up execution. Set to NULL to allow all vertices in the pipeline and environment (default). Otherwise, you can supply symbols or tidyselect helpers like starts_with().


Optional, define the set of exclude vertices from the graph. Unlike names, exclude is invoked only after the graph is mostly resolved, so it will not speed up execution. Set to NULL to exclude no vertices. Otherwise, you can supply symbols or tidyselect helpers like any_of() and starts_with().


Logical, whether to show colors to distinguish outdated targets from up-to-date targets. (Global functions and objects still show these colors.) Looking for outdated targets takes a lot of time for large pipelines with lots of branches, and setting outdated to FALSE is a nice way to speed up the graph if you only want to see dependency relationships and pipeline progress.


Character of length 1, name of the reporter to user. Controls how messages are printed as targets are checked. Choices:

  • "silent": print nothing.

  • "forecast": print running totals of the checked and outdated targets found so far.


Positive numeric of length 1 with the minimum number of seconds between times when the reporter prints progress messages to the R console.


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.


A list of arguments to callr_function.


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.


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.


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


A list with two data frames: vertices and edges. The vertices data frame has one row per target and columns with the the type of the target or object (stem, branch, map, cross, function, or object), each target's description, and each target's status (up to date, outdated, dispatched, completed, canceled, or errored), as well as metadata if available (seconds of runtime, bytes of storage, and number of dynamic branches). The edges data frame has one row for every edge and columns to and from to mark the starting and terminating vertices.

Dependency graph

The dependency graph of a pipeline is a directed acyclic graph (DAG) where each node indicates a target or global object and each directed edge indicates where a downstream node depends on an upstream node. The DAG is not always a tree, but it never contains a cycle because no target is allowed to directly or indirectly depend on itself. The dependency graph should show a natural progression of work from left to right. targets uses static code analysis to create the graph, so the order of tar_target() calls in the _targets.R file does not matter. However, targets does not support self-referential loops or other cycles. For more information on the dependency graph, please read

See also


if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) { # for CRAN
tar_dir({ # tar_dir() runs code from a temp dir for CRAN.
    tar_target(y1, 1 + 1),
    tar_target(y2, 1 + 1, description = "y2 info"),
    tar_target(z, y1 + y2, description = "z info")
}, ask = FALSE)
tar_network(targets_only = TRUE)