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#' @title Set target options.
#' @export
#' @family configuration
#' @description Set target options, including default arguments to
#' [tar_target()] such as packages, storage format,
#' iteration type, and cue. Only the non-null arguments are actually
#' set as options. See currently set options with [tar_option_get()].
#' To use `tar_option_set()` effectively, put it in your workflow's
#' target script file (default: `_targets.R`)
#' before calls to [tar_target()] or [tar_target_raw()].
#' @return `NULL` (invisibly).
#' @inheritSection tar_target Storage formats
#' @inheritParams tar_target
#' @param repository_meta Character of length 1 with the same values as
#' `repository` (`"aws"`, `"gcp"`, `"local"`). Cloud repository
#' for the metadata text files in `_targets/meta/`, including target
#' metadata and progress data. Defaults to `tar_option_get("repository")`.
#' @param imports Character vector of package names.
#' For every package listed, `targets` tracks every
#' dataset and every object in the package namespace
#' as if it were part of the global namespace.
#' As an example, say you have a package called `customAnalysisPackage`
#' which contains an object called `analysis_function()`.
#' If you write `tar_option_set(imports = "yourAnalysisPackage")` in your
#' target script file (default: `_targets.R`),
#' then a function called `"analysis_function"` will show up in the
#' [tar_visnetwork()] graph, and any targets or functions
#' referring to the symbol `"analysis_function"` will depend on the
#' function `analysis_function()` from package `yourAnalysisPackage`.
#' This is best combined with
#' `tar_option_set(packages = "yourAnalysisPackage")` so
#' that `analysis_function()` can actually be called in your code.
#'
#' There are several important limitations:
#' 1. Namespaced calls, e.g. `yourAnalysisPackage::analysis_function()`,
#' are ignored because of the limitations in `codetools::findGlobals()`
#' which powers the static code analysis capabilities of `targets`.
#' 2. The `imports` option only looks at R objects and R code.
#' It not account for low-level compiled code
#' such as C/C++ or Fortran.
#' 3. If you supply multiple packages,
#' e.g. `tar_option_set(imports = c("p1", "p2"))`, then the objects in
#' `p1` override the objects in `p2` if there are name conflicts.
#' 4. Similarly, objects in `tar_option_get("envir")` override
#' everything in `tar_option_get("imports")`.
#' @param envir Environment containing functions and global objects
#' common to all targets in the pipeline.
#' 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.
#'
#' If `envir` is the global environment, all the promise objects
#' are diffused before sending the data to parallel workers
#' in [tar_make_future()] and [tar_make_clustermq()],
#' but otherwise the environment is unmodified.
#' This behavior improves performance by decreasing
#' the size of data sent to workers.
#'
#' If `envir` is not the global environment, then it should at least inherit
#' from the global environment or base environment
#' so `targets` can access attached packages.
#' In the case of a non-global `envir`, `targets` attempts to remove
#' potentially high memory objects that come directly from `targets`.
#' That includes `tar_target()` objects of class `"tar_target"`,
#' as well as objects of class `"tar_pipeline"` or `"tar_algorithm"`.
#' This behavior improves performance by decreasing
#' the size of data sent to workers.
#'
#' Package environments should not be assigned to `envir`.
#' To include package objects as upstream dependencies in the pipeline,
#' assign the package to the `packages` and `imports` arguments
#' of `tar_option_set()`.
#' @param backoff An object from [`tar_backoff()`] configuring the exponential
#' backoff algorithm of the pipeline. See [`tar_backoff()`] for details.
#' A numeric argument for `backoff` is still allowed, but deprecated.
#' @param debug Character vector of names of targets to run in debug mode.
#' To use effectively, you must set `callr_function = NULL` and
#' restart your R session just before running. You should also
#' [tar_make()], [tar_make_clustermq()], or [tar_make_future()].
#' For any target mentioned in `debug`, `targets` will force the target to
#' run locally (with `tar_cue(mode = "always")` and `deployment = "main"`
#' in the settings) and pause in an interactive debugger to help you diagnose
#' problems. This is like inserting a `browser()` statement at the
#' beginning of the target's expression, but without invalidating any
#' targets.
#' @param workspaces Character vector of target names.
#' Could be non-branching targets, whole dynamic branching targets,
#' or individual branch names. [tar_make()] and friends
#' will save workspace files for these targets even if
#' the targets are skipped. Workspace files help with debugging.
#' See [tar_workspace()] for details about workspaces.
#' @param workspace_on_error Logical of length 1, whether to save
#' a workspace file for each target that throws an error.
#' Workspace files help with debugging.
#' See [tar_workspace()] for details about workspaces.
#' @param seed Integer of length 1, seed for generating
#' target-specific pseudo-random number generator seeds.
#' These target-specific seeds are deterministic and depend on
#' `tar_option_get("seed")` and the target name. Target-specific seeds
#' are safely and reproducibly applied to each target's command,
#' and they are stored in the metadata and retrievable with
#' [tar_meta()] or [tar_seed()].
#'
#' Either the user or third-party packages built on top of `targets`
#' may still set seeds inside the command of a target.
#' For example, some target factories in the
#' `tarchetypes` package assigns replicate-specific
#' seeds for the purposes of reproducible within-target batched replication.
#' In cases like these, the effect of the target-specific seed saved
#' in the metadata becomes irrelevant and the seed defined in the command
#' applies.
#'
#' The `seed` option can also be `NA` to disable
#' automatic seed-setting. Any targets defined while
#' `tar_option_get("seed")` is `NA` will not set a seed.
#' In this case, those targets will never be up to date
#' unless they have `cue = tar_cue(seed = FALSE)`.
#' @param controller A controller or controller group object
#' produced by the `crew` R package. `crew` brings auto-scaled
#' distributed computing to [tar_make()].
#' @param trust_object_timestamps Logical of length 1, whether to use
#' file system modification timestamps to check whether the target output
#' data files in `_targets/objects/` are up to date. This is an advanced
#' setting and usually does not need to be set by the user
#' except on old or difficult platforms.
#'
#' If `trust_object_timestamps`
#' is `TRUE` (default), then `targets` looks at the timestamp first.
#' If it agrees with the timestamp recorded in the metadata, then `targets`
#' considers the file unchanged. If the timestamps disagree, then `targets`
#' recomputes the hash to make a final determination.
#' This practice reduces the number of hash computations
#' and thus saves time.
#'
#' However, timestamp precision varies from a few
#' nanoseconds at best to 2 entire seconds at worst, and timestamps
#' with poor precision should not be fully trusted if there is any
#' possibility that you will manually change the file within 2 seconds
#' after the pipeline finishes.
#' If the data store is on a file system with low-precision timestamps,
#' then you may
#' consider setting `trust_object_timestamps` to `FALSE` so `targets`
#' errs on the safe side and always recomputes the hashes of files in
#' `_targets/objects/`.
#'
#' To check if your
#' file system has low-precision timestamps, you can run
#' `file.create("x"); nanonext::msleep(1); file.create("y");`
#' from within the directory containing the `_targets` data store
#' and then check
#' `difftime(file.mtime("y"), file.mtime("x"), units = "secs")`.
#' If the value from `difftime()` is around 0.001 seconds
#' (must be strictly above 0 and below 1) then you do not need to set
#' `trust_object_timestamps = FALSE`.
#' @examples
#' tar_option_get("format") # default format before we set anything
#' tar_target(x, 1)$settings$format
#' tar_option_set(format = "fst_tbl") # new default format
#' tar_option_get("format")
#' tar_target(x, 1)$settings$format
#' tar_option_reset() # reset the format
#' tar_target(x, 1)$settings$format
#' if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) { # for CRAN
#' tar_dir({ # tar_dir() runs code from a temp dir for CRAN.
#' tar_script({
#' tar_option_set(cue = tar_cue(mode = "always")) # All targets always run.
#' list(tar_target(x, 1), tar_target(y, 2))
#' })
#' tar_make()
#' tar_make()
#' })
#' }
tar_option_set <- function(
tidy_eval = NULL,
packages = NULL,
imports = NULL,
library = NULL,
envir = NULL,
format = NULL,
repository = NULL,
repository_meta = NULL,
iteration = NULL,
error = NULL,
memory = NULL,
garbage_collection = NULL,
deployment = NULL,
priority = NULL,
backoff = NULL,
resources = NULL,
storage = NULL,
retrieval = NULL,
cue = NULL,
description = NULL,
debug = NULL,
workspaces = NULL,
workspace_on_error = NULL,
seed = NULL,
controller = NULL,
trust_object_timestamps = NULL
) {
force(envir)
if_any(is.null(tidy_eval), NULL, tar_options$set_tidy_eval(tidy_eval))
if_any(is.null(packages), NULL, tar_options$set_packages(packages))
if_any(is.null(imports), NULL, tar_options$set_imports(imports))
if_any(is.null(library), NULL, tar_options$set_library(library))
if_any(is.null(envir), NULL, tar_options$set_envir(envir))
if_any(is.null(format), NULL, tar_options$set_format(format))
if_any(is.null(repository), NULL, tar_options$set_repository(repository))
if_any(
is.null(repository_meta),
NULL,
tar_options$set_repository_meta(repository_meta)
)
if_any(is.null(iteration), NULL, tar_options$set_iteration(iteration))
if_any(is.null(error), NULL, tar_options$set_error(error))
if_any(is.null(memory), NULL, tar_options$set_memory(memory))
if_any(
is.null(garbage_collection),
NULL,
tar_options$set_garbage_collection(garbage_collection)
)
if_any(is.null(deployment), NULL, tar_options$set_deployment(deployment))
if_any(is.null(priority), NULL, tar_options$set_priority(priority))
if_any(is.null(backoff), NULL, tar_options$set_backoff(backoff))
if_any(is.null(resources), NULL, tar_options$set_resources(resources))
if_any(is.null(storage), NULL, tar_options$set_storage(storage))
if_any(is.null(retrieval), NULL, tar_options$set_retrieval(retrieval))
if_any(is.null(cue), NULL, tar_options$set_cue(cue))
if_any(is.null(description), NULL, tar_options$set_description(description))
if_any(is.null(debug), NULL, tar_options$set_debug(debug))
if_any(is.null(workspaces), NULL, tar_options$set_workspaces(workspaces))
if_any(
is.null(workspace_on_error),
NULL,
tar_options$set_workspace_on_error(workspace_on_error)
)
if_any(is.null(seed), NULL, tar_options$set_seed(seed))
if_any(is.null(controller), NULL, tar_options$set_controller(controller))
if_any(
is.null(trust_object_timestamps),
NULL,
tar_options$set_trust_object_timestamps(trust_object_timestamps)
)
invisible()
}
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