Target with a custom condition to force execution.
Description
Create a target that always runs if a user-defined
condition rule is met.
Usage
tar_force(
name,
command,
force,
tidy_eval = targets::tar_option_get("tidy_eval"),
packages = targets::tar_option_get("packages"),
library = targets::tar_option_get("library"),
format = targets::tar_option_get("format"),
repository = targets::tar_option_get("repository"),
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"),
storage = targets::tar_option_get("storage"),
retrieval = targets::tar_option_get("retrieval"),
cue = targets::tar_option_get("cue"),
description = targets::tar_option_get("description")
)
Arguments
name |
Symbol, name of the target.
In tar_target(), name is an unevaluated symbol, e.g.
tar_target(name = data).
In tar_target_raw(), name is a character string, e.g.
tar_target_raw(name = "data").
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 most cases, The target name is the name of its local data file
in storage. Some file systems are not case sensitive, which means
converting a name to a different case may overwrite a different target.
Please ensure all target names have unique names when converted to
lower case.
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 tar_seed_set()
on the result to locally recreate the target's initial RNG state.
|
command |
R code to run the target.
In tar_target(), command is an unevaluated expression, e.g.
tar_target(command = data).
In tar_target_raw(), command is an evaluated expression, e.g.
tar_target_raw(command = quote(data)).
|
force |
R code for the condition that forces a build.
If it evaluates to TRUE, then your work will run during tar_make().
|
tidy_eval |
Whether to invoke tidy evaluation
(e.g. the !! operator from rlang) as soon as the target is defined
(before tar_make()). Applies to arguments command and force.
|
packages |
Character vector of packages to load right before
the target runs or the output data is reloaded for
downstream targets. Use tar_option_set() to set packages
globally for all subsequent targets you define.
|
library |
Character vector of library paths to try
when loading packages.
|
format |
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.
|
repository |
Character of length 1, remote repository for target
storage. Choices:
-
"local": file system of the local machine.
-
"aws": Amazon Web Services (AWS) S3 bucket. Can be configured
with a non-AWS S3 bucket using the endpoint argument of
tar_resources_aws(), but versioning capabilities may be lost
in doing so.
See the cloud storage section of
https://books.ropensci.org/targets/data.html
for details for instructions.
-
"gcp": Google Cloud Platform storage bucket.
See the cloud storage section of
https://books.ropensci.org/targets/data.html
for details for instructions.
A character string from tar_repository_cas() for content-addressable
storage.
Note: if repository is not "local" and format is "file"
then the target should create a single output file.
That output file is uploaded to the cloud and tracked for changes
where it exists in the cloud. The local file is deleted after
the target runs.
|
iteration |
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 non-dynamic data frame.
For iteration = "group", the target must not by dynamic
(the pattern argument of tar_target() must be left NULL).
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().
|
error |
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.
-
"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. In addition,
as of targets version 1.8.0.9011, a value of NULL is given
to upstream dependencies with error = "null" if loading fails.
-
"abridge": any currently running targets keep running,
but no new targets launch after that.
-
"trim": all currently running targets stay running. A queued
target is allowed to start if:
It is not downstream of the error, and
It is not a sibling branch from the same tar_target() call
(if the error happened in a dynamic branch).
The idea is to avoid starting any new work that the immediate error
impacts. error = "trim" is just like error = "abridge",
but it allows potentially healthy regions of the dependency graph
to begin running.
(Visit https://books.ropensci.org/targets/debugging.html
to learn how to debug targets using saved workspaces.)
|
memory |
Character of length 1, memory strategy. Possible values:
-
"auto": new in targets version 1.8.0.9011, memory = "auto"
is equivalent to memory = "transient" for dynamic branching
(a non-null pattern argument) and memory = "persistent"
for targets that do not use dynamic branching.
-
"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).
-
"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"),
the memory option 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.
|
garbage_collection |
Logical: TRUE to run base::gc()
just before the target runs,
FALSE to omit garbage collection.
In the case of high-performance computing,
gc() runs both locally and on the parallel worker.
All this garbage collection is skipped if the actual target
is skipped in the pipeline.
Non-logical values of garbage_collection are converted to TRUE or
FALSE using isTRUE(). In other words, non-logical values are
converted FALSE. For example, garbage_collection = 2
is equivalent to garbage_collection = FALSE.
|
deployment |
Character of length 1. If deployment is
"main", then the target will run on the central controlling R process.
Otherwise, if deployment is "worker" and you set up the pipeline
with distributed/parallel computing, then
the target runs on a parallel worker. For more on distributed/parallel
computing in targets, please visit
https://books.ropensci.org/targets/crew.html.
|
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 |
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.
|
storage |
Character string to control when the output of the target
is saved to storage. Only relevant when using targets
with parallel workers (https://books.ropensci.org/targets/crew.html).
Must be one of the following values:
-
"main": the target's return value is sent back to the
host machine and saved/uploaded locally.
-
"worker": the worker saves/uploads the value.
-
"none": targets makes no attempt to save the result
of the target to storage in the location where targets
expects it to be. Saving to storage is the responsibility
of the user. Use with caution.
|
retrieval |
Character string to control when the current target
loads its dependencies into memory before running.
(Here, a "dependency" is another target upstream that the current one
depends on.) Only relevant when using targets
with parallel workers (https://books.ropensci.org/targets/crew.html).
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 runs.
-
"worker": the worker loads the target's dependencies.
-
"none": targets makes no attempt to load its
dependencies. With retrieval = "none", loading dependencies
is the responsibility of the user. Use with caution.
|
cue |
An optional object from tar_cue()
to customize the rules that decide whether the target is up to date.
Only applies to the downstream target. The upstream target always runs.
|
description |
Character of length 1, a custom free-form human-readable
text description of the target. Descriptions appear as target labels
in functions like tar_manifest() and tar_visnetwork(),
and they let you select subsets of targets for the names argument of
functions like tar_make(). For example,
tar_manifest(names = tar_described_as(starts_with("survival model")))
lists all the targets whose descriptions start with the character
string "survival model".
|
Details
tar_force() creates a target that always runs
when a custom condition is met. The implementation builds
on top of tar_change(). Thus, a pair of targets is created:
an upstream auxiliary target to indicate the custom condition
and a downstream target that responds to it and does your work.
tar_force() does not actually use tar_cue_force(), and the
mechanism is totally different.
Because the upstream target always runs,
tar_outdated() and tar_visnetwork() will always
show both targets as outdated. However, tar_make() will still
skip the downstream one if the upstream custom condition is not met.
Value
A list of 2 targets objects: one to indicate whether the custom
condition is met, and another to respond to it and do your
actual work.
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.
See Also
Other targets with custom invalidation rules:
tar_change(),
tar_download(),
tar_skip()
Examples
if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
list(
tarchetypes::tar_force(x, tempfile(), force = 1 > 0)
)
})
targets::tar_make()
targets::tar_make()
})
}
