tar_age {tarchetypes} | R Documentation |
Create a target that runs when the last run gets old
Description
tar_age()
creates a target that reruns
itself when it gets old enough.
In other words, the target reruns periodically at regular
intervals of time.
Usage
tar_age(
name,
command,
age,
pattern = NULL,
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 |
Name of the target.
tar_cue_age() expects an unevaluated symbol for the name
argument, whereas tar_cue_age_raw() expects a character string
for name .
|
command |
R code to run the target and return a value.
|
age |
A difftime object of length 1, such as
as.difftime(3, units = "days") . If the target's output data
files are older than age (according to the most recent
time stamp over all the target's output files)
then the target will rerun.
On the other hand, if at least one data file is
younger than Sys.time() - age , then the ordinary
invalidation rules apply, and the target may or not rerun.
If you want to force the target to run every 3 days,
for example, set age = as.difftime(3, units = "days") .
|
pattern |
Code to define a dynamic branching branching for a target.
In tar_target() , pattern is an unevaluated expression, e.g.
tar_target(pattern = map(data)) .
In tar_target_raw() , command is an evaluated expression, e.g.
tar_target_raw(pattern = quote(map(data))) .
To demonstrate dynamic branching patterns, suppose we have
a pipeline with numeric vector targets x and y . Then,
tar_target(z, x + y, pattern = map(x, y)) implicitly defines
branches of z that each compute x[1] + y[1] , x[2] + y[2] ,
and so on. See the user manual for details.
|
tidy_eval |
Logical, whether to enable tidy evaluation
when interpreting command and pattern . If TRUE , you can use the
"bang-bang" operator !! to programmatically insert
the values of global objects.
|
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 |
Logical, whether to rerun the target if the user-specified
storage format changed. The storage format is user-specified through
tar_target() or tar_option_set() .
|
repository |
Logical, whether to rerun the target if the user-specified
storage repository changed. The storage repository is user-specified
through tar_target() or tar_option_set() .
|
iteration |
Logical, whether to rerun the target if the user-specified
iteration method changed. The iteration method is user-specified through
tar_target() or tar_option_set() .
|
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 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 |
A targets::tar_cue() object. (See the "Cue objects"
section for background.) This cue object should contain any
optional secondary invalidation rules, anything except
the mode argument. mode will be automatically determined
by the age argument of tar_age() .
|
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_age()
uses the cue from tar_cue_age()
, which
uses the time stamps from targets::tar_meta()$time
.
See the help file of targets::tar_timestamp()
for an explanation of how this time stamp is calculated.
Value
A target object. See the "Target objects" section for background.
Dynamic branches at regular time intervals
Time stamps are not recorded for whole dynamic targets,
so tar_age()
is not a good fit for dynamic branching.
To invalidate dynamic branches at regular intervals,
it is recommended to use targets::tar_older()
in combination
with targets::tar_invalidate()
right before calling tar_make()
.
For example,
tar_invalidate(any_of(tar_older(Sys.time - as.difftime(1, units = "weeks"))))
# nolint
invalidates all targets more than a week old. Then, the next tar_make()
will rerun those targets.
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 cues:
tar_cue_age()
,
tar_cue_force()
,
tar_cue_skip()
Examples
if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
library(tarchetypes)
list(
tarchetypes::tar_age(
data,
data.frame(x = seq_len(26)),
age = as.difftime(0.5, units = "secs")
)
)
})
targets::tar_make()
Sys.sleep(0.6)
targets::tar_make()
})
}
[Package
tarchetypes version 0.11.0
Index]