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 thename
argument, whereastar_cue_age_raw()
expects a character string forname
.- command
R code to run the target and return a value.
- age
A
difftime
object of length 1, such asas.difftime(3, units = "days")
. If the target's output data files are older thanage
(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 thanSys.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, setage = 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))
. Intar_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
andy
. Then,tar_target(z, x + y, pattern = map(x, y))
implicitly defines branches ofz
that each computex[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
andpattern
. IfTRUE
, 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()
ortar_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()
ortar_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()
ortar_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 returnsNULL
. The data hash is deliberately wrong so the target is not up to date for the next run of the pipeline."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 likeerror = "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. If
"persistent"
, the target stays in memory until the end of the pipeline (unlessstorage
is"worker"
, in which casetargets
unloads the value from memory right after storing it in order to avoid sending copious data over a network). If"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"
withrepository = "aws"
), this memory strategy 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, whether to run
base::gc()
just before the target runs.- deployment
Character of length 1. If
deployment
is"main"
, then the target will run on the central controlling R process. Otherwise, ifdeployment
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 intargets
, 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 oftargets
. Seetar_resources()
for details.- storage
Character of length 1, only relevant to
tar_make_clustermq()
andtar_make_future()
. 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"
: almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language. If you do use it, then the return value of the target is totally ignored when the target ends, but each downstream target still attempts to load the data file (except whenretrieval = "none"
).If you select
storage = "none"
, then the return value of the target's command is ignored, and the data is not saved automatically. As with dynamic files (format = "file"
) it is the responsibility of the user to write to the data store from inside the target.The distinguishing feature of
storage = "none"
(as opposed toformat = "file"
) is that in the general case, downstream targets will automatically try to load the data from the data store as a dependency. As a corollary,storage = "none"
is completely unnecessary ifformat
is"file"
.
- retrieval
Character of length 1, only relevant to
tar_make_clustermq()
andtar_make_future()
. 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 targets dependencies."none"
: the dependencies are not loaded at all. This choice is almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language.
- 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 themode
argument.mode
will be automatically determined by theage
argument oftar_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()
andtar_visnetwork()
, and they let you select subsets of targets for thenames
argument of functions liketar_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.
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()
})
}