Create a target that responds to a change in an arbitrary value. If the value changes, the target reruns.
Usage
tar_change(
name,
command,
change,
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)
. Intar_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 nameddownstream_target
which depends on a targetupstream_target
and a functionf()
. 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 withtar_meta(your_target, seed)
and runtar_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)
. Intar_target_raw()
,command
is an evaluated expression, e.g.tar_target_raw(command = quote(data))
.- change
R code for the upstream change-inducing target.
- tidy_eval
Whether to invoke tidy evaluation (e.g. the
!!
operator fromrlang
) as soon as the target is defined (beforetar_make()
). Applies to argumentscommand
andchange
.- 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 theendpoint
argument oftar_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"
andformat
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 withvctrs::vec_slice()
and aggregation happens withvctrs::vec_c()
."list"
, branching happens with[[]]
and aggregation happens withlist()
."group"
:dplyr::group_by()
-like functionality to branch over subsets of a non-dynamic data frame. Foriteration = "group"
, the target must not by dynamic (thepattern
argument oftar_target()
must be leftNULL
). The target's return value must be a data frame with a specialtar_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 thetar_group()
function to see how you can create the specialtar_group
column withdplyr::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 returnsNULL
. 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 ofNULL
is given to upstream dependencies witherror = "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 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. Possible values:
"auto"
: new intargets
version 1.8.0.9011,memory = "auto"
is equivalent tomemory = "transient"
for dynamic branching (a non-nullpattern
argument) andmemory = "persistent"
for targets that do not use dynamic branching."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)."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"
), thememory
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 runbase::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 ofgarbage_collection
are converted toTRUE
orFALSE
usingisTRUE()
. In other words, non-logical values are convertedFALSE
. For example,garbage_collection = 2
is equivalent togarbage_collection = FALSE
.- 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 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 wheretargets
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. Withretrieval = "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()
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"
.
Value
A list of two target objects, one upstream and one downstream. The upstream one triggers the change, and the downstream one responds to it. See the "Target objects" section for background.
Details
tar_change()
creates a pair of targets, one upstream
and one downstream. The upstream target always runs and returns
an auxiliary value. This auxiliary value gets referenced in the
downstream target, which causes the downstream target to rerun
if the auxiliary value changes. The behavior is cancelled if
cue
is tar_cue(depend = FALSE)
or tar_cue(mode = "never")
.
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 target
did not detect a change.
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_download()
,
tar_force()
,
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_change(x, command = tempfile(), change = tempfile())
)
})
targets::tar_make()
targets::tar_make()
})
}