A target is a single step of computation in a pipeline. It runs an R command and returns a value. This value gets treated as an R object that can be used by the commands of targets downstream. Targets that are already up to date are skipped. See the user manual for more details.
tar_target() defines a target using non-standard evaluation.
The name argument is an unevaluated symbol,
and the command and pattern
arguments are unevaluated expressions. Example:
tar_target(name = data, command = get_data()).
tar_target_raw() defines a target with standard evaluation.
The name argument is a character string,
and the command and pattern
arguments are evaluated expressions. Example:
tar_target_raw(name = "data", command = quote(get_data())).
tar_target_raw() also has extra arguments deps and string
for advanced customization.
tar_target(
name,
command,
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 = isTRUE(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")
)tar_target_raw(
name,
command,
pattern = NULL,
packages = targets::tar_option_get("packages"),
library = targets::tar_option_get("library"),
deps = NULL,
string = NULL,
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 = isTRUE(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")
)
A target object. Users should not modify these directly,
just feed them to list() in your target script file
(default: _targets.R).
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.
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)).
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.
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.
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.
Character vector of library paths to try
when loading packages.
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.
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.
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().
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.)
Character of length 1, memory strategy. Possible values:
"auto" (default): equivalent to memory = "transient" in almost
all cases. But to avoid superfluous reads from disk,
memory = "auto" is equivalent to memory = "persistent" for
for non-dynamically-branched targets that other targets
dynamically branch over. For example: if your pipeline has
tar_target(name = y, command = x, pattern = map(x)),
then tar_target(name = x, command = f(), memory = "auto")
will use persistent memory in order to avoid rereading all of x
for every branch of y.
"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.
"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).
For cloud-based file targets
(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.
Logical: TRUE to run base::gc()
just before the target runs, in whatever R process it is about to run
(which could be a parallel worker).
FALSE to omit garbage collection.
Numeric values get converted to FALSE.
The garbage_collection option in tar_option_set()
is independent of the
argument of the same name in tar_target().
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.
Deprecated on 2025-04-08 (targets version 1.10.1.9013).
targets has moved to a more efficient scheduling algorithm
(https://github.com/ropensci/targets/issues/1458)
which cannot support priorities.
The priority argument of tar_target() no longer has a reliable
effect on execution order.
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.
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:
"worker" (default): the worker saves/uploads the value.
"main": the target's return value is sent back to the
host machine and saved/uploaded locally.
"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.
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:
"auto" (default): equivalent to retrieval = "worker" in almost all
cases. But to avoid unnecessary reads from disk, retrieval = "auto"
is equivalent to retrieval = "main" for dynamic branches that
branch over non-dynamic targets. For example: if your pipeline has
tar_target(x, command = f()), then
tar_target(y, command = x, pattern = map(x), retrieval = "auto")
will use "main" retrieval in order to avoid rereading all of x
for every branch of y.
"worker": the worker loads the target's dependencies.
"main": the target's dependencies are loaded on the host machine
and sent to the worker before the target runs.
"none": targets makes no attempt to load its
dependencies. With retrieval = "none", loading dependencies
is the responsibility of the user. Use with caution.
An optional object from tar_cue() to customize the
rules that decide whether the target is up to date.
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".
Optional character vector of the adjacent upstream
dependencies of the target, including targets and global objects.
If NULL, dependencies are resolved automatically as usual.
The deps argument is only for developers of extension
packages such as tarchetypes,
not for end users, and it should almost never be used at all.
In scenarios that at first appear to requires deps,
there is almost always a simpler and more robust workaround
that avoids setting deps.
Optional string representation of the command.
Internally, the string gets hashed to check if the command changed
since last run, which helps targets decide whether the
target is up to date. External interfaces can take control of
string to ignore changes in certain parts of the command.
If NULL, the strings is just deparsed from command (default).
Functions like tar_target() produce target objects,
special objects with specialized sets of S3 classes.
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.
targets has several built-in storage formats to control how return
values are saved and loaded from disk:
"rds": Default, uses saveRDS() and readRDS(). Should work for
most objects, but slow.
"auto": either "file" or "qs", depending on the return value
of the target. If the return value is a character vector of
existing files (and/or directories), then the format becomes
"file" before tar_make() saves the target. Otherwise,
the format becomes "qs".
NOTE: format = "auto" slows down pipelines with 10000+ targets
because it creates deep copies of 20000+ internal data objects.
Pipelines of this size should use a more explicit format instead of
"auto".
"qs": Uses qs2::qs_save() and qs2::qs_read(). Should work for
most objects, much faster than "rds". Optionally configure settings
through tar_resources() and tar_resources_qs().
Prior to targets version 1.8.0.9014, format = "qs" used the qs
package. qs has since been superseded in favor of qs2, and so
later versions of targets use qs2 to save new data. To read
existing data, targets first attempts qs2::qs_read(), and then if
that fails, it falls back on qs::qread().
"feather": Uses arrow::write_feather() and
arrow::read_feather() (version 2.0). Much faster than "rds",
but the value must be a data frame. Optionally set
compression and compression_level in arrow::write_feather()
through tar_resources() and tar_resources_feather().
Requires the arrow package (not installed by default).
"parquet": Uses arrow::write_parquet() and
arrow::read_parquet() (version 2.0). Much faster than "rds",
but the value must be a data frame. Optionally set
compression and compression_level in arrow::write_parquet()
through tar_resources() and tar_resources_parquet().
Requires the arrow package (not installed by default).
"fst": Uses fst::write_fst() and fst::read_fst().
Much faster than "rds", but the value must be
a data frame. Optionally set the compression level for
fst::write_fst() through tar_resources() and tar_resources_fst().
Requires the fst package (not installed by default).
"fst_dt": Same as "fst", but the value is a data.table.
Deep copies are made as appropriate in order to protect
against the global effects of in-place modification.
Optionally set the compression level the same way as for "fst".
"fst_tbl": Same as "fst", but the value is a tibble.
Optionally set the compression level the same way as for "fst".
"keras": superseded by tar_format() and incompatible
with error = "null" (in tar_target() or tar_option_set()).
Uses keras::save_model_hdf5() and
keras::load_model_hdf5(). The value must be a Keras model.
Requires the keras package (not installed by default).
"torch": superseded by tar_format() and incompatible
with error = "null" (in tar_target() or tar_option_set()).
Uses torch::torch_save() and torch::torch_load().
The value must be an object from the torch package
such as a tensor or neural network module.
Requires the torch package (not installed by default).
"file": A file target. To use this format,
the target needs to manually identify or save some data
and return a character vector of paths
to the data (must be a single file path if repository
is not "local"). (These paths must be existing files
and nonempty directories.)
Then, targets automatically checks those files and cues
the appropriate run/skip decisions if those files are out of date.
Those paths must point to files or directories,
and they must not contain characters | or *.
All the files and directories you return must actually exist,
or else targets will throw an error. (And if storage is "worker",
targets will first stall out trying to wait for the file
to arrive over a network file system.)
If the target does not create any files, the return value should be
character(0).
If repository is not "local" and format is "file",
then the character vector returned by the target must be of length 1
and point to a single file. (Directories and vectors of multiple
file paths are not supported for file targets on the cloud.)
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.
"url": An input URL. For this storage format,
repository is implicitly "local",
URL format is like format = "file"
except the return value of the target is a URL that already exists
and serves as input data for downstream targets. Optionally
supply a custom curl handle through
tar_resources() and tar_resources_url().
in new_handle(), nobody = TRUE is important because it
ensures targets just downloads the metadata instead of
the entire data file when it checks time stamps and hashes.
The data file at the URL needs to have an ETag or a Last-Modified
time stamp, or else the target will throw an error because
it cannot track the data. Also, use extreme caution when
trying to use format = "url" to track uploads. You must be absolutely
certain the ETag and Last-Modified time stamp are fully updated
and available by the time the target's command finishes running.
targets makes no attempt to wait for the web server.
A custom format can be supplied with tar_format(). For this choice,
it is the user's responsibility to provide methods for (un)serialization
and (un)marshaling the return value of the target.
The formats starting with "aws_" are deprecated as of 2022-03-13
(targets version > 0.10.0). For cloud storage integration, use the
repository argument instead.
Formats "rds", "file", and "url" are general-purpose formats
that belong in the targets package itself.
Going forward, any additional formats should be implemented with
tar_format() in third-party packages like tarchetypes
and geotargets (for example: tarchetypes::tar_format_nanoparquet()).
Formats "qs", "fst", etc. are legacy formats from before the
existence of tar_format(), and they will continue to remain in
targets without deprecation.
Other targets:
tar_cue()
# Defining targets does not run them.
data <- tar_target(target_name, get_data(), packages = "tidyverse")
analysis <- tar_target(analysis, analyze(x), pattern = map(x))
# In a pipeline:
if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) { # for CRAN
tar_dir({ # tar_dir() runs code from a temp dir for CRAN.
tar_script({
library(targets)
library(tarchetypes)
list(
tar_target(name = x, command = 1 + 1),
tar_target_raw(name = "y", command = quote(x + y))
)
})
tar_make()
tar_read(x)
})
# Tidy evaluation
tar_option_set(envir = environment())
n_rows <- 30L
data <- tar_target(target_name, get_data(!!n_rows))
print(data)
# Disable tidy evaluation:
data <- tar_target(target_name, get_data(!!n_rows), tidy_eval = FALSE)
print(data)
tar_option_reset()
}
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