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(
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 = 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. 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 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.
Language to define branching for a target.
For example, in a pipeline with numeric vector targets x
and y
,
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.
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.
"abridge"
: any currently running targets keep running,
but no new targets launch after that.
(Visit https://books.ropensci.org/targets/debugging.html
to learn how to debug targets using saved workspaces.)
"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.
Character of length 1, memory strategy.
If "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).
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"
with repository = "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.
Logical, whether to run base::gc()
just before the target runs.
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.
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()
).
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 of length 1, only relevant to
tar_make_clustermq()
and tar_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 when retrieval = "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 to format = "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 if format
is "file"
.
Character of length 1, only relevant to
tar_make_clustermq()
and tar_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.
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"
.
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.
"rds"
: Default, uses saveRDS()
and readRDS()
. Should work for
most objects, but slow.
"qs"
: Uses qs::qsave()
and qs::qread()
. Should work for
most objects, much faster than "rds"
. Optionally set the
preset for qsave()
through tar_resources()
and tar_resources_qs()
.
"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 dynamic file. 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 dynamic files 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.
To check if the file is up to date, targets
avoids timestamps
and always recomputes the hash. If you find this to be too slow,
and if you trust the time stamps on your file system
(see the trust_object_timestamps
argument of tar_option_set()
),
then consider format = "file_fast"
instead.
"file_fast"
: same as format = "file"
, except that targets
uses time stamps to check if a file is up to date. If the time stamp
of the file agrees with the time stamp in the metadata, the
file is considered up to date. Otherwise, targets
recomputes the
hash of the file to make a final determination. Low-precision
timestamps are not reliable for this, and some file systems
have timestamp precision as poor as 2 seconds. See the
trust_object_timestamps
argument of tar_option_set()
for advice on this.
"url"
: A dynamic 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.
Other targets:
tar_cue()
,
tar_format()
,
tar_target_raw()
# Defining targets does not run them.
data <- tar_target(target_name, get_data(), packages = "tidyverse")
analysis <- tar_target(analysis, analyze(x), pattern = map(x))
# Pipelines accept targets.
pipeline <- list(data, analysis)
# 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()
# 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(tar_target(x, 1 + 1), ask = FALSE)
tar_make()
tar_read(x)
})
}
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