tar_target {targets} | R Documentation |
Declare a target.
Description
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.
Usage
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")
)
Arguments
name |
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. |
command |
R code to run the target. |
pattern |
Language to define branching for a target.
For example, in a pipeline with numeric vector targets |
tidy_eval |
Logical, whether to enable tidy evaluation
when interpreting |
packages |
Character vector of packages to load right before
the target runs or the output data is reloaded for
downstream targets. Use |
library |
Character vector of library paths to try
when loading |
format |
Optional storage format for the target's return value.
With the exception of |
repository |
Character of length 1, remote repository for target storage. Choices:
Note: if |
iteration |
Character of length 1, name of the iteration mode of the target. Choices:
|
error |
Character of length 1, what to do if the target stops and throws an error. Options:
|
memory |
Character of length 1, memory strategy.
If |
garbage_collection |
Logical, whether to run |
deployment |
Character of length 1. If |
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 |
resources |
Object returned by |
storage |
Character of length 1, only relevant to
|
retrieval |
Character of length 1, only relevant to
|
cue |
An optional object from |
description |
Character of length 1, a custom free-form human-readable
text description of the target. Descriptions appear as target labels
in functions like |
Value
A target object. Users should not modify these directly,
just feed them to list()
in your target script file
(default: _targets.R
).
Target objects
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.
Storage formats
-
"rds"
: Default, usessaveRDS()
andreadRDS()
. Should work for most objects, but slow. -
"qs"
: Usesqs::qsave()
andqs::qread()
. Should work for most objects, much faster than"rds"
. Optionally set the preset forqsave()
throughtar_resources()
andtar_resources_qs()
. -
"feather"
: Usesarrow::write_feather()
andarrow::read_feather()
(version 2.0). Much faster than"rds"
, but the value must be a data frame. Optionally setcompression
andcompression_level
inarrow::write_feather()
throughtar_resources()
andtar_resources_feather()
. Requires thearrow
package (not installed by default). -
"parquet"
: Usesarrow::write_parquet()
andarrow::read_parquet()
(version 2.0). Much faster than"rds"
, but the value must be a data frame. Optionally setcompression
andcompression_level
inarrow::write_parquet()
throughtar_resources()
andtar_resources_parquet()
. Requires thearrow
package (not installed by default). -
"fst"
: Usesfst::write_fst()
andfst::read_fst()
. Much faster than"rds"
, but the value must be a data frame. Optionally set the compression level forfst::write_fst()
throughtar_resources()
andtar_resources_fst()
. Requires thefst
package (not installed by default). -
"fst_dt"
: Same as"fst"
, but the value is adata.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 atibble
. Optionally set the compression level the same way as for"fst"
. -
"keras"
: superseded bytar_format()
and incompatible witherror = "null"
(intar_target()
ortar_option_set()
). Useskeras::save_model_hdf5()
andkeras::load_model_hdf5()
. The value must be a Keras model. Requires thekeras
package (not installed by default). -
"torch"
: superseded bytar_format()
and incompatible witherror = "null"
(intar_target()
ortar_option_set()
). Usestorch::torch_save()
andtorch::torch_load()
. The value must be an object from thetorch
package such as a tensor or neural network module. Requires thetorch
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 ifrepository
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 elsetargets
will throw an error. (And ifstorage
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 becharacter(0)
.If
repository
is not"local"
andformat
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 thetrust_object_timestamps
argument oftar_option_set()
), then considerformat = "file_fast"
instead. -
"file_fast"
: same asformat = "file"
, except thattargets
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 thetrust_object_timestamps
argument oftar_option_set()
for advice on this. -
"url"
: A dynamic input URL. For this storage format,repository
is implicitly"local"
, URL format is likeformat = "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 customcurl
handle throughtar_resources()
andtar_resources_url()
. innew_handle()
,nobody = TRUE
is important because it ensurestargets
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 useformat = "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 therepository
argument instead.
See Also
Other targets:
tar_cue()
,
tar_format()
,
tar_target_raw()
Examples
# 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)
})
}