Read the metadata of all recorded targets and global objects.
tar_meta(
names = NULL,
fields = NULL,
targets_only = FALSE,
complete_only = FALSE,
store = targets::tar_config_get("store")
)A data frame with one row per target/object and the selected fields.
Optional, names of the targets. If supplied, tar_meta()
only returns metadata on these targets.
You can supply symbols
or tidyselect helpers like any_of() and starts_with().
If NULL, all names are selected.
Optional, names of columns/fields to select. If supplied,
tar_meta() only returns the selected metadata columns.
If NULL, all fields are selected.
You can supply symbols or tidyselect helpers
like any_of() and starts_with().
The name column is always included first
no matter what you select. Choices:
name: name of the target or global object.
type: type of the object: either "function" or "object"
for global objects, and "stem", "branch",
"map", or "cross" for targets.
data: hash of the output data.
command: hash of the target's deparsed command.
depend: hash of the immediate upstream dependencies of the target.
seed: random number generator seed with which the target ran.
A target's random number generator seed
is a deterministic function of its name. 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.
path: A list column of paths to target data. Usually, each element
is a single path, but there could be multiple paths per target
for dynamic files (i.e. tar_target(format = "file")).
time: POSIXct object with the time the target's data in storage
was last modified. If the target stores no local file,
then the time stamp corresponds to the time the target last
ran successfully. Only targets that run commands have time stamps:
just non-branching targets and individual dynamic branches.
Displayed in the current time zone of the system.
If there are multiple outputs for that target, as with file targets,
then the maximum time is shown.
size: hash of the sum of all the bytes of the files at path.
bytes: total file size in bytes of all files in path.
format: character, one of the admissible data storage formats.
See the format argument in the tar_target() help file for details.
iteration: character, either "list" or "vector"
to describe the iteration and aggregation mode of the target. See the
iteration argument in the tar_target() help file for details.
parent: for branches, name of the parent pattern.
children: list column, names of the children of targets that
have them. These include buds of stems and branches of patterns.
seconds: number of seconds it took to run the target.
warnings: character string of warning messages
from the last run of the target.
Only the first 50 warnings are available,
and only the first 2048 characters of the concatenated warning messages.
error: character string of the error message if the target errored.
Logical, whether to just show information about targets or also return metadata on functions and other global objects.
Logical, whether to return only complete rows
(no NA values).
Character of length 1, path to the
targets data store. Defaults to tar_config_get("store"),
which in turn defaults to _targets/.
When you set this argument, the value of tar_config_get("store")
is temporarily changed for the current function call.
See tar_config_get() and tar_config_set() for details
about how to set the data store path persistently
for a project.
Several functions like tar_make(), tar_read(), tar_load(),
tar_meta(), and tar_progress() read or modify
the local data store of the pipeline.
The local data store is in flux while a pipeline is running,
and depending on how distributed computing or cloud computing is set up,
not all targets can even reach it. So please do not call these
functions from inside a target as part of a running
pipeline. The only exception is literate programming
target factories in the tarchetypes package such as tar_render()
and tar_quarto().
Metadata files help targets
read data objects and decide if the pipeline is up to date.
Usually, these metadata files live in files in the local
_targets/meta/ folder in your project, e.g. _targets/meta/meta.
But in addition, if you set repository to anything other than
"local" in tar_option_set() in _targets.R, then tar_make()
continuously uploads the metadata files to the bucket you specify
in resources. tar_meta_delete() will delete those files from the
cloud, and so will tar_destroy() if destroy is
set to either "all" or "cloud".
Other functions in targets, such as tar_meta(),
tar_visnetwork(), tar_outdated(), and tar_invalidate(),
use the local metadata only and ignore the copies on the cloud.
So if you are working on a different computer than the
one running the pipeline, you will need to download the cloud metadata
to your current machine using tar_meta_download(). Other functions
tar_meta_upload(), tar_meta_sync(), and tar_meta_delete()
also manage metadata across the cloud and the local file system.
Remarks:
The repository_meta option in tar_option_set() is actually
what controls where the metadata lives in the cloud, but it defaults
to repository.
Like tar_make(), tar_make_future() and tar_make_clustermq()
also continuously upload metadata files to the cloud bucket
specified in resources.
tar_meta_download() and related functions need to run _targets.R
to detect tar_option_set() options repository_meta and resources,
so please be aware of side effects that may happen running your
custom _targets.R file.
A metadata row only updates when the target completes.
tar_progress() shows information on targets that are running.
That is why the number of branches may disagree between tar_meta()
and tar_progress() for actively running pipelines.
Other metadata:
tar_meta_delete(),
tar_meta_download(),
tar_meta_sync(),
tar_meta_upload()
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(x, seq_len(2)),
tar_target(y, 2 * x, pattern = map(x))
)
}, ask = FALSE)
tar_make()
tar_meta()
tar_meta(starts_with("y_")) # see also any_of()
})
}
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