Read a target's return value from its file in
_targets/objects/
. For file targets (i.e. format = "file"
)
the paths are returned.
tar_read()
expects an
unevaluated symbol for the name
argument, whereas tar_read_raw()
expects a character string.
Usage
tar_read(
name,
branches = NULL,
meta = tar_meta(store = store),
store = targets::tar_config_get("store")
)
tar_read_raw(
name,
branches = NULL,
meta = tar_meta(store = store),
store = targets::tar_config_get("store")
)
Arguments
- name
Name of the target to read.
tar_read()
expects an unevaluated symbol for thename
argument, whereastar_read_raw()
expects a character string.- branches
Integer of indices of the branches to load if the target is a pattern.
- meta
Data frame of metadata from
tar_meta()
.tar_read()
with the default arguments can be inefficient for large pipelines because all the metadata is stored in a single file. However, if you calltar_meta()
beforehand and supply it to themeta
argument, then successive calls totar_read()
may run much faster.- store
Character of length 1, path to the
targets
data store. Defaults totar_config_get("store")
, which in turn defaults to_targets/
. When you set this argument, the value oftar_config_get("store")
is temporarily changed for the current function call. Seetar_config_get()
andtar_config_set()
for details about how to set the data store path persistently for a project.
Value
The target's return value from its file in
_targets/objects/
, or the paths to the custom files and directories
if format = "file"
was set.
Cloud target data versioning
Some buckets in Amazon S3 or Google Cloud Storage are "versioned",
which means they track historical versions of each data object.
If you use targets
with cloud storage
(https://books.ropensci.org/targets/cloud-storage.html)
and versioning is turned on, then targets
will record each
version of each target in its metadata.
Functions like tar_read()
and tar_load()
load the version recorded in the local metadata,
which may not be the same as the "current" version of the
object in the bucket. Likewise, functions tar_delete()
and tar_destroy()
only remove
the version ID of each target as recorded in the local
metadata.
If you want to interact with the latest version of an object instead of the version ID recorded in the local metadata, then you will need to delete the object from the metadata.
Make sure your local copy of the metadata is current and up to date. You may need to run
tar_meta_download()
ortar_meta_sync()
first.Run
tar_unversion()
to remove the recorded version IDs of your targets in the local metadata.With the version IDs gone from the local metadata, functions like
tar_read()
andtar_destroy()
will use the latest version of each target data object.Optional: to back up the local metadata file with the version IDs deleted, use
tar_meta_upload()
.
Storage access
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()
.
See also
Other storage:
tar_format()
,
tar_load()
,
tar_load_everything()
,
tar_objects()
Examples
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, 1 + 1))
})
tar_make()
tar_read(x)
tar_read_raw("x")
})
}