Set target options, including default arguments to
tar_target() such as packages, storage format,
iteration type, and cue. Only the non-null arguments are actually
set as options. See currently set options with tar_option_get().
To use tar_option_set() effectively, put it in your workflow's
target script file (default: _targets.R)
before calls to tar_target() or tar_target_raw().
tar_option_set(
tidy_eval = NULL,
packages = NULL,
imports = NULL,
library = NULL,
envir = NULL,
format = NULL,
iteration = NULL,
error = NULL,
memory = NULL,
garbage_collection = NULL,
deployment = NULL,
priority = NULL,
backoff = NULL,
resources = NULL,
storage = NULL,
retrieval = NULL,
cue = NULL,
debug = NULL,
workspaces = NULL,
workspace_on_error = NULL
)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 builds. Use tar_option_set() to set packages
globally for all subsequent targets you define.
Character vector of package names to track
global dependencies. For example, if you write
tar_option_set(imports = "yourAnalysisPackage") early in your
target script file (default: _targets.R)
then tar_make() will automatically rerun or skip targets
in response to changes to the R functions and objects defined in
yourAnalysisPackage. Does not account for low-level compiled code
such as C/C++ or Fortran. If you supply multiple packages,
e.g. tar_option_set(imports = c("p1", "p2")), then the objects in
p1 override the objects in p2 if there are name conflicts.
Similarly, objects in tar_option_get("envir") override
everything in tar_option_get("imports").
Character vector of library paths to try
when loading packages.
Environment containing functions and global objects
common to all targets in the pipeline.
The envir argument of tar_make() and related functions
always overrides the current value of tar_option_get("envir")
in the current R session just before running the target script file,
so whenever you need to set an alternative envir, you should always set
it with tar_option_set() from within the target script file.
In other words, if you call tar_option_set(envir = envir1) in an
interactive session and then
tar_make(envir = envir2, callr_function = NULL),
then envir2 will be used.
If envir is the global environment, all the promise objects
are diffused before sending the data to parallel workers
in tar_make_future() and tar_make_clustermq(),
but otherwise the environment is unmodified.
This behavior improves performance by decreasing
the size of data sent to workers.
If envir is not the global environment, then it should at least inherit
from the global environment or base environment
so targets can access attached packages.
In the case of a non-global envir, targets attempts to remove
potentially high memory objects that come directly from targets.
That includes tar_target() objects of class "tar_target",
as well as objects of class "tar_pipeline" or "tar_algorithm".
This behavior improves performance by decreasing
the size of data sent to workers.
Package environments should not be assigned to envir.
To include package objects as upstream dependencies in the pipeline,
assign the package to the packages and imports arguments
of tar_option_set().
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, 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 data frame. 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.)
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 such as format = "aws_file",
this memory strategy applies to
temporary local copies of the file in _targets/scratch/":
"persistent" means they remain until the end of the pipeline,
and "transient" means they get deleted from the file system
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, only relevant to
tar_make_clustermq() and tar_make_future(). If "worker",
the target builds on a parallel worker. If "main",
the target builds on the host machine / process managing the pipeline.
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 built earlier
(and polled earlier in tar_make_future()).
Numeric of length 1, must be greater than or equal to 0.01.
Maximum upper bound of the random polling interval
for the priority queue (seconds).
In high-performance computing (e.g. tar_make_clustermq()
and tar_make_future()) it can be expensive to repeatedly poll the
priority queue if no targets are ready to process. The number of seconds
between polls is runif(1, 0.001, max(backoff, 0.001 * 1.5 ^ index)),
where index is the number of consecutive polls so far that found
no targets ready to skip or run.
(If no target is ready, index goes up by 1. If a target is ready,
index resets to 0. For more information on exponential,
backoff, visit https://en.wikipedia.org/wiki/Exponential_backoff).
Raising backoff is kinder to the CPU etc. but may incur delays
in some instances.
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().
If "main", the target's return value is sent back to the
host machine and saved locally. If "worker", the worker
saves the value.
Character of length 1, only relevant to
tar_make_clustermq() and tar_make_future().
If "main", the target's dependencies are loaded on the host machine
and sent to the worker before the target builds.
If "worker", the worker loads the targets dependencies.
An optional object from tar_cue() to customize the
rules that decide whether the target is up to date.
Character vector of names of targets to run in debug mode.
To use effectively, you must set callr_function = NULL and
restart your R session just before running. You should also
tar_make(), tar_make_clustermq(), or tar_make_future().
For any target mentioned in debug, targets will force the target to
build locally (with tar_cue(mode = "always") and deployment = "main"
in the settings) and pause in an interactive debugger to help you diagnose
problems. This is like inserting a browser() statement at the
beginning of the target's expression, but without invalidating any
targets.
Character vector of target names.
Could be non-branching targets, whole dynamic branching targets,
or individual branch names. tar_make() and friends
will save workspace files for these targets even if
the targets are skipped. Workspace files help with debugging.
See tar_workspace() for details about workspaces.
Logical of length 1, whether to save
a workspace file for each target that throws an error.
Workspace files help with debugging.
See tar_workspace() for details about workspaces.
NULL (invisibly).
Other configuration:
tar_config_get(),
tar_config_set(),
tar_envvars(),
tar_option_get(),
tar_option_reset()
# NOT RUN {
tar_option_get("format") # default format before we set anything
tar_target(x, 1)$settings$format
tar_option_set(format = "fst_tbl") # new default format
tar_option_get("format")
tar_target(x, 1)$settings$format
tar_option_reset() # reset the format
tar_target(x, 1)$settings$format
if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) {
tar_dir({ # tar_dir() runs code from a temporary directory.
tar_script({
tar_option_set(cue = tar_cue(mode = "always")) # All targets always run.
list(tar_target(x, 1), tar_target(y, 2))
})
tar_make()
tar_make()
})
}
# }
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