Dynamic-within-static branching for data frames (count batching).
Source:R/tar_map2_count.R
, R/tar_map2_count_raw.R
tar_map2_count.Rd
Define targets for batched dynamic-within-static branching for data frames, where the user sets the (maximum) number of batches.
tar_map2_count()
expects unevaluated language for arguments
name
, command1
, command2
, columns1
, and columns2
.
tar_map2_count_raw()
expects a character string for name
and an evaluated expression object for each of
command1
, command2
, columns1
, and columns2
.
Usage
tar_map2_count(
name,
command1,
command2,
values = NULL,
names = NULL,
descriptions = tidyselect::everything(),
batches = 1L,
combine = TRUE,
suffix1 = "1",
suffix2 = "2",
columns1 = tidyselect::everything(),
columns2 = tidyselect::everything(),
rep_workers = 1,
delimiter = "_",
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"),
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")
)
tar_map2_count_raw(
name,
command1,
command2,
values = NULL,
names = NULL,
descriptions = quote(tidyselect::everything()),
batches = 1L,
combine = TRUE,
suffix1 = "1",
suffix2 = "2",
columns1 = quote(tidyselect::everything()),
columns2 = quote(tidyselect::everything()),
rep_workers = 1,
delimiter = "_",
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"),
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
Name of the target.
tar_rep()
expects unevaluatedname
andcommand
arguments (e.g.tar_rep(name = sim, command = simulate())
) whereastar_rep_raw()
expects an evaluated string forname
and an evaluated expression object forcommand
(e.g.tar_rep_raw(name = "sim", command = quote(simulate()))
).- command1
R code to create named arguments to
command2
. Must return a data frame with one row per call tocommand2
when run.In regular
tarchetypes
functions, thecommand1
argument is an unevaluated expression. In the"_raw"
versions of functions,command1
is an evaluated expression object.- command2
R code to map over the data frame of arguments produced by
command1
. Must return a data frame.In regular
tarchetypes
functions, thecommand2
argument is an unevaluated expression. In the"_raw"
versions of functions,command2
is an evaluated expression object.- values
Named list or data frame with values to iterate over. The names are the names of symbols in the commands and pattern statements, and the elements are values that get substituted in place of those symbols.
tar_map()
uses these elements to create new R code, so they should be basic types, symbols, or R expressions. For objects even a little bit complicated, especially objects with attributes, it is not obvious how to convert the object into code that generates it. For complicated objects, consider usingquote()
when you definevalues
, as shown at https://github.com/ropensci/tarchetypes/discussions/105.- names
Subset of
names(values)
used to generate the suffixes in the names of the new targets. The value ofnames
should be atidyselect
expression such as a call toany_of()
orstarts_with()
.- descriptions
Names of a column in
values
to append to the custom description of each generated target. The value ofdescriptions
should be atidyselect
expression such as a call toany_of()
orstarts_with()
.- batches
Positive integer of length 1, maximum number of batches (dynamic branches within static branches) of the downstream (
command2
) targets. Batches are formed from row groups of thecommand1
target output.- combine
Logical of length 1, whether to statically combine all the results into a single target downstream.
- suffix1
Character of length 1, suffix to apply to the
command1
targets to distinguish them from thecommand2
targets.- suffix2
Character of length 1, suffix to apply to the
command2
targets to distinguish them from thecommand1
targets.- columns1
A tidyselect expression to select which columns of
values
to append to the output of all targets. Columns already in the target output are not appended.In regular
tarchetypes
functions, thecolumns1
argument is an unevaluated expression. In the"_raw"
versions of functions,columns1
is an evaluated expression object.- columns2
A tidyselect expression to select which columns of
command1
output to append tocommand2
output. Columns already in the target output are not appended.columns1
takes precedence overcolumns2
.In regular
tarchetypes
functions, thecolumns2
argument is an unevaluated expression. In the"_raw"
versions of functions,columns2
is an evaluated expression object.- rep_workers
Positive integer of length 1, number of local R processes to use to run reps within batches in parallel. If 1, then reps are run sequentially within each batch. If greater than 1, then reps within batch are run in parallel using a PSOCK cluster.
- delimiter
Character of length 1, string to insert between other strings when creating names of targets.
- tidy_eval
Whether to invoke tidy evaluation (e.g. the
!!
operator fromrlang
) as soon as the target is defined (beforetar_make()
). Applies to thecommand
argument.- packages
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.- library
Character vector of library paths to try when loading
packages
.- format
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.- repository
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 theendpoint
argument oftar_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.A character string from
tar_repository_cas()
for content-addressable storage.
Note: if
repository
is not"local"
andformat
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.- error
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."null"
: The errored target continues and returnsNULL
. The data hash is deliberately wrong so the target is not up to date for the next run of the pipeline."abridge"
: any currently running targets keep running, but no new targets launch after that."trim"
: all currently running targets stay running. A queued target is allowed to start if:It is not downstream of the error, and
It is not a sibling branch from the same
tar_target()
call (if the error happened in a dynamic branch).
The idea is to avoid starting any new work that the immediate error impacts.
error = "trim"
is just likeerror = "abridge"
, but it allows potentially healthy regions of the dependency graph to begin running. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.)
- memory
Character of length 1, memory strategy. If
"persistent"
, the target stays in memory until the end of the pipeline (unlessstorage
is"worker"
, in which casetargets
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"
withrepository = "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.- garbage_collection
Logical, whether to run
base::gc()
just before the target runs.- deployment
Character of length 1. If
deployment
is"main"
, then the target will run on the central controlling R process. Otherwise, ifdeployment
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 intargets
, please visit https://books.ropensci.org/targets/crew.html.- 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
tar_make_future()
).- resources
Object returned by
tar_resources()
with optional settings for high-performance computing functionality, alternative data storage formats, and other optional capabilities oftargets
. Seetar_resources()
for details.- storage
Character of length 1, only relevant to
tar_make_clustermq()
andtar_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 whenretrieval = "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 toformat = "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 ifformat
is"file"
.
- retrieval
Character of length 1, only relevant to
tar_make_clustermq()
andtar_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.
- cue
An optional object from
tar_cue()
to customize the rules that decide whether the target is up to date.- description
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()
andtar_visnetwork()
, and they let you select subsets of targets for thenames
argument of functions liketar_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"
.
Details
Static branching creates one pair of targets
for each row in values
. In each pair,
there is an upstream non-dynamic target that runs command1
and a downstream dynamic target that runs command2
.
command1
produces a data frame of arguments to
command2
, and command2
dynamically maps over
these arguments in batches.
Target objects
Most tarchetypes
functions are target factories,
which means they return target objects
or lists of target objects.
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.
Replicate-specific seeds
In ordinary pipelines, each target has its own unique deterministic
pseudo-random number generator seed derived from its target name.
In batched replicate, however, each batch is a target with multiple
replicate within that batch. That is why tar_rep()
and friends give each replicate its own unique seed.
Each replicate-specific seed is created
based on the dynamic parent target name,
tar_option_get("seed")
(for targets
version 0.13.5.9000 and above),
batch index, and rep-within-batch index.
The seed is set just before the replicate runs.
Replicate-specific seeds are invariant to batching structure.
In other words,
tar_rep(name = x, command = rnorm(1), batches = 100, reps = 1, ...)
produces the same numerical output as
tar_rep(name = x, command = rnorm(1), batches = 10, reps = 10, ...)
(but with different batch names).
Other target factories with this seed scheme are tar_rep2()
,
tar_map_rep()
, tar_map2_count()
, tar_map2_size()
,
and tar_render_rep()
.
For the tar_map2_*()
functions,
it is possible to manually supply your own seeds
through the command1
argument and then invoke them in your
custom code for command2
(set.seed()
, withr::with_seed
,
or withr::local_seed()
). For tar_render_rep()
,
custom seeds can be supplied to the params
argument
and then invoked in the individual R Markdown reports.
Likewise with tar_quarto_rep()
and the execute_params
argument.
See also
Other branching:
tar_map2()
,
tar_map2_size()
,
tar_map_rep()
,
tar_rep()
,
tar_rep2()
,
tar_rep_map()
,
tar_rep_map_raw()
Examples
if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
tarchetypes::tar_map2_count(
x,
command1 = tibble::tibble(
arg1 = arg1,
arg2 = seq_len(6)
),
command2 = tibble::tibble(
result = paste(arg1, arg2),
random = sample.int(1e9, size = 1),
length_input = length(arg1)
),
values = tibble::tibble(arg1 = letters[seq_len(2)]),
batches = 3
)
})
targets::tar_make()
targets::tar_read(x)
# With tar_map2_count_raw():
targets::tar_script({
tarchetypes::tar_map2_count_raw(
name = "x",
command1 = quote(
tibble::tibble(
arg1 = arg1,
arg2 = seq_len(6)
)
),
command2 = quote(
tibble::tibble(
result = paste(arg1, arg2),
random = sample.int(1e9, size = 1),
length_input = length(arg1)
)
),
values = tibble::tibble(arg1 = letters[seq_len(2)]),
batches = 3
)
})
})
}