Shorthand to include knitr
document in a
targets
pipeline.
Usage
tar_knit(
name,
path,
output_file = NULL,
working_directory = NULL,
tidy_eval = targets::tar_option_get("tidy_eval"),
packages = targets::tar_option_get("packages"),
library = targets::tar_option_get("library"),
error = targets::tar_option_get("error"),
memory = targets::tar_option_get("memory"),
garbage_collection = targets::tar_option_get("garbage_collection"),
deployment = "main",
priority = targets::tar_option_get("priority"),
resources = targets::tar_option_get("resources"),
retrieval = targets::tar_option_get("retrieval"),
cue = targets::tar_option_get("cue"),
description = targets::tar_option_get("description"),
quiet = TRUE,
...
)
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.
tar_target(downstream_target, f(upstream_target))
is a target nameddownstream_target
which depends on a targetupstream_target
and a functionf()
. In addition, a target's name determines its random number generator seed. 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 withtar_meta(your_target, seed)
and runtar_seed_set()
on the result to locally recreate the target's initial RNG state.- path
Character string, file path to the
knitr
source file. Must have length 1.- output_file
Character string, file path to the rendered output file.
- working_directory
Optional character string, path to the working directory to temporarily set when running the report. The default is
NULL
, which runs the report from the current working directory at the time the pipeline is run. This default is recommended in the vast majority of cases. To use anything other thanNULL
, you must manually set the value of thestore
argument relative to the working directory in all calls totar_read()
andtar_load()
in the report. Otherwise, these functions will not know where to find the data.- tidy_eval
Logical, whether to enable tidy evaluation when interpreting
command
andpattern
. IfTRUE
, you can use the "bang-bang" operator!!
to programmatically insert the values of global objects.- 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
.- 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."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.)"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.
- 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.- 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"
.- quiet
Boolean; suppress the progress bar and messages?
- ...
Named arguments to
knitr::knit()
. These arguments are evaluated when the target actually runs intar_make()
, not when the target is defined.
Value
A tar_target()
object with format = "file"
.
When this target runs, it returns a character vector
of file paths. The first file paths are the output files
(returned by knitr::knit()
) and the knitr
source file is last. But unlike knitr::knit()
,
all returned paths are relative paths to ensure portability
(so that the project can be moved from one file system to another
without invalidating the target).
See the "Target objects" section for background.
Details
tar_knit()
is an alternative to tar_target()
for
knitr
reports that depend on other targets. The knitr
source
should mention dependency targets with tar_load()
and tar_read()
in the active code chunks (which also allows you to knit the report
outside the pipeline if the _targets/
data store already exists).
(Do not use tar_load_raw()
or tar_read_raw()
for this.)
Then, tar_knit()
defines a special kind of target. It
1. Finds all the tar_load()
/tar_read()
dependencies in the report
and inserts them into the target's command.
This enforces the proper dependency relationships.
(Do not use tar_load_raw()
or tar_read_raw()
for this.)
2. Sets format = "file"
(see tar_target()
) so targets
watches the files at the returned paths and reruns the report
if those files change.
3. Configures the target's command to return both the output
report files and the input source file. All these file paths
are relative paths so the project stays portable.
4. Forces the report to run in the user's current working directory
instead of the working directory of the report.
5. Sets convenient default options such as deployment = "main"
in the target and quiet = TRUE
in knitr::knit()
.
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.
See also
Other Literate programming targets:
tar_knit_raw()
,
tar_quarto()
,
tar_quarto_raw()
,
tar_quarto_rep()
,
tar_quarto_rep_raw()
,
tar_render()
,
tar_render_raw()
,
tar_render_rep()
,
tar_render_rep_raw()
Examples
if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
# Ordinarily, you should create the report outside
# tar_script() and avoid temporary files.
lines <- c(
"---",
"title: report",
"output_format: html_document",
"---",
"",
"```{r}",
"targets::tar_read(data)",
"```"
)
path <- tempfile()
writeLines(lines, path)
list(
targets::tar_target(data, data.frame(x = seq_len(26), y = letters)),
tarchetypes::tar_knit(report, path)
)
})
targets::tar_make()
})
}