Shorthand to include a Quarto project in a
targets pipeline.
tar_quarto() expects an unevaluated symbol for the name
argument and an unevaluated expression for the exectue_params argument.
tar_quarto_raw() expects a character string for the name
argument and an evaluated expression object
for the exectue_params argument.
tar_quarto(
name,
path = ".",
output_file = NULL,
working_directory = NULL,
extra_files = character(0),
execute = TRUE,
execute_params = list(),
cache = NULL,
cache_refresh = FALSE,
debug = FALSE,
quiet = TRUE,
quarto_args = NULL,
pandoc_args = NULL,
profile = NULL,
tidy_eval = targets::tar_option_get("tidy_eval"),
packages = NULL,
library = NULL,
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")
)tar_quarto_raw(
name,
path = ".",
output_file = NULL,
working_directory = NULL,
extra_files = character(0),
execute = TRUE,
execute_params = NULL,
cache = NULL,
cache_refresh = FALSE,
debug = FALSE,
quiet = TRUE,
quarto_args = NULL,
pandoc_args = NULL,
profile = NULL,
packages = NULL,
library = NULL,
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")
)
A target object with format = "file".
When this target runs, it returns a character vector
of file paths: the rendered documents, the Quarto source files,
and other input and output files.
The output files are determined by the YAML front-matter of
standalone Quarto documents and _quarto.yml in Quarto projects,
and you can see these files with tar_quarto_files()
(powered by quarto::quarto_inspect()).
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.
Name of the target.
tar_quarto() expects an unevaluated symbol for the name
argument, and
tar_quarto_raw() expects a character string for name.
Character string, path to the Quarto source file if rendering a single file, or the path to the root of the project if rendering a whole Quarto project.
The name of the output file. If using NULL, the output
filename will be based on the filename for the input file. output_file is
mapped to the --output option flag of the quarto CLI. It is expected to
be a filename only, not a path, relative or absolute.
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 than NULL, you must manually set the value
of the store argument relative to the working directory in all calls
to tar_read() and tar_load() in the report. Otherwise,
these functions will not know where to find the data.
Character vector of extra files and
directories to track for changes. The target will be invalidated
(rerun on the next tar_make()) if the contents of these files changes.
No need to include anything already in the output of tar_quarto_files(),
the list of file dependencies automatically detected through
quarto::quarto_inspect().
Whether to execute embedded code chunks.
Named collection of parameters
for parameterized Quarto documents. These parameters override the custom
custom elements of the params list in the YAML front-matter of the
Quarto source files.
tar_quarto() expects an unevaluated expression for the
exectue_params argument, whereas
tar_quarto_raw() expects an evaluated expression object.
Cache execution output (uses knitr cache and jupyter-cache respectively for Rmd and Jupyter input files).
Force refresh of execution cache.
Leave intermediate files in place after render.
Suppress warning and other messages.
Character vector of other quarto CLI arguments to append
to the Quarto command executed by this function. This is mainly intended for
advanced usage and useful for CLI arguments which are not yet mirrored in a
dedicated parameter of this R function. See quarto render --help for options.
Additional command line arguments to pass on to Pandoc.
Quarto project profile(s) to use. Either
a character vector of profile names or NULL to use the default profile.
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 runs or the output data is reloaded for
downstream targets. Use tar_option_set() to set packages
globally for all subsequent targets you define.
Character vector of library paths to try
when loading packages.
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 returns NULL.
The data hash is deliberately wrong so the target is not
up to date for the next run of the pipeline. In addition,
as of targets version 1.8.0.9011, a value of NULL is given
to upstream dependencies with error = "null" if loading fails.
"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 like error = "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.)
Character of length 1, memory strategy. Possible values:
"auto": new in targets version 1.8.0.9011, memory = "auto"
is equivalent to memory = "transient" for dynamic branching
(a non-null pattern argument) and memory = "persistent"
for targets that do not use dynamic branching.
"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).
"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" with repository = "aws"),
the memory option 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.
Logical: TRUE to run base::gc()
just before the target runs,
FALSE to omit garbage collection.
In the case of high-performance computing,
gc() runs both locally and on the parallel worker.
All this garbage collection is skipped if the actual target
is skipped in the pipeline.
Non-logical values of garbage_collection are converted to TRUE or
FALSE using isTRUE(). In other words, non-logical values are
converted FALSE. For example, garbage_collection = 2
is equivalent to garbage_collection = FALSE.
Character of length 1. If deployment is
"main", then the target will run on the central controlling R process.
Otherwise, if deployment 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 in targets, please visit
https://books.ropensci.org/targets/crew.html.
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()).
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 string to control when the current target
loads its dependencies into memory before running.
(Here, a "dependency" is another target upstream that the current one
depends on.) Only relevant when using targets
with parallel workers (https://books.ropensci.org/targets/crew.html).
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 target's dependencies.
"none": targets makes no attempt to load its
dependencies. With retrieval = "none", loading dependencies
is the responsibility of the user. Use with caution.
An optional object from tar_cue() to customize the
rules that decide whether the target is up to date.
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() and tar_visnetwork(),
and they let you select subsets of targets for the names argument of
functions like tar_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".
If you encounter difficult errors, please read
https://github.com/quarto-dev/quarto-r/issues/16.
In addition, please try to reproduce the error using
quarto::quarto_render("your_report.qmd", execute_dir = getwd())
without using targets at all. Isolating errors this way
makes them much easier to solve.
Literate programming files are messy and variable,
so functions like tar_render() have limitations:
* Child documents are not tracked for changes.
* Upstream target dependencies are not detected if tar_read()
and/or tar_load() are called from a user-defined function.
In addition, single target names must be mentioned and they must
be symbols. tar_load("x") and tar_load(contains("x")) may not
detect target x.
* Special/optional input/output files may not be detected in all cases.
* tar_render() and friends are for local files only. They do not
integrate with the cloud storage capabilities of targets.
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.
tar_quarto() is an alternative to tar_target() for
Quarto projects and standalone Quarto source documents
that depend on upstream targets. The Quarto
R source documents (*.qmd and *.Rmd files)
should mention dependency targets with tar_load() and tar_read()
in the active R code chunks (which also allows you to render the project
outside the pipeline if the _targets/ data store already exists).
(Do not use tar_load_raw() or tar_read_raw() for this.)
Then, tar_quarto() defines a special kind of target. It
1. Finds all the tar_load()/tar_read() dependencies in the
R source reports 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
rendered files and the input dependency files (such as
Quarto source documents). 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 quarto::quarto_render().
Other Literate programming targets:
tar_knit(),
tar_quarto_rep(),
tar_render(),
tar_render_rep()
if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
# Unparameterized Quarto document:
lines <- c(
"---",
"title: report.qmd source file",
"output_format: html",
"---",
"Assume these lines are in report.qmd.",
"```{r}",
"targets::tar_read(data)",
"```"
)
writeLines(lines, "report.qmd")
# Include the report in a pipeline as follows.
targets::tar_script({
library(tarchetypes)
list(
tar_target(data, data.frame(x = seq_len(26), y = letters)),
tar_quarto(name = report, path = "report.qmd")
)
}, ask = FALSE)
# Then, run the pipeline as usual.
# Parameterized Quarto:
lines <- c(
"---",
"title: 'report.qmd source file with parameters'",
"output_format: html_document",
"params:",
" your_param: \"default value\"",
"---",
"Assume these lines are in report.qmd.",
"```{r}",
"print(params$your_param)",
"```"
)
writeLines(lines, "report.qmd")
# Include the report in the pipeline as follows.
unlink("_targets.R") # In tar_dir(), not the user's file space.
targets::tar_script({
library(tarchetypes)
list(
tar_target(data, data.frame(x = seq_len(26), y = letters)),
tar_quarto(
name = report,
path = "report.qmd",
execute_params = list(your_param = data)
),
tar_quarto_raw(
name = "report2",
path = "report.qmd",
execute_params = quote(list(your_param = data))
)
)
}, ask = FALSE)
})
# Then, run the pipeline as usual.
}
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