Shorthand to include a Quarto project in a
targets
pipeline.
Usage
tar_quarto(
name,
path = ".",
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")
)
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 of length 1, either the single
*.qmd
source file to be rendered or a directory containing a Quarto project. Defaults to the working directory of thetargets
pipeline. Passed directly to theinput
argument ofquarto::quarto_render()
.- 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.- extra_files
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 oftar_quarto_files()
, the list of file dependencies automatically detected throughquarto::quarto_inspect()
.- execute
Whether to execute embedded code chunks.
- execute_params
Code, cannot be
NULL
.execute_params
evaluates to a named list of parameters for parameterized Quarto documents. These parameters override the custom custom elements of theparams
list in the YAML front-matter of the Quarto source files. The list is quoted (not evaluated until the target runs) so that upstream targets can serve as parameter values.- cache
Cache execution output (uses knitr cache and jupyter-cache respectively for Rmd and Jupyter input files).
- cache_refresh
Force refresh of execution cache.
- debug
Leave intermediate files in place after render.
- quiet
Suppress warning and other messages.
- quarto_args
Character vector of other
quarto
CLI flag pass to the command. This is mainly for advanced usage, e.g it can be useful for new options added to quarto CLI and not yet supported as function argument.- pandoc_args
Additional command line options to pass to pandoc.
- profile
Character of length 1, Quarto profile. If
NULL
, the default profile will be used. Requires Quarto version 1.2 or higher. See https://quarto.org/docs/projects/profiles.html for details.- 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"
.
Value
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.
Details
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()
.
Quarto troubleshooting
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 limitations
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
.
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()
,
tar_knit_raw()
,
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.
# 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(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(
report,
path = "report.qmd",
execute_params = list(your_param = data)
)
)
}, ask = FALSE)
})
# Then, run the pipeline as usual.
}