Shorthand to include an R Markdown document in a
targets
pipeline (raw version)
Usage
tar_render_raw(
name,
path,
output_file = NULL,
working_directory = NULL,
packages = targets::tar_option_get("packages"),
library = targets::tar_option_get("library"),
error = targets::tar_option_get("error"),
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,
render_arguments = quote(list())
)
Arguments
- name
Character of length 1, name of the target.
- path
Character string, file path to the R Markdown 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.- 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.
- 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
An option to suppress printing during rendering from knitr, pandoc command line and others. To only suppress printing of the last "Output created: " message, you can set
rmarkdown.render.message
toFALSE
- render_arguments
Optional language object with a list of named arguments to
rmarkdown::render()
. Cannot be an expression object. (Usequote()
, notexpression()
.) The reason for quoting is that these arguments may depend on upstream targets whose values are not available at the time the target is defined, and becausetar_render_raw()
is the "raw" version of a function, we want to avoid all non-standard evaluation.
Value
A target object with format = "file"
.
When this target runs, it returns a character vector
of file paths: the rendered document, the source file,
and then the *_files/
directory if it exists.
Unlike rmarkdown::render()
,
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_render_raw()
is just like tar_render()
except that it uses standard evaluation. The name
argument
is a character vector, and the render_arguments
argument
is a language object.
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.
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
.
See also
Other Literate programming targets:
tar_knit()
,
tar_knit_raw()
,
tar_quarto()
,
tar_quarto_raw()
,
tar_quarto_rep()
,
tar_quarto_rep_raw()
,
tar_render()
,
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 R Markdown report:
lines <- c(
"---",
"title: 'report.Rmd source file'",
"output_format: html_document",
"---",
"Assume these lines are in report.Rmd.",
"```{r}",
"targets::tar_read(data)",
"```"
)
# Include the report in the pipeline as follows:
targets::tar_script({
library(tarchetypes)
list(
tar_target(data, data.frame(x = seq_len(26), y = letters)),
tar_render_raw("report", "report.Rmd")
)
}, ask = FALSE)
# Then, run the targets pipeline as usual.
# Parameterized R Markdown:
lines <- c(
"---",
"title: 'report.Rmd source file with parameters.'",
"output_format: html_document",
"params:",
" your_param: \"default value\"",
"---",
"Assume these lines are in report.Rmd.",
"```{r}",
"print(params$your_param)",
"```"
)
# Include this parameterized report in the pipeline as follows.
targets::tar_script({
library(tarchetypes)
list(
tar_target(data, data.frame(x = seq_len(26), y = letters)),
tar_render_raw(
"report",
"report.Rmd",
render_arguments = quote(list(params = list(your_param = data)))
)
)
}, ask = FALSE)
# Then, run the targets pipeline as usual.
})
}