View source: R/tar_render_rep.R
tar_render_rep | R Documentation |
Targets to render a parameterized R Markdown report with multiple sets of parameters.
tar_render_rep(
name,
path,
working_directory = NULL,
params = data.frame(),
batches = NULL,
rep_workers = 1,
packages = targets::tar_option_get("packages"),
library = targets::tar_option_get("library"),
format = targets::tar_option_get("format"),
iteration = targets::tar_option_get("iteration"),
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"),
retrieval = targets::tar_option_get("retrieval"),
cue = targets::tar_option_get("cue"),
description = targets::tar_option_get("description"),
quiet = TRUE,
...
)
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. |
path |
Character string, file path to the R Markdown source file. Must have length 1. |
working_directory |
Optional character string,
path to the working directory
to temporarily set when running the report.
The default is |
params |
Code to generate a data frame or |
batches |
Number of batches. This is also the number of dynamic
branches created during |
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. |
packages |
Character vector of packages to load right before
the target runs or the output data is reloaded for
downstream targets. Use |
library |
Character vector of library paths to try
when loading |
format |
Optional storage format for the target's return value.
With the exception of |
iteration |
Character of length 1, name of the iteration mode of the target. Choices:
|
error |
Character of length 1, what to do if the target stops and throws an error. Options:
|
memory |
Character of length 1, memory strategy.
If |
garbage_collection |
Logical, whether to run |
deployment |
Character of length 1. If |
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 |
resources |
Object returned by |
retrieval |
Character of length 1, only relevant to
|
cue |
An optional object from |
description |
Character of length 1, a custom free-form human-readable
text description of the target. Descriptions appear as target labels
in functions like |
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 |
... |
Other named arguments to |
tar_render_rep()
is an alternative to tar_target()
for
parameterized R Markdown reports that depend on other targets.
Parameters must be given as a data frame with one row per
rendered report and one column per parameter. An optional
output_file
column may be included to set the output file path
of each rendered report.
The R Markdown source should mention other dependency targets
tar_load()
and tar_read()
in the active code chunks
(which also allows you to render the report
outside the pipeline if the _targets/
data store already exists
and appropriate defaults are specified for the parameters).
(Do not use tar_load_raw()
or tar_read_raw()
for this.)
Then, tar_render()
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 the output
report files: the rendered document, the source file,
and then the *_files/
directory if it exists. 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 rmarkdown::render()
.
A list of target objects to render the R Markdown
reports. Changes to the parameters, source file, dependencies, etc.
will cause the appropriate targets to rerun during tar_make()
.
See the "Target objects" section for background.
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.
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.
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
.
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_raw()
,
tar_render_rep_raw()
if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
# Parameterized R Markdown:
lines <- c(
"---",
"title: 'report.Rmd file'",
"output_format: html_document",
"params:",
" par: \"default value\"",
"---",
"Assume these lines are in a file called report.Rmd.",
"```{r}",
"print(params$par)",
"```"
)
# The following pipeline will run the report for each row of params.
targets::tar_script({
library(tarchetypes)
list(
tar_render_rep(
report,
"report.Rmd",
params = tibble::tibble(par = c(1, 2))
)
)
}, ask = FALSE)
# Then, run the targets pipeline as usual.
})
}
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