Targets to render a parameterized R Markdown report
with multiple sets of parameters.
tar_render_rep(
name,
path,
params = data.frame(),
batches = NULL,
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"),
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"),
quiet = TRUE,
...
)
Arguments
name |
Symbol, name of the target. |
path |
Character string, file path to the R Markdown source file.
Must have length 1. |
params |
Code to generate a data frame or tibble
with one row per rendered report
and one column per R Markdown parameter. You may also include an
output_file column to specify the path of each rendered report. |
batches |
Number of batches to group the R Markdown files.
For a large number of reports, increase the number of batches
to decrease target-level overhead. Defaults to the number of
reports to render (1 report per batch). |
packages |
Character vector of packages to load right before
the target builds. 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 . |
format |
Optional storage format for the target's return value.
With the exception of format = "file" , each target
gets a file in _targets/objects , and each format is a different
way to save and load this file.
Possible formats:
"rds" : Default, uses saveRDS() and readRDS() . Should work for
most objects, but slow.
"qs" : Uses qs::qsave() and qs::qread() . Should work for
most objects, much faster than "rds" . Optionally set the
preset for qsave() through the resources argument, e.g.
tar_target(..., resources = list(preset = "archive")) .
"fst" : Uses fst::write_fst() and fst::read_fst() .
Much faster than "rds" , but the value must be
a data frame. Optionally set the compression level for
fst::write_fst() through the resources argument, e.g.
tar_target(..., resources = list(compress = 100)) .
"fst_dt" : Same as "fst" , but the value is a data.table .
Optionally set the compression level the same way as for "fst" .
"fst_tbl" : Same as "fst" , but the value is a tibble .
Optionally set the compression level the same way as for "fst" .
"keras" : Uses keras::save_model_hdf5() and
keras::load_model_hdf5() . The value must be a Keras model.
"torch" : Uses torch::torch_save() and torch::torch_load() .
The value must be an object from the torch package
such as a tensor or neural network module.
"file" : A dynamic file. To use this format,
the target needs to manually identify or save some data
and return a character vector of paths
to the data. Then, targets automatically checks those files and cues
the appropriate build decisions if those files are out of date.
Those paths must point to files or directories,
and they must not contain characters | or * .
All the files and directories you return must actually exist,
or else targets will throw an error. (And if storage is "worker" ,
targets will first stall out trying to wait for the file
to arrive over a network file system.)
"url" : A dynamic input URL. It works like format = "file"
except the return value of the target is a URL that already exists
and serves as input data for downstream targets. Optionally
supply a custom curl handle through the resources argument, e.g.
tar_target(..., resources = list(handle = curl::new_handle())) .
The data file at the URL needs to have an ETag or a Last-Modified
time stamp, or else the target will throw an error because
it cannot track the data. Also, use extreme caution when
trying to use format = "url" to track uploads. You must be absolutely
certain the ETag and Last-Modified time stamp are fully updated
and available by the time the target's command finishes running.
targets makes no attempt to wait for the web server.
"aws_rds" , "aws_qs" , "aws_fst" , "aws_fst_dt" ,
"aws_fst_tbl" , "aws_keras" : AWS-powered versions of the
respective formats "rds" , "qs" , etc. The only difference
is that the data file is uploaded to the AWS S3 bucket
you supply to resources . See the cloud computing chapter
of the manual for details.
"aws_file" : arbitrary dynamic files on AWS S3. The target
should return a path to a temporary local file, then
targets will automatically upload this file to an S3
bucket and track it for you. Unlike format = "file" ,
format = "aws_file" can only handle one single file,
and that file must not be a directory.
tar_read() and downstream targets
download the file to _targets/scratch/ locally and return the path.
_targets/scratch/ gets deleted at the end of tar_make() .
Requires the same resources and other configuration details
as the other AWS-powered formats. See the cloud computing
chapter of the manual for details.
|
iteration |
Character of length 1, name of the iteration mode
of the target. Choices:
"vector" : branching happens with vctrs::vec_slice() and
aggregation happens with vctrs::vec_c() .
"list" , branching happens with [[]] and aggregation happens with
list() .
"group" : dplyr::group_by() -like functionality to branch over
subsets of a data frame. The target's return value must be a data
frame with a special tar_group column of consecutive integers
from 1 through the number of groups. Each integer designates a group,
and a branch is created for each collection of rows in a group.
See the tar_group() function to see how you can
create the special tar_group column with dplyr::group_by() .
|
error |
Character of length 1, what to do if the target
runs into an error. If "stop" , the whole pipeline stops
and throws an error. If "continue" , the error is recorded,
but the pipeline keeps going. |
deployment |
Character of length 1, only relevant to
tar_make_clustermq() and tar_make_future() . If "worker" ,
the target builds on a parallel worker. If "main" ,
the target builds on the host machine / process managing the pipeline. |
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 built earlier.
Only applies to tar_make_future() and tar_make_clustermq()
(not tar_make() ). tar_make_future() with no extra settings is
a drop-in replacement for tar_make() in this case. |
resources |
A named list of computing resources. Uses:
Template file wildcards for future::future() in tar_make_future() .
Template file wildcards clustermq::workers() in tar_make_clustermq() .
Custom target-level future::plan() , e.g.
resources = list(plan = future.callr::callr) .
Custom curl handle if format = "url" ,
e.g. resources = list(handle = curl::new_handle(nobody = TRUE)) .
In custom handles, most users should manually set nobody = TRUE
so targets does not download the entire file when it
only needs to check the time stamp and ETag.
Custom preset for qs::qsave() if format = "qs" , e.g.
resources = list(handle = "archive") .
Custom compression level for fst::write_fst() if
format is "fst" , "fst_dt" , or "fst_tbl" , e.g.
resources = list(compress = 100) .
AWS bucket and prefix for the "aws_" formats, e.g.
resources = list(bucket = "your-bucket", prefix = "folder/name") .
bucket is required for AWS formats. See the cloud computing chapter
of the manual for details.
|
retrieval |
Character of length 1, only relevant to
tar_make_clustermq() and tar_make_future() .
If "main" , the target's dependencies are loaded on the host machine
and sent to the worker before the target builds.
If "worker" , the worker loads the targets dependencies. |
cue |
An optional object from tar_cue() to customize the
rules that decide whether the target is up to date. |
quiet |
An option to suppress printing of the pandoc command line. |
... |
Other named arguments to rmarkdown::render() .
Unlike tar_render() , these arguments are evaluated when the target
is defined, not when it is run. (The only reason to delay evaluation
in tar_render() was to handle R Markdown parameters, and
tar_render_rep() handles them differently.) |
Value
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()
.
Target objects represent skippable steps of the analysis pipeline
as described at https://books.ropensci.org/targets/.
Please see the design specification at
https://books.ropensci.org/targets-design/
to learn about the structure and composition of target objects.
Details
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()
.
Examples