file_out | R Documentation |
file_out()
marks individual files
(and whole directories) that your targets create.
file_out(...)
... |
Character vector, paths to files and directories. Use
|
A character vector of declared output file or directory paths.
drake_plan()
understands special keyword functions for your commands.
With the exception of target()
, each one is a proper function
with its own help file.
target()
: give the target more than just a command.
Using target()
, you can apply a transformation
(examples: https://books.ropensci.org/drake/plans.html#large-plans
), # nolint
supply a trigger (https://books.ropensci.org/drake/triggers.html
), # nolint
or set any number of custom columns.
file_in()
: declare an input file dependency.
file_out()
: declare an output file to be produced
when the target is built.
knitr_in()
: declare a knitr
file dependency such as an
R Markdown (*.Rmd
) or R LaTeX (*.Rnw
) file.
ignore()
: force drake
to entirely ignore a piece of code:
do not track it for changes and do not analyze it for dependencies.
no_deps()
: tell drake
to not track the dependencies
of a piece of code. drake
still tracks the code itself for changes.
id_chr()
: Get the name of the current target.
drake_envir()
: get the environment where drake builds targets.
Intended for advanced custom memory management.
file_in()
, knitr_in()
, ignore()
, no_deps()
## Not run:
isolate_example("contain side effects", {
# The `file_out()` and `file_in()` functions
# just takes in strings and returns them.
file_out("summaries.txt")
# Their main purpose is to orchestrate your custom files
# in your workflow plan data frame.
plan <- drake_plan(
out = write.csv(mtcars, file_out("mtcars.csv")),
contents = read.csv(file_in("mtcars.csv"))
)
plan
# drake knows "\"mtcars.csv\"" is the first target
# and a dependency of `contents`. See for yourself:
make(plan)
file.exists("mtcars.csv")
# You may use `.id_chr` inside `file_out()` and `file_in()`
# to refer to the current target. This works inside `map()`,
# `combine()`, `split()`, and `cross()`.
plan <- drake::drake_plan(
data = target(
write.csv(data, file_out(paste0(.id_chr, ".csv"))),
transform = map(data = c(airquality, mtcars))
)
)
plan
# You can also work with entire directories this way.
# However, in `file_out("your_directory")`, the directory
# becomes an entire unit. Thus, `file_in("your_directory")`
# is more appropriate for subsequent steps than
# `file_in("your_directory/file_inside.txt")`.
plan <- drake_plan(
out = {
dir.create(file_out("dir"))
write.csv(mtcars, "dir/mtcars.csv")
},
contents = read.csv(file.path(file_in("dir"), "mtcars.csv"))
)
plan
make(plan)
file.exists("dir/mtcars.csv")
# See the connections that the file relationships create:
if (requireNamespace("visNetwork", quietly = TRUE)) {
vis_drake_graph(plan)
}
})
## End(Not run)
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