file_out() marks individual files
(and whole directories) that your targets create.
Arguments
- ...
- Character vector, paths to files and directories. Use - .id_chrto refer to the current target by name.- .id_chris not limited to use in- file_in()and- file_out().
Keywords
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- knitrfile dependency such as an R Markdown (- *.Rmd) or R LaTeX (- *.Rnw) file.
- ignore(): force- draketo entirely ignore a piece of code: do not track it for changes and do not analyze it for dependencies.
- no_deps(): tell- draketo not track the dependencies of a piece of code.- drakestill 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.
Examples
if (FALSE) { # \dontrun{
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)
}
})
} # }