knitr_in()
marks individual knitr
/R Markdown
reports as dependencies. In drake
, these reports are pieces
of the pipeline. R Markdown is a great tool for displaying
precomputed results, but not for running a large workflow
from end to end. These reports should do as little
computation as possible.
Details
Unlike file_in()
and file_out()
, knitr_in()
does not work with entire directories.
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. Usingtarget()
, 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 aknitr
file dependency such as an R Markdown (*.Rmd
) or R LaTeX (*.Rnw
) file.ignore()
: forcedrake
to entirely ignore a piece of code: do not track it for changes and do not analyze it for dependencies.no_deps()
: telldrake
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.
Examples
if (FALSE) { # \dontrun{
isolate_example("contain side effects", {
if (requireNamespace("knitr", quietly = TRUE)) {
# `knitr_in()` is like `file_in()`
# except that it analyzes active code chunks in your `knitr`
# source file and detects non-file dependencies.
# That way, updates to the right dependencies trigger rebuilds
# in your report.
# The mtcars example (`drake_example("mtcars")`)
# already has a demonstration
load_mtcars_example()
make(my_plan)
# Now how did drake magically know that
# `small`, `large`, and `coef_regression2_small` were
# dependencies of the output file `report.md`?
# because the command in the workflow plan had
# `knitr_in("report.Rmd")` in it, so drake knew
# to analyze the active code chunks. There, it spotted
# where `small`, `large`, and `coef_regression2_small`
# were read from the cache using calls to `loadd()` and `readd()`.
}
})
} # }