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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.





Character strings. File paths of knitr/rmarkdown source files supplied to a command in your workflow plan data frame.


A character vector of declared input file paths.


Unlike file_in() and file_out(), knitr_in() does not work with entire directories.


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:, # nolint supply a trigger (, # 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.


if (FALSE) {
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


# Now how did drake magically know that
# `small`, `large`, and `coef_regression2_small` were
# dependencies of the output file ``?
# 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()`.