tar_eval_raw {tarchetypes}

Evaluate multiple expressions created with symbol substitution (raw version).

Description

Loop over a grid of values, create an expression object from each one, and then evaluate that expression. Helps with general metaprogramming. Unlike tar_sub(), which quotes the expr argument, tar_sub_raw() assumes expr is an expression object.

Usage

tar_eval_raw(expr, values, envir = parent.frame())

Arguments

expr

Expression object with the starting expression. Values are iteratively substituted in place of symbols in expr to create each new expression, and then each expression is evaluated.

values

List of values to substitute into expr to create the expressions. All elements of values must have the same length.

envir

Environment in which to evaluate the new expressions.

Value

A list of return values from evaluating the expression objects. Often, these values are target objects. See the "Target objects" section for background on target objects specifically.

Target objects

Most tarchetypes functions are target factories, which means they return target objects or lists of target objects. Target objects represent skippable steps of the analysis pipeline as described at https://books.ropensci.org/targets/. Please read the walkthrough at https://books.ropensci.org/targets/walkthrough.html to understand the role of target objects in analysis pipelines.

For developers, https://wlandau.github.io/targetopia/contributing.html#target-factories explains target factories (functions like this one which generate targets) and the design specification at https://books.ropensci.org/targets-design/ details the structure and composition of target objects.

See Also

Other Metaprogramming utilities: tar_eval(), tar_sub(), tar_sub_raw()

Examples

# tar_map() is incompatible with tar_render() because the latter
# operates on preexisting tar_target() objects. By contrast,
# tar_eval_raw() and tar_sub_raw() iterate over code farther upstream.
values <- list(
  name = lapply(c("name1", "name2"), as.symbol),
  file = c("file1.Rmd", "file2.Rmd")
)
tar_sub_raw(quote(list(name, file)), values = values)
tar_sub_raw(quote(tar_render(name, file)), values = values)
path <- tempfile()
file.create(path)
str(tar_eval_raw(quote(tar_render(name, path)), values = values))
# So in your _targets.R file, you can define a pipeline like as below.
# Just make sure to set a unique name for each target
# (which tar_map() does automatically).
values <- list(
  name = lapply(c("name1", "name2"), as.symbol),
  file = c(path, path)
)
list(
  tar_eval_raw(quote(tar_render(name, file)), values = values)
)