Loop over a grid of values, create an expression object from each one, and then evaluate that expression. Helps with general metaprogramming.
Starting expression. Values are iteratively substituted
in place of symbols in
List of values to substitute into
Environment in which to evaluate the new expressions.
A list of return values from the generated expression objects. Often, these values are target objects. See the "Target objects" section for background on target objects specifically.
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
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.
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# tar_map() is incompatible with tar_render() because the latter # operates on preexisting tar_target() objects. By contrast, # tar_eval() and tar_sub() iterate over the literal code # farther upstream. values <- list( name = lapply(c("name1", "name2"), as.symbol), file = list("file1.Rmd", "file2.Rmd") ) tar_sub(list(name, file), values = values) tar_sub(tar_render(name, file), values = values) path <- tempfile() file.create(path) str(tar_eval(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(tar_render(name, file), values = values) )
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