drake (version 5.3.0)

load_mtcars_example: Load the mtcars example from drake_example("mtcars")

Description

Is there an association between the weight and the fuel efficiency of cars? To find out, we use the mtcars dataset. The mtcars dataset itself only has 32 rows, so we generate two larger bootstrapped datasets and then analyze them with regression models. Finally, we summarize the regression models to see if there is an association.

Usage

load_mtcars_example(envir = parent.frame(), report_file = "report.Rmd",
  overwrite = FALSE, force = FALSE)

Arguments

envir

The environment to load the example into. Defaults to your workspace. For an insulated workspace, set envir = new.env(parent = globalenv()).

report_file

where to write the report file report.Rmd.

overwrite

logical, whether to overwrite an existing file report.Rmd

force

logical, whether to force the loading of a non-back-compatible cache from a previous version of drake.

Value

A drake_config() configuration list.

Details

Use drake_example("mtcars") to get the code for the mtcars example. The included R script is a detailed, heavily-commented walkthrough. The chapter of the user manual at https://ropenscilabs.github.io/drake-manual/mtcars.html # nolint also walks through the mtcars example. This function also writes/overwrites the file, report.Rmd.

Examples

# NOT RUN {
test_with_dir("Quarantine side effects.", {
# Populate your workspace and write 'report.Rmd'.
load_mtcars_example() # Get the code: drake_example("mtcars")
# Check the dependencies of an imported function.
deps_code(reg1)
# Check the dependencies of commands in the workflow plan.
deps_code(my_plan$command[1])
deps_code(my_plan$command[4])
# Plot the interactive network visualization of the workflow.
config <- drake_config(my_plan)
outdated(config) # Which targets are out of date?
# Run the workflow to build all the targets in the plan.
make(my_plan)
outdated(config) # Everything should be up to date.
# For the reg2() model on the small dataset,
# the p-value is so small that there may be an association
# between weight and fuel efficiency after all.
readd(coef_regression2_small)
# Remove the whole cache.
clean(destroy = TRUE)
# Clean up the imported file.
unlink("report.Rmd")
})
# }