Is there an association between the weight and the fuel efficiency of cars? To find out, we use the mtcars example from drake_example("mtcars"). 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.

  envir = parent.frame(),
  report_file = NULL,
  overwrite = FALSE,
  force = FALSE



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


Where to write the report file. Deprecated. In a future release, the report file will always be report.Rmd and will always be written to your working directory (current default).


Logical, whether to overwrite an existing file report.Rmd.






Use drake_example("mtcars") to get the code for the mtcars example. This function also writes/overwrites the file, report.Rmd.

See also


if (FALSE) { isolate_example("Quarantine side effects.", { if (suppressWarnings(require("knitr"))) { # 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. outdated(my_plan) # Which targets are out of date? # Run the workflow to build all the targets in the plan. make(my_plan) outdated(my_plan) # Everything should be up to date.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) # Clean up the example. clean_mtcars_example() } }) }