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This is all a hack: use at your own risk. This is pretty heavily tailored around a specific need that we have

What is this for?

This is to address a pattern in computational research. Suppose you have a set of parameters that you need to run a simulation on each of -- some sort of embarrassingly parallel job. You might be running this on your own computer or on a cluster, which might have a scheduler, which in turn requires you write a little bash script to launch it. This gets really annoying as you recreate parameter lists in a shell file and propagate them through R's less-than-awesome command line passing tools.


  • A project may have multiple experiments in it.
  • Each experiment may have multiple tasks.
  • Each experiment has a set of parameters that are common to all tasks. Each task will be run on each set of parameters.

The set up

Run experimentr::create_dirs(). This creates directories:

  • experments/parameters/
  • experiments/output/

The first directory will hold sets of parameters associated with each experiment, while the second holds generated output.

Next, suppose you have some long-running function, say target_fn, defined in file simulation.R:

target_fn <- function(a, b) {
  list(a=a, b=b)

This is obviously silly, but perhaps this runs a long running set of calculations. You want to run this function over a large set of parameters, which you'll also need to provide. expand.grid is useful here. Something like:

pars <- expand.grid(a=1:10, b=letters[1:5])

Generates 100 parameter combination with pairwise combinations of the integers 1 to 10 and the letters "a" to "e". Set up the experiment:

setup_experiment("trial", pars, scripts="simulation.R")

This creates a bunch of directories, and a file with the parameters in it. We can add a task corresponding to the target function above:

add_task("trial", "testing", "target_fn")

Then, running

main(list(experiment="trial", task="testing", id=1))

runs the first set of parameters from this set.


By default, we save package version information for all packages installed (via sessionInfo(), plus the status of the git repository that the project is run in (SHA plus a list of files unknown to git or modified), plus the system information (via If extra information is required, an experiment can have a key metadata that refers to a function that takes no arguments and returns a list of other metadata.