Reproducible pipeline

…This places an obligation on all creators of software to program in such a way that the computations can be understood and trusted. This obligation I label the Prime Directive.

  • John Chambers (Software for Data Analysis: Programming with R)

Adaptive management

  • Ideally, we can rerun all decision support tools over and over again
  • Must be MUCH easier the second, third, etc. time
  • If there is an incremental change in input data, then we shouldn’t need to redo the same effort as the first time around.

Why a Discrete Event Simulator is necessary?

  • There is much fanfare about using Rmarkdown as a tool for reproducibility (i.e, Rmarkdown = “code, output, narrative”)
  • But this allows exact reproducibility

    • Everything will be run exactly as before
  • What happens if the goal is to run it again, but with slight changes to the input data?
  • The Rmarkdown script approach, which is very linear, can deal with this to a certain extent, but it will be clunky

Adding novelty

  • What happens if you want to add something new, but keep it mostly the same
  • You have a good vegetation model and a good caribou model, but you want to add a wolf model
  • With Rmarkdown, you would have to edit the original file
  • With SpaDES, you can use the other modules by calling them… no need to know how they work
  • You add your own wolf model, looking at outputs of the vegetation model and caribou model
  • Using the model metadata from those other modules

Deploying for policy and management

Need the reusable work flow, and reproducible science … but with a GUI

  • shine function is a start
  • we created a demo version of a generic web interface for models (link)
  • A non-scientist can ask questions of a system of modules without knowing which modules are necessary


  • For the above to work for, say, a national question, it can’t run the model on the fly
  • Could have massive data requirements, long simulation times, even on supercomputers
  • Caching allows for near instant results from very complex systems

Nested Caching

  • Imagine we have large model, with many modules, with replication and alternative module collections (e.g., alternative fire models)
  • To run this would have a nested structure with the following functions:
simInit --> many .inputObjects calls

experiment --> many spades calls --> many module calls --> many event calls --> many function calls

Lets say we start to introduce caching to this structure. We start from the “inner” most functions that we could imaging Caching would be useful. Lets say there are some GIS operations, like raster::projectRaster, which operates on an input shapefile. We can Cache the projectRaster call to make this much faster, since it will always be the same result for a given input raster.

If we look back at our structure above, we see that we still have LOTS of places that are not Cached. That means that the experiment call will still spawn many spades calls, which will still spawn many module calls, and many event calls, just to get to the one Cache(projectRaster) call which is Cached. This function will likely be called hundreds of times (because experiment runs the spades call 100 times due to replication). This is good, but Cache does take some time. So, even if Cache(projectRaster) takes only 0.02 seconds, calling it hundreds of times means maybe 4 seconds. If we are doing this for many functions, then this will be too slow.

We can start putting Cache all up the sequence of calls. Unfortunately, the way we use Cache at each of these levels is a bit different, so we need a slightly different approach for each.

Cache the experiment call


This will assess the simList (the objects, times, modules, etc.) and if they are all the same, it will return the final list of simLists that came from the first experiment call. NOTE: because this can be large, it is likely that you want clearSimEnv = TRUE, and have all objects that are needed after the experiment call saved to disk. Any stochasticity/randomness inside modules will be frozen. This is likely ok if the objective is to show results in a web app (via shiny or otherwise) or another visualization about the experiment outputs, e.g., comparing treatments, once sufficient stochasticity has been achieved.

mySimListOut <- Cache(experiment, mySim, clearSimEnv = TRUE)

Cache the spades calls inside experiment

experiment(cache = TRUE)

This will cache each of the spades calls inside the experiment call. That means that there are as many cache events as there are replicates and experimental treatments, which, again could be a lot. Like caching the experiment call, stochasticity/randomness will be frozen. Note, one good use of this is when you are making iterative, incremental replication, e.g.,

mySimOut <- experiment(mySim, replicates = 5, cache = TRUE)

You decide after waiting 10 minutes for it to finish, that you need more replication. Rather than start from zero replicates, you can just pick up where you left off:

mySimOut <- experiment(mySim, replicates = 10, cache = TRUE)

This will only add 5 more replicates.

Cache a whole module

Pass .useCache = TRUE as a parameter to the module, during the simInit

Some modules are inherently non-random, such as GIS modules, or parameter fitting statistical modules. We expect these to be identical results each time, so we can safely cache the entire module.

parameters = list(
  FireModule = list(.useCache = TRUE)
mySim <- simInit(..., params = parameters)
mySimOut <- spades(mySim)

The messaging should indicate the caching is happening on every event in that module.

Note: This option REQUIRES that the metadata in inputs and outputs be exactly correct, i.e., all inputObjects and outputObjects must be correctly identified and listed in the defineModule metadata

If the module is cached, and there are errors when it is run, it almost is garanteed to be a problem with the inputObjects and outputObjects incorrectly specified.

Cache individual functions

Cache(<functionName>, <other arguments>)

This will allow fine scale control of individual function calls.


Once nested Caching is used all the way up to the experiment level and even further up (e.g., if there is a shiny module), then even very complex models can be put into a complete workflow.

The current vision for SpaDES is that it will allow this type of “data to decisions” complete workflow that allows for deep, robust models, across disciplines, with easily accessible front ends, that are quick and responsive to users, yet can handle data changes, module changes, etc.

Bringing the best science, data, models into the hands of policy makers in real time, on their phones