# 04 Advanced `SpaDES` use

#### Alex M. Chubaty

#### Eliot J. B. McIntire

#### August 28 2026

# 1 Advanced `SpaDES` use

This vignette covers tools that become useful once you are building or maintaining a substantial `SpaDES` workflow: monitoring memory use across events, and statically checking that a module’s code agrees with its declared metadata.

## 1.1 Memory monitoring

`profvis::profvis` is great for digging into R internals, but it doesn’t fit the discrete-event pattern very well. Often the question is simpler: which event uses the most memory, and how much? That number sets how many parallel runs you can fit on a machine.

`SpaDES.core` ships a small background sampler. Turn it on by setting the option `spades.memoryUseInterval` to a sampling interval in seconds (e.g., `0.2`). If `future` and `future.callr` are installed, each `spades()` call will then:

1. spawn a `future` (parallel) R session that runs `system('ps')` once per interval, keeping only the row for the main R process;
2. write that to a text file;
3. when `spades()` returns, read the file into `sim@.xData$.memoryUse$obj` and delete the file;
4. [memoryUse()] joins that table to [completed()] by timestamp, so each event is annotated with its memory use.

    if (requireNamespace("future", quietly = TRUE) &&
        requireNamespace("future.callr", quietly = TRUE)) {
      options(spades.memoryUseInterval = 0.5)

      # sim <- simInit(...)
      # sim <- spades(sim)

      memoryUse(sim, max = TRUE)  # peak memory by event type (summarised across times)
      memoryUse(sim, max = FALSE) # peak memory for every individual event
    }

[memoryUseThisSession()] returns the memory currently used by the running R session; useful as a quick sanity check between events.

## 1.2 Static code checking

`simInit()` parses your module’s source and statically checks that the code agrees with the metadata: every read of `sim$x` should have `x` listed in `expectsInput()` or `createsOutput()`, every parameter access (`Par$y`, `P(sim)$y`, `params(sim)$mod$y`) should match a `defineParameter()` entry, every `doEvent.*` function should return `sim`, and so on. Mismatches are reported as a structured table with file/line/column locations.

The checks are controlled by two options:

    options(spades.moduleCodeChecks = TRUE)    # turn checking on/off
    options(spades.codeCheckEngine = "v2")     # "v1" (legacy) or "v2" (new, structured)

You can also run the checks on a module on disk, without going through `simInit()`. This is handy while authoring or refactoring a module:

    # returns a data.frame of findings; also prints a grouped report
    findings <- codeCheckModule("path/to/myModule")

    # subset by severity or rule id
    findings[findings$severity == "warning", ]
    findings[findings$id == "param_used_undeclared", ]

A finding has columns `id`, `severity`, `module`, `where`, `name`, `fn`, `file`, `line`, `col`, `message`, `suggestion`. The suggestion is a plain-language fix (e.g., “add `defineParameter('coverThresh', ...)` to parameters”). When `simInit()` runs the checks, the same data.frame is stashed at `sim@.xData$.codeCheck[[moduleName]]`.

Typical findings the checker flags:

id | what it catches
--- | ---
`out_declared_unused` | output declared in metadata, never assigned in code
`out_used_undeclared` | `sim$x <- ...` for `x` not in `createsOutput()`
`in_declared_unused` | input declared, never read
`in_used_undeclared` | `sim$x` read for `x` not in `expectsInput()`
`param_declared_unused` | `defineParameter("x", ...)` but no `Par$x` in the code
`param_used_undeclared` | `Par$x` / `P(sim)$x` for `x` not declared
`unresolved_accessor` | `sim[[expr]]` or `get(var, envir=envir(sim))` — can’t be checked statically
`must_return_sim` | `doEvent.*` not ending with `return(invisible(sim))`
`must_assign_to_sim` | `scheduleEvent(...)` or `saveFiles(...)` not assigned back to `sim`
`module_named_object` | `sim$<moduleName> <- ...` (forbidden)
`conflicting_fn_unqualified` | bare `levels`/`scale`/`which.max` (ambiguous with `raster::`)

See [codeCheckModule()] for details.

# 2 References