as.data.table.simLists: Coerce elements of a 'simLists' object to a 'data.table'

In PredictiveEcology/SpaDES.experiment: Simulation Experiments Within The SpaDES Ecosystem

Description

This is particularly useful to build plots using the tidyverse, e.g., ggplot2.

Usage

## S3 method for class 'simLists'
as.data.table(x, vals, objectsFromSim = NULL, objectsFromOutputs = NULL, ...)

Arguments

x	An R object.
vals	A (named) list of object names to extract from each simList, or a named list of quoted expressions to calculate for each simList, or a mix of character and quoted expressions.
objectsFromSim	Character vector of objects to extract from the simLists. If omitted, it will extract all objects from each simList in order to calculate the vals. This may have a computational cost. If NA, then no objects will be accessed from the simList. Objects identified here will only be as they are in the simList, i.e., at end(sim).
objectsFromOutputs	List of (named) character vectors of objects to load from the outputs(sim) prior to evaluating vals. If there already is an object with that same name in the simList, then it will be overwritten with the object loaded from outputs(sim). If there are many objects with the same name, specifically from several saveTime values in the outputs(sim), these will all be loaded, one at a time, vals evaluated one at a time, and each of the values will be returned from each saveTime. A column, saveTime, will be part of the returned data.table. For cases where more than one object is required at a given saveTime, all should be identified here, without time specified. This function will take all identified objects from the same time period.
...	Additional arguments. Currently unused.

Details

See examples.

Examples

## Not run: 
  library(SpaDES.core)
  tmpdir <- file.path(tempdir(), "examples")
  # Make 3 simLists -- set up scenarios
  endTime <- 2

  # Example of changing parameter values
  # Make 3 simLists with some differences between them
  mySim <- lapply(c(10, 20, 30), function(nFires) {
    simInit(
      times = list(start = 0.0, end = endTime, timeunit = "year"),
      params = list(
        .globals = list(stackName = "landscape", burnStats = "nPixelsBurned"),
        # Turn off interactive plotting
        fireSpread = list(.plotInitialTime = NA, spreadprob = c(0.2), nFires = c(10)),
        caribouMovement = list(.plotInitialTime = NA),
        randomLandscapes = list(.plotInitialTime = NA, .useCache = "init")
      ),
      modules = list("randomLandscapes", "fireSpread", "caribouMovement"),
      paths = list(modulePath = system.file("sampleModules", package = "SpaDES.core"),
                   outputPath = tmpdir),
      # Save final state of landscape and caribou
      outputs = data.frame(
        objectName = c(rep("landscape", endTime), "caribou", "caribou"),
        saveTimes = c(seq_len(endTime), unique(c(ceiling(endTime / 2), endTime))),
        stringsAsFactors = FALSE
      )
    )
  })

  planTypes <- c("sequential") # try others! ?future::plan
  sims <- experiment2(sim1 = mySim[[1]], sim2 = mySim[[2]], sim3 = mySim[[3]],
                      replicates = 3)

  # Try pulling out values from simulation experiments
  # 2 variables
  df1 <- as.data.table(sims, vals = c("nPixelsBurned", NCaribou = quote(length(caribou$x1))))

  # Now use objects that were saved to disk at different times during spades call
  df1 <- as.data.table(sims,
                       vals = c("nPixelsBurned", NCaribou = quote(length(caribou$x1))),
                       objectsFromOutputs = list(nPixelsBurned = NA, NCaribou = "caribou"))


  # now calculate 4 different values, some from data saved at different times
  # Define new function -- this calculates perimeter to area ratio
  fn <- quote({
    landscape$Fires[landscape$Fires[] == 0] <- NA;
    a <- boundaries(landscape$Fires, type = "inner");
    a[landscape$Fires[] > 0 & a[] == 1] <- landscape$Fires[landscape$Fires[] > 0 & a[] == 1];
    peri <- table(a[]);
    area <- table(landscape$Fires[]);
    keep <- match(names(area),names(peri));
    mean(peri[keep]/area)
  })

  df1 <- as.data.table(sims,
                       vals = c("nPixelsBurned",
                                perimToArea = fn,
                                meanFireSize = quote(mean(table(landscape$Fires[])[-1])),
                                caribouPerHaFire = quote({
                                  NROW(caribou) /
                                    mean(table(landscape$Fires[])[-1])
                                })),
                       objectsFromOutputs = list(NA, c("landscape"), c("landscape"),
                                                 c("landscape", "caribou")),
                       objectsFromSim = "nPixelsBurned")

  if (interactive()) {
    # with an unevaluated string
    library(ggplot2)
    p <- lapply(unique(df1$vals), function(var) {
      ggplot(df1[vals == var,],
             aes(x = saveTime, y = value, group = simList, color = simList)) +
        stat_summary(geom = "point", fun.y = mean) +
        stat_summary(geom = "line", fun.y = mean) +
        stat_summary(geom = "errorbar", fun.data = mean_se, width = 0.2) +
        ylab(var)
    })

    # Arrange all 4 -- could use gridExtra::grid.arrange -- easier
    pushViewport(viewport(layout = grid.layout(2, 2)))
    vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y)
    print(p[[1]], vp = vplayout(1, 1))
    print(p[[2]], vp = vplayout(1, 2))
    print(p[[3]], vp = vplayout(2, 1))
    print(p[[4]], vp = vplayout(2, 2))
  }

## End(Not run)

PredictiveEcology/SpaDES.experiment documentation built on Dec. 3, 2019, 4:59 p.m.