# Exporting objects and functions from the workspace

#### Phil Chalmers

#### 2025-03-28

# 1 Including fixed objects

R is fun language for computer programming and statistics, but it’s not without it’s quirks. For instance, R generally has a recursive strategy when attempting to find objects within functions. If an object can’t be found, R will start to look outside the function’s environment to see if the object can be located there, and if not, look within even higher-level environments… This recursive search continues until it searches for the object in the user workspace/Global environment, and only when the object can’t be found here will an error be thrown. This is a strange feature to most programmers who come from other languages, and when writing simulations may cause some severely unwanted masking issues. This tutorial demonstrates how to make sure all required user-defined objects are visible to `SimDesign`.

# 2 Scoping

To demonstrate the issue, let’s define two objects and a function which uses these objects.

    obj1 <- 10
    obj2 <- 20

When evaluated, these objects are visible to the user, and can be seen by typing in the R console by typing `ls()`. Functions which do not define objects with the same name will also be able to locate these values.

    myfun <- function(x) obj1 + obj2
    myfun(1)

    ## [1] 30

This behavior is indeed a bit strange, but it’s one of R’s quirks. Unfortunately, when running code in parallel across different cores these objects will not be visible, and therefore must be exported using other methods (e.g., in the `parallel` package this is done with `clusterExport()`).

    library(parallel)
    cl <- makeCluster(2)
    res <- try(parSapply(cl=cl, 1:4, myfun))
    res

    ## Error in checkForRemoteErrors(val) :
    ##   2 nodes produced errors; first error: object 'obj1' not found

Exporting the objects to the cluster fixes the issue.

    clusterExport(cl=cl, c('obj1', 'obj2'))
    parSapply(cl=cl, 1:4, myfun)

    ## [1] 30 30 30 30

The same reasoning above applies to functions defined in the R work-space as well, including functions defined within third-party R packages. Hence, in order to use functions from other packages they must either be explicitly loaded with `require()` or `library()` within the distributed code, or referenced via their Namespace with the `::` operator (e.g., `mvtnorm::rmvtnorm()`).

# 3 Exporting objects example in `SimDesign`

In order to make objects safely visible in `SimDesign` the strategy is very simple: wrap all desired objects into a named list (or other object), and pass this to the `fixed_objects` argument. From here, elements can be indexed using the `$` operator or `with()` function, or whatever other method may be convenient. Note, however, this is only required for defined objects not functions — `SimDesign` automatically makes user-defined functions available across all nodes.

As an aside, an alternative approach is simply to define/source the objects within the respective `SimDesign` functions; that way they will clearly be visible at run-time. The following `fixed_objects` approach is really only useful when the defined objects contain a large amount of code.

    library(SimDesign)
    #SimFunctions(comments = FALSE)

    ### Define design conditions and number of replications
    Design <- createDesign(N = c(10, 20, 30))
    replications <- 1000

    # define custom functions and objects (or use source() to read these in from an external file)
    SD <- 2
    my_gen_fun <- function(n, sd) rnorm(n, sd = sd)
    my_analyse_fun <- function(x) c(p = t.test(x)$p.value)
    fixed_objects <- list(SD=SD)

    #---------------------------------------------------------------------------

    Generate <- function(condition, fixed_objects) {
        Attach(condition) # make condition names available (e.g., N)

        # further, can use with() to use 'SD' directly instead of 'fixed_objects$SD'
        ret <- with(fixed_objects, my_gen_fun(N, sd=SD))
        ret
    }

    Analyse <- function(condition, dat, fixed_objects) {
        ret <- my_analyse_fun(dat)
        ret
    }

    Summarise <- function(condition, results, fixed_objects) {
        ret <- EDR(results, alpha = .05)
        ret
    }

    #---------------------------------------------------------------------------

    ### Run the simulation
    res <- runSimulation(Design, replications, verbose=FALSE, fixed_objects=fixed_objects,
                         generate=Generate, analyse=Analyse, summarise=Summarise, debug='none')
    res

    ## # A tibble: 3 × 6
    ##       N     p REPLICATIONS SIM_TIME       SEED COMPLETED
    ##   <dbl> <dbl>        <dbl> <chr>         <int> <chr>
    ## 1    10 0.044         1000 0.27s     488349520 Fri Mar 28 10:46:50 2025
    ## 2    20 0.06          1000 0.27s     469344150 Fri Mar 28 10:46:50 2025
    ## 3    30 0.054         1000 0.27s    1550099436 Fri Mar 28 10:46:50 2025

By placing objects in a list and passing this to `fixed_objects`, the objects are safely exported to all relevant functions. Furthermore, running this code in parallel will also be valid as a consequence (see below) because all objects are properly exported to each core.

    res <- runSimulation(Design, replications, verbose=FALSE, fixed_objects=fixed_objects,
                         generate=Generate, analyse=Analyse, summarise=Summarise, debug='none',
                         parallel = TRUE)

Again, remember that this is only required for R objects, NOT for user-defined functions!