#' Rerun a workflow object.
#'
#' Takes a workflow object and reruns it.
#'
#' @param workflow A zoonWorkflow object from a previous zoon analysis
#' @param from Which modules should be run. If NULL (default), run from the
#' first NULL output (i.e. where the workflow broke). Otherwise takes an
#' integer and runs from that module.
#'
#' @return A list with the results of each module and a copy of the
#' call used to execute the workflow.
#'
#' @export
#' @name RerunWorkflow
#' @importFrom utils sessionInfo
#' @examples \dontrun{
#' w <- workflow(UKAnophelesPlumbeus,
#' UKAir,
#' Background(n = 70),
#' LogisticRegression,
#' PrintMap)
#'
#' RerunWorkflow(w)
#' }
RerunWorkflow <- function(workflow, from = NULL) {
stopifnot(inherits(workflow, "zoonWorkflow"))
# If from isn't NULL, it should be an integer 1:5
if (!is.null(from)) {
stopifnot(from %in% c(1:5))
}
# Find first NULL modules and run from there.
if (is.null(from)) {
NullModules <- vapply(workflow[1:5],
is.null,
FUN.VALUE = FALSE)
if (any(NullModules)) {
from <- which.max(NullModules)
} else {
from <- 1
}
}
# get the arguments from the call used to run this workflow
callArgs <- SplitCall(workflow$call)
# These are strings at first but we need to change them
# to calls as is expected in the main zoon workflow function
# this
occSub <- StringToCall(callArgs["occurrence"])
covSub <- StringToCall(callArgs["covariate"])
proSub <- StringToCall(callArgs["process"])
modSub <- StringToCall(callArgs["model"])
outSub <- StringToCall(callArgs["output"])
forceReproducible <- as.logical(callArgs["forceReproducible"])
# save the local environment as it needs to be passed to various functions.
e <- new.env(parent = parent.frame())
# capture the session info to return in workflow object
session.info <- sessionInfo()
# Check all modules are of same list structure
occurrence.module <- CheckModList(occSub)
covariate.module <- CheckModList(covSub)
process.module <- CheckModList(proSub)
model.module <- CheckModList(modSub)
output.module <- CheckModList(outSub)
# Only one of occurrence, covariate, process and model can be a list of
# multiple modules.
module_list <- list(occurrence.module,
covariate.module,
process.module,
model.module,
output.module)
isChain <- vapply(module_list,
function (x) {
isTRUE(attr(x, "chain"))
},
FUN.VALUE = FALSE)
NoOfModules <- vapply(module_list,
length,
FUN.VALUE = 0)
if (sum(NoOfModules[!isChain] > 1) > 1)
stop("Only one module type can be a list of multiple modules.")
# Get the modules (functions) from github.
# Save name of functions as well as load functions into global namespace.
# Will probably want to make this so it checks namespace first.
occurrenceName <- LapplyGetModule(occurrence.module, forceReproducible, e)
covariateName <- LapplyGetModule(covariate.module, forceReproducible, e)
processName <- LapplyGetModule(process.module, forceReproducible, e)
# Check for val type lon lat covs
modelName <- LapplyGetModule(model.module, forceReproducible, e)
# Test for predict method
outputName <- LapplyGetModule(output.module, forceReproducible, e)
fun_ver <- function(x) c(module = x$func, version = x$version)
eg <- c(module = "a", version = "b")
# Build module version list
moduleVersions <- list(
occurrence = vapply(occurrenceName, fun_ver, FUN.VALUE = eg),
covariate = vapply(covariateName, fun_ver, FUN.VALUE = eg),
process = vapply(processName, fun_ver, FUN.VALUE = eg),
model = vapply(modelName, fun_ver, FUN.VALUE = eg),
output = vapply(outputName, fun_ver, FUN.VALUE = eg)
)
# Different to workflow(), We have an if statement before each module is run
# to check the 'from' argument.
# Run the modules. (these functions are in DoModuleFunctions.R)
# But we have to check for chained modules and deal with them
# And work out which module has been given as a list, and lapply over that.
# Each module is in trycatch.
# If a module breaks we want to save the progress so far and let the user
# know which module broke.
# set up object to return on error
# Collate output
output <- list(
occurrence.output = NULL,
covariate.output = NULL,
process.output = NULL,
model.output = NULL,
report = NULL,
call = workflow$call,
call.list = workflow$call.list,
session.info = session.info,
module.versions = moduleVersions
)
class(output) <- "zoonWorkflow"
# whether exiting on error, or successful completion, return this
on.exit(return(output))
# First the data collection modules
# Actually tryCatch here only tells user which module broke, nothing to save.
if (from <= 1) {
tryCatch(
{
occurrence.output <- lapply(occurrenceName,
FUN = DoOccurrenceModule,
e)
# Then bind together if the occurrence modules were chained
if (identical(attr(occurrence.module, "chain"), TRUE)) {
occurrence.output <- list(do.call(rbind, occurrence.output))
attr(occurrence.output[[1]], "call_path") <- list(occurrence = paste(
"Chain(",
paste(
lapply(occurrenceName, function(x) x$module),
collapse = ", "
),
")", sep = ""
))
}
output$occurrence.output <- occurrence.output
},
error = function(cond) {
ErrorModule(cond, 1, e)
}
)
} else {
occurrence.output <- workflow$occurrence.output
output$occurrence.output <- occurrence.output
}
if (from <= 2) {
tryCatch(
{
covariate.output <- lapply(covariateName, FUN = DoCovariateModule, e)
if (identical(attr(covariate.module, "chain"), TRUE)) {
covariate.output <- list(do.call(raster::stack, covariate.output))
attr(covariate.output[[1]], "call_path") <- list(covariate = paste(
"Chain(",
paste(
lapply(covariateName, function(x) x$module),
collapse = ", "
),
")", sep = ""
))
}
output$covariate.output <- covariate.output
},
error = function(cond) {
ErrorModule(cond, 2, e)
}
)
} else {
covariate.output <- workflow$covariate.output
output$covariate.output <- covariate.output
}
# Simply combine data into basic df shape
# This shape is then input and output of all process modules.
# Also makes it easy to implement a NULL process
if (length(covariateName) > 1) {
data <- lapply(
covariate.output,
function(x) ExtractAndCombData(occurrence.output[[1]], x)
)
} else {
data <- lapply(
occurrence.output,
function(x) ExtractAndCombData(x, covariate.output[[1]])
)
}
if (from <= 3) {
tryCatch(
{
process.output <- DoProcessModules(process.module, processName, data, e)
output$process.output <- process.output
},
error = function(cond) {
ErrorModule(cond, 3, e)
}
)
} else {
process.output <- workflow$process.output
output$process.output <- process.output
}
# Model module
if (from <= 4) {
tryCatch(
{
model.output <- DoModelModules(model.module,
modelName,
process.output,
e)
output$model.output <- model.output
},
error = function(cond) {
ErrorModule(cond, 4, e)
}
)
} else {
model.output <- workflow$model.output
output$model.output <- model.output
}
# output module
# If output isn't chained, might have to lapply over
# output, covariate or process
# If output is chained, either covariate or process only.
# Within this need to chain output
if (from <= 5) {
tryCatch(
{
output.output <- DoOutputModules(
output.module, outputName,
process.module, process.output, model.output, e
)
output$report <- output.output
},
error = function(cond) {
ErrorModule(cond, 5, e)
}
)
} else {
output.output <- workflow$report
output$report <- output.output
}
}
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