##' Default Processing Parameters
##'
##' This function can be used to generate the input parameters for the data
##' pre-processing code. This is a good way to get a list of the required
##' parameters and then modify parameters to match your particular
##' configuration.
##'
##' @param datasetConfig The dataset configuration returned by the function
##' \code{GetDatasetConfig}
##' @param dataset The dataset name in the database.
##' @param removePriorImputation logical, whether previous imputation values
##' should be removed.
##' @param removeManualEstimation logical, whether previous manual estimation
##' should be removed.
##' @param imputationObservationFlag The observation flag that represents
##' imputation.
##' @param imputationMethodFlag The observation flag that represents estimated
##' by statistical algorithm.
##' @param balanceMethodFlag The method flag that for calculation based on
##' identity
##' @param manualEstimationObservationFlag The observation status flag that
##' corresponds to manual (FAO) estimates
##' @param manualEstimationMethodFlag The method flag that corresponds to manual
##' (FAO) estimates
##' @param missingValueObservationFlag The observation flag corresponding to
##' missing values.
##' @param missingValueMethodFlag The method flag corresponding to missing
##' values.
##'
##' @return Returns a list of the default parameters used in the data
##' pre-processing algorithm.
##'
##' @details Below is a description of the parameters: \itemize{
##'
##' \item areaVar: The column corresponding to the geographic area.
##' \item yearVar: The column corresponding to the time dimension.
##' \item itemVar: The column corresponding to the item/commodity.
##' \item elementVar: The column corresponding to the measured element.
##' \item valueVar: The name for value column in the normalised form.
##' \item flagObservationVar: The name for observation flag in the normalised form.
##' \item flagMethod: The name for method flag in the normalised form.
##' \item removePriorImputation: Should previous imputations be removed?
##' \item removeManualEstimation: Should previous manual estimates be removed?
##' \item imputationObservationFlag: The flag for imputation.
##' \item imputationMethodFlag: The observation flag that represents estimated by statistical algorithm.
##' \item balanceMethodFlag: The method flag that for calculation based on identity
##' \item manualEstimationObservationFlag: The observation status flag that corresponds to manual (FAO) estimates
##' \item manualEstimationMethodFlag: The method flag that corresponds to manual (FAO) estimates
##' \item missingValueObservationFlag: The observation flag corresponding to missing values.
##' \item missingValueMethodFlag: The method flag corresponding to missing values.
##' }
##'
##' @export
##'
productionProcessingParameters = function(datasetConfig,
dataset = "aproduction",
removePriorImputation = FALSE,
removeManualEstimation = FALSE,
keepOnlyProtected = TRUE,
imputationObservationFlag = "I",
imputationMethodFlag = "e",
balanceMethodFlag = "i",
manualEstimationObservationFlag = "E",
manualEstimationMethodFlag = "f",
missingValueObservationFlag = "M",
missingValueMethodFlag = "u"){
## HACK (Michael): There is no information on how to configure this, and
## thus it is hard coded.
areaVar = datasetConfig$dimensions[1]
itemVar = datasetConfig$dimensions[3]
elementVar = datasetConfig$dimensions[2]
yearVar = datasetConfig$timeDimension
flagObservationVar = datasetConfig$flags[1]
flagMethodVar = datasetConfig$flags[2]
valueVar = "Value"
list(
domain = datasetConfig$domain,
dataset = dataset,
areaVar = areaVar,
yearVar = yearVar,
itemVar = itemVar,
elementVar = elementVar,
flagObservationVar = flagObservationVar,
flagMethodVar = flagMethodVar,
valueVar = valueVar,
keepOnlyProtected =keepOnlyProtected,
removePriorImputation = removePriorImputation,
removeManualEstimation = removeManualEstimation,
imputationObservationFlag = imputationObservationFlag,
imputationMethodFlag = imputationMethodFlag,
## NOTE (Michael): balanceMethod should not have an observation flag,
## it uses flag aggregation.
balanceMethodFlag = balanceMethodFlag,
manualEstimationObservationFlag = manualEstimationObservationFlag,
manualEstimationMethodFlag = manualEstimationMethodFlag,
missingValueObservationFlag = missingValueObservationFlag,
missingValueMethodFlag = missingValueMethodFlag
)
}
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