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# Convert fd.features list to list of column indices and check for consitency.
fdFeatsToColumnIndex = function(df, fd.features = NULL, exclude.cols = NULL) {
# If the data.frame already contains matricies, keep them
fd.mats = which(vcapply(df, function(x) class(x)[1L]) == "matrix")
# If fd.features is NULL, all numerics are a single functional feature
# Already existing matricies are not converted
if (is.null(fd.features)) {
fd.features = list("fd1" = setdiff(which(vlapply(df, is.numeric)), c(exclude.cols, fd.mats)))
}
# Return the column index and check if indices/names refer to columns
lapply(fd.features, function(fd.feature) {
if (is.character(fd.feature)) {
assertSubset(fd.feature, colnames(df), empty.ok = FALSE)
setdiff(which(colnames(df) %in% fd.feature), exclude.cols)
} else {
assertSubset(fd.feature, seq_len(ncol(df)))
setdiff(fd.feature, exclude.cols)
}
})
}
# Convert a data.frame containing functional features to a data.frame containing
# them as numerics.
functionalToNormalData = function(df) {
if (hasFunctionalFeatures(df)) {
df = do.call(data.frame, as.list(df))
message("Functional features have been converted to numerics")
}
return(df)
}
# Helper function that checks functional data columns for consistency
# Takes a data.frame and a functional data column
# Returns the functional matrix-column
checkFDCols = function(data, col) {
assertClass(data, "data.frame")
assertChoice(col, choices = colnames(data))
data = as.matrix(data[, col, drop = FALSE])
assertNumeric(data)
return(data)
}
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