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#' Get value labels from data
#'
#' Detects factors and converts them to compatible metadata/study data.
#'
#' @param resp_vars [variable] names of the variables to fetch the value labels
#' from the data
#' @param study_data [data.frame] the data frame that contains the measurements
#'
#' @return a [list] with:
#' - `VALUE_LABELS`: vector of value labels and modified study data
#' - `ModifiedStudyData`: study data with factors as integers
#'
#' @export
#' @importFrom stats setNames
#'
#' @examples
#' \dontrun{
#' dataquieR::prep_datatype_from_data(iris)
#' }
prep_valuelabels_from_data <-
function(resp_vars = colnames(study_data), study_data) {
if (!missing(resp_vars) && is.data.frame(resp_vars) && missing(study_data)) {
study_data <- resp_vars
resp_vars <- colnames(study_data)
} else if (missing(study_data) || !is.data.frame(study_data)) {
util_error("Need study data as a data frame in the argument %s",
dQuote("study_data"))
}
util_expect_data_frame(study_data)
study_data <- util_cast_off(study_data, "study_data")
if ((length(study_data) == 0) || !is.character(resp_vars)) {
util_error(
"%s should be missing or give variable names referring the study_data.",
dQuote("resp_vars"), applicability_problem = TRUE)
}
if (!(all(resp_vars %in% colnames(study_data)))) {
util_message(c(
"The following %s are missing from the %s.",
"Won't return a type for them: %s"),
dQuote("resp_vars"),
dQuote("study_data"),
sQuote(resp_vars[!(resp_vars %in% colnames(study_data))]),
applicability_problem = TRUE,
intrinsic_applicability_problem = TRUE
)
}
factor_vars <-
colnames(study_data)[vapply(study_data, is.factor, FUN.VALUE = logical(1))]
factor_resp_vars <- intersect(factor_vars, resp_vars)
valuelabels <- vapply(setNames(nm = resp_vars),
FUN.VALUE = character(1),
function(variable) {
if (variable %in% factor_resp_vars) {
fctr <- factor(levels = levels(study_data[[variable]]),
x = levels(study_data[[variable]]))
paste(as.integer(fctr), "=", levels(fctr)[as.integer(fctr)],
collapse = sprintf(" %s ", SPLIT_CHAR))
} else {
NA_character_
}
})
modifiedstudydata <- study_data
modifiedstudydata[, factor_resp_vars] <-
lapply(study_data[, factor_resp_vars, FALSE], as.integer)
return(list(
VALUE_LABELS = valuelabels,
ModifiedStudyData = modifiedstudydata
))
}
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