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#' Create an environment with several alias names for the study data variables
#'
#' generates an environment similar to `as.environment(ds1)`, but makes
#' variables available by their `VAR_NAME`, `LABEL`, and `label_col` - names.
#'
#' @param study_data [data.frame] the data frame that contains the measurements
#' @param meta_data [data.frame] the data frame that contains metadata
#' attributes of study data
#' @param label_col [variable attribute] the name of the column in the metadata
#' with labels of variables. If
#' `study_data` has already been mapped,
#' i.e., `util_ds1_eval_env(ds1, ...)` is
#' called, this will work too
util_ds1_eval_env <- function(study_data,
meta_data = "item_level",
label_col = LABEL) {
if (isTRUE(attr(study_data, "MAPPED"))) {
ds1 <- study_data
} else {
prep_prepare_dataframes()
}
label_col_from <- attr(ds1, "label_col")
label_col_to <- label_col
res <- ds1
lct <- setdiff(c(VAR_NAMES, LABEL, LONG_LABEL, label_col_to), label_col_from)
lct <- intersect(lct, colnames(meta_data))
for (cur_nm in lct) {
res[, util_map_labels(colnames(ds1),
from = label_col_from,
to = cur_nm,
meta_data = meta_data)] <-
ds1[, colnames(ds1), FALSE]
}
as.environment(res)
}
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