R/data.R

In heaping: Correction of Heaping on Individual Level

#' Sample Data for Heaping Correction Examples
#'
#' A stratified random sample of demographic and income data from a synthetic
#' population generated using the \pkg{simPop} package based on EU-SILC data.
#' This dataset can be used to demonstrate and test heaping correction methods.
#'
#' @format A data frame with 25 variables:
#' \describe{
#'   \item{db030}{Household ID}
#'   \item{hsize}{Household size}
#'   \item{age}{Age in years}
#'   \item{rb090}{Gender}
#'   \item{db040}{Region (Bundesland)}
#'   \item{pid}{Person ID}
#'   \item{weight}{Original sampling weight}
#'   \item{pl031}{Economic status}
#'   \item{pb220a}{Citizenship status}
#'   \item{pb190}{Marital status}
#'   \item{pe040}{Education level}
#'   \item{pl111}{Employment status}
#'   \item{pgrossIncomeCat}{Personal gross income category}
#'   \item{pgrossIncome}{Personal gross income}
#'   \item{py010g}{Employee cash or near cash income}
#'   \item{py021g}{Company car income}
#'   \item{py050g}{Self-employment income}
#'   \item{py080g}{Private pension income}
#'   \item{py090g}{Unemployment benefits}
#'   \item{py100g}{Old-age benefits}
#'   \item{py110g}{Survivor benefits}
#'   \item{py120g}{Sickness benefits}
#'   \item{py130g}{Disability benefits}
#'   \item{py140g}{Education-related allowances}
#'   \item{.weight}{Sampling weight from stratified sampling}
#' }
#'
#' @source Generated using \pkg{simPop} from EU-SILC 2013 public use file.
#'   The full synthetic population can be regenerated using the script
#'   \code{inst/scripts/create_pop.R}.
#'
#' @seealso \code{\link[simPop]{eusilc13puf}} for the original data source.
#'
#' @examples
#' data(samp)
#' head(samp)
#'
#' # Check age distribution
#' hist(samp$age, breaks = 50, main = "Age Distribution")
#'
#' # Introduce artificial heaping and correct it
#' age_heaped <- round(samp$age / 5) * 5
#' age_corrected <- correctHeaps(age_heaped, heaps = "5year")
"samp"