#' Monthly Relative Humidity Data
#'
#' @description
#' Monthly meteorological data including relative humidity (rh),
#' temperature, insolation, precipitation, and wind speed for a
#' Brazilian station (2000-2025).
#'
#' The dataset contains two missing observations in the variables \code{ins}
#' and \code{pre}, corresponding to September 2020 and November 2025. To address
#' these missing values, imputation was performed via seasonal interpolation using
#' the \pkg{imputeTS} package, with the imputed versions stored as \code{ins2}
#' and \code{pre2}.
#'
#' @docType data
#' @usage data(RelativeHumidity)
#'
#' @format A data frame with 312 observations and 11 variables:
#' \describe{
#'   \item{date}{Observation date (YYYY-MM-DD)}
#'   \item{rh}{Monthly mean relative humidity, originally measured in percent and
#'   rescaled to the unit interval (0,1)}
#'   \item{ins}{Monthly total insolation (in hours), contains 2 missing values}
#'   \item{ins2}{Monthly total insolation (in hours) with missing values imputed
#'   via seasonal interpolation}
#'   \item{pre}{Monthly total precipitation (in mm), contains 2 missing values}
#'   \item{pre2}{Monthly total precipitation (in mm) with missing values imputed
#'   via seasonal interpolation}
#'   \item{neb}{Monthly mean cloudiness (in tenths)}
#'   \item{ap}{Monthly mean atmospheric pressure (in hPa)}
#'   \item{mt}{Monthly mean temperature (in Degrees Celsius)}
#'   \item{ws}{Monthly mean wind speed (in m/s)}
#'   \item{dir}{Monthly predominant wind direction (in degrees)}
#' }
#' @source Brazilian meteorological station
#'
#' @keywords datasets
#'
#' @examples
#' # Load the dataset
#' data(RelativeHumidity)
#'
#' # View first rows
#' head(RelativeHumidity)
#'
#' # Check structure
#' str(RelativeHumidity)
#'
#' # Insolation with and without imputation
#' plot(RelativeHumidity$ins, type = "l", col = "red",
#'      ylab = "Insolation", main = "Missing values visible as gaps")
#' points(RelativeHumidity$ins2, type = "l", col = "blue")
"RelativeHumidity"
