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#' Implements the geometric spatial transfer of statistics among Spanish census sections corresponding to two different spatial divisions
#'
#' @description Spatially transfers the statistics available in a set of Spanish census sections corresponding to
#' the division into force in a given year to the census sections of another division
#' with reference in another year.
#'
#' @author Jose M. Pavia, \email{pavia@@uv.es}
#' @author Virgilio Perez, \email{virgilio.perez@@uv.es}
#' @references Pavia, JM and Cantarino, I (2017a). Can dasymetric mapping significantly improve population data reallocation in a dense urban area? *Geographical Analysis*, 49(2), 155-174. \doi{10.1111/gean.12112}
#' @references Pavia, JM and Cantarino, I (2017b). Dasymetric distribution of votes in a dense city. *Applied Geography*, 86, 22-31. \doi{10.1016/j.apgeog.2017.06.021}
#' @references Perez, V and Pavia, JM (2024b) Automating the transfer of data between census sections and postal codes areas over time. An application to Spain. *Investigaciones Regionales - Journal of Regional Research*, forthcoming. \doi{10.38191/iirr-jorr.24.057}
#'
#' @param x A data frame of order N x K (with K > 1) with the statistics to be spatially transferred/imputed.
#' The first column must contains the codes of the census sections to which the statistics belong to. The statistical nature
#' of the data columns must be of the same type. See the argument `data.type`.
#'
#' @param year.sscc.origin An integer number. Reference year of the census sections included in the first column of `x`.
#' Only 2001 and 2003 to 2023 are allowed.
#'
#' @param year.sscc.dest An integer number. Reference year of the census sections to which the statistics are going to be transferred.
#' Only 2001 and 2003 to 2023 are allowed and it must be different than `year.sscc.origin`.
#'
#' @param data.type A character string indicating the type of data to be transferred, either `"counts"` (aggregate statistics)
#' or `"averages"` (mean, proportion or rate statistics). Default `"counts"`.
#'
#' @param all.units A `TRUE/FALSE` logical value indicating the census section units of the destination division to be included
#' in the output data frame. If `TRUE` all the units of the destination division are included. If `FALSE` only
#' those units for which a value is imputed are included. Default, `FALSE`.
#'
#' @param na.rm A `TRUE/FALSE` logical value indicating whether `NA` values should be stripped before
#' the computations proceed. Default, `TRUE`.
#'
#'
#' @param ... Other arguments to be passed to the function. Not currently used.
#'
#'
#' @return
#' A list with the following components
#' \item{df}{ A data frame with the statistics spatially transferred to the census sections corresponding to the `year.sscc.dest` division.}
#' \item{missing}{ A vector with the codes of the census sections included in `x` that are not available in the shp file of census sections corresponding to the `year.sscc.origin` division.}
#'
#' @note The data that allows to transfer throughout time statistics among census sections
#' has been own elaboration by the authors using the Spanish Digital Cartography Files
#' in http://www.ine.es that contain the digitalisation of the georeferenced polygons
#' of the census sections, according to UTM coordinates 28, 29, 30 and 31.
#' @note The Spanish Statistical Office (Instituto Nacional de Estadistica) had any
#' involvement in preparing this package. They bear no responsibility on the results
#' derived from using this package.
#'
#' @export
#'
#' @seealso \code{\link{sc2cp}} \code{\link{cp2sc}}
#' @importFrom stats aggregate
#'
#' @examples
#' data <- structure(list(SSCC = c(3403601001, 3403701001, 3403801001, 3403901001,
#' 3404101001, 3404201001, 3404501001, 3404601001,
#' 3404701001, 3404701002, 3404801001),
#' X15.19 = c(4L, 7L, 13L, 0L, 0L, 13L, 1L, 5L, 30L, 48L, 1L),
#' X20.24 = c(5L, 5L, 9L, 0L, 2L, 12L, 2L, 1L, 34L, 61L, 3L)),
#' row.names = 1:11, class = "data.frame")
#' example <- sc2sc(x = data, year.sscc.origin = 2020, year.sscc.dest = 2019)
sc2sc <- function(x,
year.sscc.origin,
year.sscc.dest,
data.type = "counts",
all.units = FALSE,
na.rm = TRUE,
...){
if (!is.data.frame(x)){
stop("ERROR: 'x' must be an object of class data.frame")
} else {
x <- as.data.frame(x)
}
# inputs <- c(as.list(environment()), list(...))
test_years(year.sscc.origin, year.sscc.dest)
if (!(data.type %in% c("counts", "averages")))
stop("ERROR: The argument 'data.type' is improper. Only 'counts' and 'averages' are allowed.")
years <- year.sscc.origin:year.sscc.dest
years <- years[years != 2002]
testeo <- test_sscc_codes(bbdd = x,
y.origin = year.sscc.origin,
y.dest = years[2L])
bbdd <- testeo$bbdd
if (data.type == "counts"){
transfer_function <- impute_total
} else {
transfer_function <- impute_average
}
for (aa in 1L:(length(years) - 1L)){
bbdd <- transfer_function(bbdd = bbdd,
y.origin = years[aa],
y.dest = years[aa + 1L],
all.units = all.units,
na.rm = na.rm)
}
# return(list("df" = bbdd, "missing" = testeo$missing, "inputs" = inputs))
return(list("df" = bbdd, "missing" = testeo$missing))
}
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