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#' Compute Absolute Centralization
#'
#' @param .data [tibble][tibble::tibble-package] with sf geometry
#' @param .cols [`tidy-select`](https://tidyselect.r-lib.org/reference/language.html)
#' Columns to compute the measure with. Must be at least 2 columns. If more than 2, treats
#' first column as first group and sum of other columns as second.
#' @param .name name for column with absolute centralization. Leave missing to return a vector.
#'
#' @return a [tibble][tibble::tibble-package] or numeric vector if .name missing
#' @export
#'
#' @md
#' @concept centralization
#' @examples
#' data("de_county")
#' ds_abs_cent(de_county, c(pop_white, starts_with('pop_')))
#' ds_abs_cent(de_county, c(pop_white, starts_with('pop_')), 'abs_cent')
ds_abs_cent <- function(.data, .cols, .name){
if (!inherits(.data, 'sf')) {
stop('`ds_abs_cent` requires `.data` to inherit sf for calculating areas.')
}
.cols <- rlang::enquo(.cols)
if (missing(.name)) {
.name <- 'v_index'
ret_t <- FALSE
} else {
ret_t <- TRUE
}
.a <- calc_area(.data)
.A <- sum(.a)
.data$.dist <- calc_dist_centroid(.data)
.data <- .data %>% dplyr::arrange(.data$.dist)
sub <- .data %>%
drop_sf() %>%
dplyr::select(!!.cols)
if (ncol(sub) <= 1) {
stop('`.cols` refers to a single column')
}
sub <- sub %>%
dplyr::rowwise() %>%
dplyr::mutate(.x = pick_n(1)) %>%
dplyr::ungroup()
.X <- sum(sub[['.x']])
sub <- sub %>%
dplyr::mutate(.Xi = cumsum(.data$.x)/.X,
.Ai = cumsum(.a)/.A,
.Xi_min1 = dplyr::lag(.data$.Xi, default = 0),
.Ai_min1 = dplyr::lag(.data$.Ai, default = 0))
out <- sub %>%
dplyr::mutate(!!.name := sum(.data$.Xi_min1*.data$.Ai) -
sum(.data$.Xi*.data$.Ai_min1)) %>%
dplyr::pull(!!.name)
if (ret_t) {
.data %>% dplyr::mutate(!!.name := out) %>% relocate_sf()
} else {
out
}
}
#' @rdname ds_abs_cent
#' @param ... arguments to forward to ds_abs_cent from abs_cent
#' @export
abs_cent <- function(..., .data = dplyr::across(everything())) {
ds_abs_cent(.data = .data, ...)
}
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