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#' Compute Relative Concentration
#'
#' @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 relative concentration. Leave missing to return a vector.
#'
#' @return a [tibble][tibble::tibble-package] or numeric vector if .name missing
#' @export
#'
#' @md
#' @concept concentration
#' @examples
#' data('de_county')
#' ds_rel_conc(de_county, c(pop_black, starts_with('pop_')))
#' ds_rel_conc(de_county, c(pop_black, starts_with('pop_')), 'rel_conc')
ds_rel_conc <- function(.data, .cols, .name) {
if (!inherits(.data, 'sf')) {
stop('`ds_rel_conc` 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
}
sub <- .data %>%
drop_sf() %>%
dplyr::select(!!.cols)
if (ncol(sub) <= 1) {
stop('`.cols` refers to a single column')
}
sub <- sub %>%
dplyr::rowwise() %>%
dplyr::mutate(
.total = sum(dplyr::c_across(everything())),
.x = pick_n(1),
.y = .data$.total - .data$.x
) %>%
dplyr::ungroup()
sub$.a <- calc_area(.data)
.X <- sum(sub$.x)
.A <- sum(sub$.a)
.Y <- sum(sub$.y)
.T <- sum(sub$.total)
if (.X / .T > 0.5) {
warning('First column indicated in `.cols` is the majority group. Values may be unstable.')
}
.n1_sum <- calc_n1_sum(sub, .X)
.n2_sum <- calc_n2_sum(sub, .X)
out <- sub %>%
dplyr::mutate(!!.name := (sum(.data$.x * .data$.a / .X) / sum(.data$.y * .data$.a / .Y) - 1)
/ ((.n1_sum / .n2_sum) - 1)) %>%
dplyr::pull(!!.name)
if (ret_t) {
.data %>%
dplyr::mutate(!!.name := out) %>%
relocate_sf()
} else {
out
}
}
#' @rdname ds_rel_conc
#' @param ... arguments to forward to ds_rel_conc from rel_conc
#' @export
rel_conc <- function(..., .data = dplyr::across(everything())) {
ds_rel_conc(.data = .data, ...)
}
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