/
spat_prox.R
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/
spat_prox.R
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#' Compute Spatial Proximity
#'
#' @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 spatial proximity. Leave missing to return a vector.
#'
#' @return a [tibble][tibble::tibble-package] or numeric vector if .name missing
#' @export
#'
#' @md
#' @concept clustering
#' @examples
#' data("de_county")
#' ds_spat_prox(de_county, c(pop_black, starts_with('pop_')))
#' ds_spat_prox(de_county, c(pop_black, starts_with('pop_')), 'spat_prox')
ds_spat_prox <- function(.data, .cols, .name){
if (!inherits(.data, 'sf')) {
stop('`ds_spat_prox` 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
}
.data$.a <- calc_area(.data)
sub <- .data %>%
drop_sf() %>%
dplyr::select(!!.cols)
if (ncol(sub) <= 1) {
stop('`.cols` refers to a single column')
}
sub <- sub %>%
dplyr::rowwise() %>%
dplyr::mutate(.total = sum(dplyr::c_across(everything())),
.x = pick_n(1),
.y = .data$.total - .data$.x) %>%
dplyr::ungroup()
.X <- sum(sub$.x)
.Y <- sum(sub$.y)
.T <- sum(sub$.total)
.pxx <- calc_pgg(.data, sub %>% dplyr::pull(.data$.x))
.pyy <- calc_pgg(.data, sub %>% dplyr::pull(.data$.y))
.ptt <- calc_pgg(.data, sub %>% dplyr::pull(.data$.total))
out <- sub %>%
dplyr::mutate(!!.name := (.pxx + .pyy)/.ptt) %>%
dplyr::pull(!!.name)
if (ret_t) {
.data %>% dplyr::mutate(!!.name := out) %>% relocate_sf()
} else {
out
}
}
#' @rdname ds_spat_prox
#' @param ... arguments to forward to ds_spat_prox from spat_prox
#' @export
spat_prox <- function(..., .data = dplyr::across(everything())) {
ds_spat_prox(.data = .data, ...)
}