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#' Generate ordered ids of OD pairs so lowest is always first
#' This function is slow on large datasets, see szudzik_pairing for faster alternative
#'
#' @inheritParams od_oneway
#'
#' @examples
#' x <- data.frame(id1 = c(1, 1, 2, 2, 3), id2 = c(1, 2, 3, 1, 4))
#' od_id_order(x) # 4th line switches id1 and id2 so stplanr.key is in order
#' @family od
#' @export
od_id_order <- function(x, id1 = names(x)[1], id2 = names(x)[2]) {
data.frame(
stringsAsFactors = FALSE,
stplanr.id1 = x[[id1]],
stplanr.id1 = x[[id2]],
stplanr.key = od_id_character(x[[id1]], x[[id2]])
)
}
#' Combine two ID values to create a single ID number
#'
#' @details
#' In OD data it is common to have many 'oneway' flows from "A to B" and "B to A".
#' It can be useful to group these an have a single ID that represents pairs of IDs
#' with or without directionality, so they contain 'twoway' or bi-directional values.
#'
#' `od_id*` functions take two vectors of equal length and return a vector of IDs,
#' which are unique for each combination but the same for twoway flows.
#'
#' - the Szudzik pairing function, on two vectors of equal
#' length. It returns a vector of ID numbers.
#'
#' This function superseeds od_id_order as it is faster on large datasets
#' @param x a vector of numeric, character, or factor values
#' @param y a vector of numeric, character, or factor values
#' @param ordermatters logical, does the order of values matter to pairing, default = FALSE
#' @family od
#' @name od_id
#' @examples
#' (d <- od_data_sample[2:9, 1:2])
#' (id <- od_id_character(d[[1]], d[[2]]))
#' duplicated(id)
#' od_id_szudzik(d[[1]], d[[2]])
#' od_id_max_min(d[[1]], d[[2]])
NULL
#' @rdname od_id
#' @export
od_id_szudzik <- function(x, y, ordermatters = FALSE) {
if (length(x) != length(y)) {
stop("x and y are not of equal length")
}
if (is(x, "factor")) {
x <- as.character(x)
}
if (is(y, "factor")) {
y <- as.character(y)
}
lvls <- unique(c(x, y))
x <- as.integer(factor(x, levels = lvls))
y <- as.integer(factor(y, levels = lvls))
if (ordermatters) {
ismax <- x > y
stplanr.key <- (ismax * 1) * (x^2 + x + y) + ((!ismax) * 1) * (y^2 + x)
} else {
a <- ifelse(x > y, y, x)
b <- ifelse(x > y, x, y)
stplanr.key <- b^2 + a
}
return(stplanr.key)
}
#' @export
#' @rdname od_id
od_id_max_min <- function(x, y) {
d <- convert_to_numeric(x, y)
a <- pmax(d$x, d$y)
b <- pmin(d$x, d$y)
a * (a + 1) / 2 + b
}
#' @export
#' @rdname od_id
od_id_character <- function(x, y) {
paste(
pmin(x, y),
pmax(x, y)
)
}
convert_to_numeric <- function(x, y) {
if (length(x) != length(y)) stop("x and y are not of equal length")
if (is(x, "factor")) x <- as.character(x)
if (is(y, "factor")) y <- as.character(y)
lvls <- unique(c(x, y))
x <- as.integer(factor(x, levels = lvls))
y <- as.integer(factor(y, levels = lvls))
list(x = x, y = y)
}
od_id_order_base <- function(x, y) {
d <- convert_to_numeric(x, y)
x <- d$x
y <- d$y
paste(pmin(x, y), pmax(x, y))
}
not_duplicated <- function(x) {
!duplicated(x)
}
#' Aggregate od pairs they become non-directional
#'
#' For example, sum total travel in both directions.
#' @param x A data frame or SpatialLinesDataFrame, representing an OD matrix
#' @param attrib A vector of column numbers or names, representing variables to be aggregated.
#' By default, all numeric variables are selected.
#' aggregate
#' @param id1 Optional (it is assumed to be the first column)
#' text string referring to the name of the variable containing
#' the unique id of the origin
#' @param id2 Optional (it is assumed to be the second column)
#' text string referring to the name of the variable containing
#' the unique id of the destination
#' @return `oneway` outputs a data frame (or `sf` data frame) with rows containing
#' results for the user-selected attribute values that have been aggregated.
#' @param stplanr.key Optional key of unique OD pairs regardless of the order,
#' e.g., as generated by [od_id_max_min()] or [od_id_szudzik()]
#' @family od
#' @export
#' @details
#' Flow data often contains movement in two directions: from point A to point B
#' and then from B to A. This can be problematic for transport planning, because
#' the magnitude of flow along a route can be masked by flows the other direction.
#' If only the largest flow in either direction is captured in an analysis, for
#' example, the true extent of travel will by heavily under-estimated for
#' OD pairs which have similar amounts of travel in both directions.
#' Flows in both direction are often represented by overlapping lines with
#' identical geometries which can be confusing
#' for users and are difficult to plot.
#' @examples
#' (od_min <- od_data_sample[c(1, 2, 9), 1:6])
#' (od_oneway <- od_oneway(od_min))
#' # (od_oneway_old = onewayid(od_min, attrib = 3:6)) # old implementation
#' nrow(od_oneway) < nrow(od_min) # result has fewer rows
#' sum(od_min$all) == sum(od_oneway$all) # but the same total flow
#' od_oneway(od_min, attrib = "all")
#' attrib <- which(vapply(flow, is.numeric, TRUE))
#' flow_oneway <- od_oneway(flow, attrib = attrib)
#' colSums(flow_oneway[attrib]) == colSums(flow[attrib]) # test if the colSums are equal
#' # Demonstrate the results from oneway and onewaygeo are identical
#' flow_oneway_sf <- od_oneway(flowlines_sf)
#' plot(flow_oneway_sf$geometry, lwd = flow_oneway_sf$All / mean(flow_oneway_sf$All))
od_oneway <- function(x,
attrib = names(x[-c(1:2)])[vapply(x[-c(1:2)], is.numeric, TRUE)],
id1 = names(x)[1],
id2 = names(x)[2],
stplanr.key = NULL) {
is_sf <- is(x, "sf")
if (is.null(stplanr.key)) {
id1_temp <- x[[id1]]
x[[id1]] <- pmin(x[[id1]], x[[id2]])
x[[id2]] <- pmax(id1_temp, x[[id2]])
if (is_sf) {
duplicated_oneway <- duplicated(sf::st_drop_geometry(x[c(id1, id2)]))
} else {
duplicated_oneway <- duplicated(x[c(id1, id2)])
}
if (is_sf) {
x_sf <- x[!duplicated_oneway, c(id1, id2)]
x <- sf::st_drop_geometry(x)
}
if (is.numeric(attrib)) {
attrib <- attrib - 2 # account for 1st 2 columns being ids
}
x_grouped <- dplyr::group_by(x, !!rlang::sym(id1), !!rlang::sym(id2))
x_oneway <- dplyr::ungroup(dplyr::summarise_at(x_grouped, attrib, sum))
if (is_sf) {
x_oneway <- sf::st_as_sf(dplyr::left_join(x_oneway, x_sf))
# class(x_oneway) # sf
}
return(x_oneway)
}
if (is.numeric(attrib)) {
attrib_names <- names(x)[attrib]
} else {
attrib_names <- attrib
attrib <- which(names(x) %in% attrib)
}
# separate geometry for sf objects
if (is_sf) {
x_sf <- sf::st_sf(data.frame(stplanr.key, stringsAsFactors = FALSE), geometry = sf::st_geometry(x))
x <- sf::st_drop_geometry(x)
}
x <- dplyr::bind_cols(x, stplanr.key = stplanr.key)
x_oneway_grouped <- dplyr::group_by(x, stplanr.key)
x_oneway <- dplyr::ungroup(dplyr::summarise_at(x_oneway_grouped, attrib, sum))
# # next lines can extract ids - assuming not necessary for now:
# x_ids_grouped <-
# dplyr::summarise(
# x_oneway_grouped,
# id1 = dplyr::first(!!rlang::sym(id1)),
# id2 = dplyr::first(!!rlang::sym(id2))
# )
# x_oneway <- dplyr::bind_cols(
# x_oneway,
# x_ids_grouped
# )
if (is_sf) {
x_sf <- x_sf[!duplicated(x_sf$stplanr.key), ]
x_oneway <- sf::st_as_sf(dplyr::inner_join(x_oneway, x_sf))
# class(x_oneway) # sf
}
# x_oneway$stplanr.key <- NULL
# names(x_oneway)[1:2] <- c(id1, id2)
return(x_oneway)
}
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