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#' Plot walking path to nearest pump (prototype).
#'
#' @param case Numeric.
#' @param destination Numeric. Pump ID.
#' @param vestry Logical. \code{TRUE} uses the 14 pumps from the map in the Vestry Report. \code{FALSE} uses the 13 pumps from the original map.
#' @param weighted Logical. \code{TRUE} computes shortest path in terms of road length. \code{FALSE} computes shortest path in terms of the number of nodes.
#' @param distance.unit Character. Unit of distance: "meter" or "yard".
#' @param time.unit Character. "hour", "minute", or "second".
#' @param walking.speed Numeric. Walking speed in km/hr.
#' @param multi.core Logical or Numeric. \code{TRUE} uses \code{parallel::detectCores()}. \code{FALSE} uses one, single core. You can also specify the number logical cores. See \code{vignette("Parallelization")} for details.
#' @export
latlongWalkingPath <- function(case = 1, destination = NULL, vestry = FALSE,
weighted = TRUE, distance.unit = "meter", time.unit = "second",
walking.speed = 5, multi.core = TRUE) {
meter.to.yard <- 1.09361
cores <- multiCore(multi.core)
if (!case %in% cholera::fatalities$case) {
stop("Valid cases range from 1 to 578.", call. = FALSE)
} else {
anchor <- cholera::anchor.case[cholera::anchor.case$case == case, "anchor"]
}
if (vestry) {
pmp <- cholera::pumps.vestry
} else {
pmp <- cholera::pumps
}
if (!is.null(destination)) {
pump.id <- selectPump(pmp, pump.select = destination, vestry = vestry)
if (any(pump.id == 2L)) {
# message("Pump 2 excluded because it's a technical isolate.")
pump.id <- pump.id[pump.id != 2L]
}
network.data <- latlongNeighborhoodData(vestry = vestry, multi.core = cores)
edge.list <- network.data$edge.list
edges <- network.data$edges
g <- network.data$g
nodes <- network.data$nodes
nodes$node <- paste0(nodes$lon, "_&_", nodes$lat)
ego.node <- nodes[nodes$case == anchor, "node"]
alters <- nodes[nodes$pump %in% pump.id & nodes$pump != 0, ]
alters <- alters[order(alters$pump),]
alter.node <- alters$node
names(alter.node) <- alters$pump
d <- c(igraph::distances(g, ego.node, alter.node, weights = edges$d))
names(d) <- pump.id
nr.pmp <- names(which.min(d))
p <- igraph::shortest_paths(g, ego.node, alter.node[nr.pmp],
weights = edges$d)$vpath
p <- names(unlist(p))
p.data <- do.call(rbind, strsplit(p, "_&_"))
path <- data.frame(id = seq_len(nrow(p.data)),
x = as.numeric(p.data[, 1]),
y = as.numeric(p.data[, 2]))
vars <- c("x", "y")
ds <- vapply(seq_len(nrow(path[-1, ])), function(i) {
geosphere::distGeo(path[i, vars], path[i + 1, vars])
}, numeric(1L))
if (time.unit == "hour") {
walking.time <- d[nr.pmp] / (1000L * walking.speed)
} else if (time.unit == "minute") {
walking.time <- (60L * d[nr.pmp]) / (1000L * walking.speed)
} else if (time.unit == "second") {
walking.time <- (3600L * d[nr.pmp]) / (1000L * walking.speed)
}
p.nm <- pmp[pmp$id == as.integer(nr.pmp), "street"]
data.summary <- data.frame(case = case, anchor = anchor, pump.name = p.nm,
pump = as.integer(nr.pmp), distance = d[nr.pmp], time = walking.time)
output <- list(path = path,
data = data.summary,
destination = destination,
vestry = vestry,
ds = ds,
distance.unit = distance.unit,
pmp = pmp,
time.unit = time.unit,
walking.speed = walking.speed)
} else {
nr.pmp <- latlongNearestPump(vestry = vestry, multi.core = cores)
case.id <- which(cholera::fatalities.address$anchor == anchor)
p <- names(nr.pmp$path[[case.id]][[1]])
destination.pump <- names(nr.pmp$path[[case.id]])
nodes <- do.call(rbind, strsplit(p, "_&_"))
dat <- data.frame(x = as.numeric(nodes[, 1]), y = as.numeric(nodes[, 2]))
ds <- vapply(seq_len(nrow(dat[-1, ])), function(i) {
geosphere::distGeo(dat[i, ], dat[i + 1, ])
}, numeric(1L))
if (distance.unit == "meter") {
speed <- walking.speed
} else if (distance.unit == "yard") {
speed <- walking.speed * meter.to.yard
ds <- ds * meter.to.yard
} else {
stop('distance.unit must be "meter" or "yard".')
}
path.length <- sum(ds)
if (time.unit == "hour") {
trip.time <- path.length / (1000L * speed)
} else if (time.unit == "minute") {
trip.time <- (60L * path.length) / (1000L * speed)
} else if (time.unit == "second") {
trip.time <- (3600L * path.length) / (1000L * speed)
}
path <- data.frame(id = seq_along(dat$x), dat)
data.summary <- data.frame(case = case, anchor = anchor,
pump.name = pmp[pmp$id == destination.pump, "street"],
pump = destination.pump, distance = path.length, time = trip.time)
output <- list(path = path,
data = data.summary,
destination = destination,
vestry = vestry,
ds = ds,
distance.unit = distance.unit,
pmp = pmp,
time.unit = time.unit,
walking.speed = walking.speed)
}
class(output) <- "latlong_walking_path"
output
}
#' Plot the walking path between selected cases and/or pumps.
#'
#' @param x An object of class "latlong_walking_path" created by latlongWalkingPath().
#' @param zoom Logical or Numeric. A numeric value >= 0 that controls the degree of zoom.
#' @param mileposts Logical. Plot mile/time posts.
#' @param milepost.unit Character. "distance" or "time".
#' @param milepost.interval Numeric. Mile post interval unit of distance (yard or meter) or unit of time (seconds).
#' @param alpha.level Numeric. Alpha level transparency for path: a value in [0, 1].
#' @param ... Additional plotting parameters.
#' @return A base R plot.
#' @export
plot.latlong_walking_path <- function(x, zoom = TRUE, mileposts = TRUE,
milepost.unit = "distance", milepost.interval = NULL, alpha.level = 1, ...) {
path.data <- x$data
case <- path.data$case
destination <- x$destination
colors <- snowColors(x$vestry)
dat <- x$path
ds <- x$ds
distance.unit <- x$distance.unit
pmp <- x$pmp
time.unit <- x$time.unit
walking.speed <- x$walking.speed
if (distance.unit == "meter") {
d.unit <- "m"
} else if (distance.unit == "yard") {
d.unit <- "yd"
}
if (milepost.unit == "distance") {
path.length <- sum(ds)
} else if (milepost.unit == "time") {
path.length <- (3600L * sum(ds)) / (1000L * walking.speed)
}
rd <- cholera::roads[cholera::roads$name != "Map Frame", ]
frame <- cholera::roads[cholera::roads$name == "Map Frame", ]
fatality <- cholera::fatalities
if (mileposts) {
if (is.null(milepost.interval)) {
if (milepost.unit == "distance") {
milepost.interval <- 50
} else if (milepost.unit == "time") {
milepost.interval <- 60
}
}
milepost.data <- milePosts(path.data, dat, ds, milepost.unit,
milepost.interval, distance.unit, time.unit, walking.speed, destination)
seg.data <- milepost.data$seg.data
if (path.length > milepost.interval) {
arrow.head <- milepost.data$arrow.head
arrow.tail <- milepost.data$arrow.tail
}
}
if (is.logical(zoom)) {
if (zoom) {
padding <- 0.00026
xlim <- c(min(dat$x) - padding, max(dat$x) + padding)
ylim <- c(min(dat$y) - padding, max(dat$y) + padding)
} else {
map.data <- rbind(frame, rd)
xlim <- range(map.data$lon)
ylim <- range(map.data$lat)
}
} else if (is.numeric(zoom)) {
if (zoom >= 0) {
xlim <- c(min(dat$x) - zoom, max(dat$x) + zoom)
ylim <- c(min(dat$y) - zoom, max(dat$y) + zoom)
} else stop("If numeric, zoom must be >= 0.")
} else stop("zoom must either be logical or numeric.")
vars <- c("lon", "lat")
plot(rd[, vars], pch = NA, asp = 1.6, xlim = xlim, ylim = ylim)
roads.list <- split(rd[, vars], rd$street)
frame.list <- split(frame[, vars], frame$street)
invisible(lapply(roads.list, lines, col = "lightgray"))
invisible(lapply(frame.list, lines))
points(fatality[, vars], col = "lightgray", pch = 16, cex = 0.5)
points(fatality[fatality$case == case, vars], col = "red", pch = 1)
text(fatality[fatality$case == case, vars], pos = 1, labels = case,
col = "red")
points(pmp[, vars], pch = 24, col = grDevices::adjustcolor(colors,
alpha.f = alpha.level))
text(pmp[, vars], pos = 1, labels = paste0("p", pmp$id))
points(dat[1, c("x", "y")], col = "dodgerblue", pch = 0)
points(dat[nrow(dat), c("x", "y")], col = "dodgerblue", pch = 0)
p.sel <- paste0("p", path.data$pump)
case.color <- grDevices::adjustcolor(colors[p.sel], alpha.f = alpha.level)
drawPathB(dat, case.color)
if (milepost.unit == "distance") {
if (distance.unit == "meter") {
post.info <- paste("posts at", milepost.interval, "m intervals")
} else if (distance.unit == "yard") {
post.info <- paste("posts at", milepost.interval, "yd intervals")
}
} else if (milepost.unit == "time") {
post.info <- paste("posts at", milepost.interval, "sec intervals")
} else {
stop('"milepost.unit" muster either be "distance" or "time".')
}
d <- paste(round(path.length, 1), d.unit)
t <- paste(round(x$data$time), paste0(time.unit, "s"), "@", walking.speed,
"km/hr")
title(main = paste("Case", case, "to Pump", path.data$pump),
sub = paste(d, t, post.info, sep = "; "))
if (mileposts) {
arrows(seg.data[1, "x2"], seg.data[1, "y2"],
seg.data[1, "x1"], seg.data[1, "y1"],
length = 0.0875, lwd = 3, col = case.color)
if (path.length >= milepost.interval) {
# dotchart(log(abs(arrow.tail$lon - arrow.head$lon)))
# dotchart(log(abs(arrow.tail$lat - arrow.head$lat)))
cutpoint <- -13
zero.length.lon <- log(abs(arrow.tail$lon - arrow.head$lon)) < cutpoint
zero.length.lat <- log(abs(arrow.tail$lat - arrow.head$lat)) < cutpoint
if (any(zero.length.lon | zero.length.lat)) {
zero.id <- unique(row.names(arrow.head[zero.length.lon, ]),
row.names(arrow.head[zero.length.lat, ]))
angle <- vapply(zero.id, function(id) {
zero.arrow <- rbind(arrow.tail[id, vars], arrow.head[id, vars])
ols <- stats::lm(lat ~ lon, data = zero.arrow)
slope <- stats::coef(ols)[2]
theta <- atan(slope)
theta * 180L / pi
}, numeric(1L))
invisible(lapply(seq_along(zero.id), function(i) {
text(arrow.head[zero.id[i], vars], labels = "<", srt = angle[i],
col = case.color, cex = 1.25)
}))
arrow.head <- arrow.head[!row.names(arrow.head) %in% zero.id, ]
arrow.tail <- arrow.tail[!row.names(arrow.tail) %in% zero.id, ]
}
arrows(arrow.tail$lon, arrow.tail$lat, arrow.head$lon, arrow.head$lat,
length = 0.0875, lwd = 3, col = case.color)
}
}
}
#' Print method for latlongWalkingPath().
#'
#' Summary output.
#' @param x An object of class "latlong_walking_path" created by latlongWalkingPath().
#' @param ... Additional parameters.
#' @return An R data frame.
#' @export
print.latlong_walking_path <- function(x, ...) {
if (!inherits(x, "latlong_walking_path")) {
stop('"x"\'s class must be "latlong_walking_path".')
}
print(x[c("path", "data")])
}
drawPathB <- function(x, case.color) {
path.data <- x
n1 <- path.data[1:(nrow(path.data) - 1), ]
n2 <- path.data[2:nrow(path.data), ]
segments(n1$x, n1$y, n2$x, n2$y, lwd = 3, col = case.color)
}
milePosts <- function(path.data, dat, ds, milepost.unit, milepost.interval,
distance.unit, time.unit, walking.speed, destination) {
rev.data <- dat[order(dat$id, decreasing = TRUE), ]
vars <- c("x", "y")
seg.vars <- c(paste0(vars, 1), paste0(vars, 2))
seg.data <- do.call(rbind, lapply(seq_len(nrow(rev.data) - 1), function(i) {
endpts <- cbind(rev.data[i, vars], rev.data[i + 1, vars])
names(endpts) <- seg.vars
data.frame(id = i, endpts)
}))
seg.data$d <- rev(ds)
seg.data$cumulative.d <- cumsum(seg.data$d)
if (milepost.unit == "distance") {
path.length <- sum(ds)
cumulative <- seg.data$cumulative.d
} else if (milepost.unit == "time") {
path.length <- path.data$time
seg.data$t <- (3600L * seg.data$d) / (1000L * walking.speed)
seg.data$cumulative.t <- cumsum(seg.data$t)
cumulative <- seg.data$cumulative.t
}
posts <- seq(0, path.length, milepost.interval)
if (max(posts) > path.length) posts <- posts[-length(posts)]
bins <- data.frame(lo = c(0, cumulative[-length(cumulative)]),
hi = cumulative)
seg.select <- vapply(posts, function(x) {
which(vapply(seq_len(nrow(bins)), function(i) {
x >= bins[i, "lo"] & x < bins[i, "hi"]
}, logical(1L)))
}, integer(1L))
if (all(seg.select == 1) & length(seg.select) > 1) {
milepost.seg.id <- unique(seg.select)
} else {
if (sum(seg.select == 1) > 1) {
milepost.seg.id <- c(1, seg.select[seg.select != 1L])
} else {
milepost.seg.id <- seg.select[seg.select != 1L]
}
}
segment.census <- table(milepost.seg.id)
if (any(segment.census > 1)) {
single.post.seg <- as.numeric(names(segment.census[segment.census == 1]))
multi.post.seg <- as.numeric(names(segment.census[segment.census > 1]))
} else {
single.post.seg <- milepost.seg.id
}
if (path.length > milepost.interval) {
origin <- data.frame(lon = min(cholera::roads$lon),
lat = min(cholera::roads$lat))
topleft <- data.frame(lon = min(cholera::roads$lon),
lat = max(cholera::roads$lat))
bottomright <- data.frame(lon = max(cholera::roads$lon),
lat = min(cholera::roads$lat))
milepost.values <- seq_along(milepost.seg.id) * milepost.interval
census <- data.frame(seg = milepost.seg.id, post = milepost.values)
if (any(segment.census > 1)) {
single.arrow.data <- arrowData(single.post.seg, census, seg.data, origin,
milepost.unit)
multi.arrow.data <- arrowData(multi.post.seg, census, seg.data, origin,
milepost.unit, multi.arrow.seg = TRUE)
arrow.data <- rbind(single.arrow.data, multi.arrow.data)
} else {
arrow.data <- arrowData(single.post.seg, census, seg.data, origin,
milepost.unit)
}
arrow.tail <- stats::setNames(arrow.data[, paste0(vars, 1)], vars)
arrow.head <- stats::setNames(arrow.data[, paste0(vars, 2)], vars)
arrow.tail <- meterLatLong(arrow.tail, origin, topleft, bottomright)
arrow.head <- meterLatLong(arrow.head, origin, topleft, bottomright)
arrow.tail <- arrow.tail[order(row.names(arrow.tail)), ]
arrow.head <- arrow.head[order(row.names(arrow.head)), ]
out <- list(seg.data = seg.data, arrow.head = arrow.head,
arrow.tail = arrow.tail)
} else {
out <- list(seg.data = seg.data)
}
out
}
arrowData <- function(segs, census, seg.data, origin, milepost.unit,
multi.arrow.seg = FALSE, vars = c("lon", "lat")) {
out <- lapply(segs, function(s) {
tmp <- seg.data[seg.data$id == s, ]
endpt1 <- stats::setNames(tmp[, grep("1", names(tmp))], vars)
endpt2 <- stats::setNames(tmp[, grep("2", names(tmp))], vars)
latlong.tmp <- rbind(endpt1, endpt2)
idx <- seq_along(latlong.tmp$lon)
meter.coords <- do.call(rbind, lapply(idx, function(i) {
tmp <- latlong.tmp[i, vars]
x.proj <- c(tmp$lon, origin$lat)
y.proj <- c(origin$lon, tmp$lat)
m.lon <- geosphere::distGeo(y.proj, tmp)
m.lat <- geosphere::distGeo(x.proj, tmp)
data.frame(x = m.lon, y = m.lat)
}))
ols <- stats::lm(y ~ x, data = meter.coords)
seg.slope <- stats::coef(ols)[2]
theta <- atan(seg.slope)
if (multi.arrow.seg) {
posts <- census[census$seg %in% s, "post"]
multi.out <- lapply(posts, function(p) {
if (milepost.unit == "distance") {
h <- tmp$cumulative.d - p
} else if (milepost.unit == "time") {
h <- tmp$cumulative.t - p
}
arrow.point <- quandrantCoordinatesB(meter.coords, h, theta)
data.frame(x1 = meter.coords[2, "x"],
y1 = meter.coords[2, "y"],
x2 = arrow.point$x,
y2 = arrow.point$y)
})
do.call(rbind, multi.out)
} else {
post <- census[census$seg == s, "post"]
if (milepost.unit == "distance") {
h <- tmp$cumulative.d - post
} else if (milepost.unit == "time") {
h <- tmp$cumulative.t - post
}
arrow.point <- quandrantCoordinatesB(meter.coords, h, theta)
data.frame(x1 = meter.coords[2, "x"],
y1 = meter.coords[2, "y"],
x2 = arrow.point$x,
y2 = arrow.point$y)
}
})
do.call(rbind, out)
}
quandrantCoordinatesB <- function(dat, h, theta) {
delta <- dat[2, ] - dat[1, ]
# Quadrant I
if (all(delta > 0)) {
post.x <- dat[2, "x"] - abs(h * cos(theta))
post.y <- dat[2, "y"] - abs(h * sin(theta))
# Quadrant II
} else if (delta[1] < 0 & delta[2] > 0) {
post.x <- dat[2, "x"] + abs(h * cos(theta))
post.y <- dat[2, "y"] - abs(h * sin(theta))
# Quadrant III
} else if (all(delta < 0)) {
post.x <- dat[2, "x"] + abs(h * cos(theta))
post.y <- dat[2, "y"] + abs(h * sin(theta))
# Quadrant IV
} else if (delta[1] > 0 & delta[2] < 0) {
post.x <- dat[2, "x"] - abs(h * cos(theta))
post.y <- dat[2, "y"] + abs(h * sin(theta))
# I:IV
} else if (delta[1] > 0 & delta[2] == 0) {
post.x <- dat[2, "x"] - abs(h * cos(theta))
post.y <- dat[2, "y"]
# I:II
} else if (delta[1] == 0 & delta[2] > 0) {
post.x <- dat[2, "x"]
post.y <- dat[2, "y"] - abs(h * sin(theta))
# II:III
} else if (delta[1] < 0 & delta[2] == 0) {
post.x <- dat[2, "x"] + abs(h * cos(theta))
post.y <- dat[2, "y"]
# III:IV
} else if (delta[1] == 0 & delta[2] < 0) {
post.x <- dat[2, "x"]
post.y <- dat[2, "y"] + abs(h * sin(theta))
}
data.frame(x = post.x, y = post.y)
}
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