tests/nn-fn.R
In kstierhoff/atm: Functions Used for the Analysis of Acoustic-Trawl Method (ATM) Survey Data
# A function for calculating spatial statistics around points
# Juan Zwolinski and Kevin Stierhoff
# 12 March 2025
library(tidyverse)
library(sf)
library(sp)
library(mapview)
# Simulated data, 3 trawls, 2 transects
trawl <- data.frame(long = c(-125, -125.5, -124.3),
lat = c( 35, 36, 35.5))
sA <- data.frame(long = rep(seq(-126, -124, l = 100), 2),
lat = c(rep(35.3, 100), rep(36, 100)) ,
nasc = exp(rnorm(200)))
# Final function, contains both sp and sf methods
neighbor_quantifier <- function(small.file, large.file, variable = NULL,
method = "sp", crs = 4326, radius = 20, length = 100,
plot.fig = TRUE, save.fig = TRUE, fig.name = NULL){
# small.file is the file to average to
# large.file is the file that provides the quantitative information of the required quantitative "variable"
# Both small.file and large.file must have long and lat
# method is either "sp" (cartesian space) or "sf" (cartesian or geodetic space)
# crs is the coordinate reference system; 4326 for lat/long data, e.g., from a GPS
# radius is in nautical miles
# length is the length of half circle, in meters?
# Rename columns in large file
names(large.file)[match(variable, names(large.file) )] <- "variable"
# Initialize vectors for storing results
n <- mean <- max <- min <- sd <- numeric(0)
if (method == "sp") {
for(i in 1:nrow(small.file)){
# Create temporary file
small.file.temp <- small.file[i, ]
# Create nodes for polygon used by {sp}
polygon.x <- c(small.file.temp$long + seq(-radius, radius, l = length) / 60 / cos(small.file.temp$lat*pi / 180),
small.file.temp$long + rev(seq(-radius, radius, l = length) / 60 / cos(small.file.temp$lat*pi / 180)))
polygon.y <- c(small.file.temp$lat + sqrt(radius^2 - seq(-radius, radius, l = length)^2) / 60,
small.file.temp$lat - sqrt(radius^2 - rev(seq(-radius, radius, l = length))^2) / 60)
# Put polygon coordinates into a data frame
polygon.i <- data.frame(polygon = i, X = polygon.x, Y = polygon.y)
if (exists("polygon.i.xy")) {
polygon.i.xy <- dplyr::bind_rows(polygon.i.xy, polygon.i)
} else {
polygon.i.xy <- polygon.i
}
if (exists("large.file.intersects")) {
# Get intervals that intersect polygons, for plotting
large.file.intersects <- large.file.intersects %>%
bind_rows(large.file[
sp::point.in.polygon(large.file$long, large.file$lat,
polygon.x, polygon.y) == 1, ])
} else {
large.file.intersects <- large.file[
sp::point.in.polygon(large.file$long, large.file$lat,
polygon.x, polygon.y) == 1, ]
}
# Compute number of intervals within each polygon
n <- c(n, sum(sp::point.in.polygon(large.file$long[!is.na(large.file$variable)],
large.file$lat[!is.na(large.file$variable)],
polygon.x, polygon.y) == 1))
# Compute mean values within each polygon
mean <- c(mean,
mean(large.file$variable[
sp::point.in.polygon(large.file$long, large.file$lat,
polygon.x, polygon.y) == 1], na.rm = TRUE))
# Compute min values within each polygon
min <- c(min,
min(large.file$variable[
sp::point.in.polygon(large.file$long, large.file$lat,
polygon.x, polygon.y) == 1], na.rm = TRUE))
# Compute max values within each polygon
max <- c(max,
max(large.file$variable[
sp::point.in.polygon(large.file$long, large.file$lat,
polygon.x, polygon.y) == 1], na.rm = TRUE))
# Compute standard deviation values within each polygon
sd <- c(sd,
sd(large.file$variable[
sp::point.in.polygon(large.file$long, large.file$lat,
polygon.x, polygon.y) == 1], na.rm = TRUE))
}
if (plot.fig) {
# Add polygon to plot
var.plot <- ggplot2::ggplot() +
ggplot2::geom_point(data = large.file, aes(long, lat, size = variable/max(lat)*10),
shape = 21, show.legend = FALSE) +
ggplot2::geom_point(data = polygon.i.xy, aes(X, Y, group = polygon), shape = 21, fill = NA) +
ggplot2::geom_point(data = large.file.intersects,
aes(long, lat, size = variable/max(lat)*10),
colour = "green", show.legend = FALSE) +
ggplot2::geom_point(data = small.file, aes(long, lat), shape = 21, fill = "red", size = 3) +
ggplot2::theme_bw()
if (save.fig) {
if (is.null(fig.name)) {
ggplot2::ggsave(var.plot, filename = "variable-plot-sp.png")
} else {
ggplot2::ggsave(var.plot, filename = fig.name)
}
}
}
} else if (method == "sf") {
# Convert files to sf objects
small.file.sf <- sf::st_as_sf(small.file, coords = c("long", "lat"), crs = crs) %>%
dplyr::mutate(
long = as.data.frame(sf::st_coordinates(.))$X,
lat = as.data.frame(sf::st_coordinates(.))$Y)
large.file.sf <- sf::st_as_sf(large.file, coords = c("long", "lat"), crs = crs) %>%
mutate(
long = as.data.frame(sf::st_coordinates(.))$X,
lat = as.data.frame(sf::st_coordinates(.))$Y)
# Rename columns in large file
names(large.file.sf)[match(variable, names(large.file.sf) )] <- "variable"
for(i in 1:nrow(small.file.sf)){
# Create circular polygon using a spatial buffer
polygon.i <- small.file.sf[i, ] %>%
sf::st_buffer(radius*1852)
if (exists("polygon.i.sf")) {
polygon.i.sf <- dplyr::bind_rows(polygon.i.sf, polygon.i)
} else {
polygon.i.sf <- polygon.i
}
# Get number of intersecting points
n <- c(n, length(st_intersection(large.file.sf, polygon.i)$variable))
# Compute mean values within each polygon
mean <- c(mean, mean(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
# Compute min values within each polygon
min <- c(min, min(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
# Compute max values within each polygon
max <- c(max, max(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
# Compute standard deviation values within each polygon
sd <- c(sd, sd(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
}
# Get intervals that intersect polygons, for plotting
large.file.intersects <- st_intersection(large.file.sf, polygon.i.sf)
if (plot.fig) {
# Add polygon to plot
var.plot <- ggplot2::ggplot() +
ggplot2::geom_sf(data = large.file.sf, aes(size = variable/max(lat)*10),
shape = 21, show.legend = FALSE) +
ggplot2::geom_sf(data = large.file.intersects,
aes(size = variable/max(long)*10),
colour = "green", show.legend = FALSE) +
ggplot2::geom_sf(data = small.file.sf, shape = 21, fill = "red", size = 3) +
ggplot2::geom_sf(data = polygon.i.sf, fill = NA, linetype = "dashed") +
ggplot2::coord_sf() +
ggplot2::theme_bw()
if (save.fig) {
if (is.null(fig.name)) {
ggplot2::ggsave(var.plot, filename = "variable-plot-sf.png")
} else {
ggplot2::ggsave(var.plot, filename = fig.name)
}
}
}
}
# Return results
return(data.frame(n, mean, min, max, sd))
}
# Clear graphics window
graphics.off()
# Small radius, no samples in two of the trawls
test.sp <- neighbor_quantifier(small.file = trawl, large.file = sA, variable ="nasc", method = "sp",
radius = 10, save.fig = TRUE)
test.sp
test.sf <- neighbor_quantifier(small.file = trawl, large.file = sA, variable ="nasc", method = "sf",
radius = 10, save.fig = TRUE)
test.sf
# Increased range, 18, 41, and 32 samples
test.sp <- neighbor_quantifier(small.file = trawl, large.file = sA, variable ="nasc", method = "sp",
radius = 20, save.fig = TRUE)
test.sp
test.sf <- neighbor_quantifier(small.file = trawl, large.file = sA, variable ="nasc", method = "sf",
radius = 20, save.fig = TRUE)
test.sf
# Increased range even more, 34, 51, 38 samples
test.sp <- neighbor_quantifier(small.file = trawl, large.file = sA, variable ="nasc", method = "sp",
radius = 30, save.fig = TRUE)
test.sp
test.sf <- neighbor_quantifier(small.file = trawl, large.file = sA, variable ="nasc", method = "sf",
radius = 30, save.fig = TRUE)
test.sf
# Unused functions ---------------------------------------------------------
# # Revised function (uses sf)
# neighbor_quantifier_sf <- function(small.file, large.file, variable = NULL, radius = 20, plot.data = TRUE, length = 100, crs = 4326) {
# # small.file is the file to average to
# # large.file is the file that provides the quantitative information of the required quantitative "variable"
# # Both small.file and large.file must have long and lat
# # radius is in nautical miles
# # length is the number of nodes on the polygon around small.file locations
#
# # Require the {sf} package
# require(sf)
# require(tidyverse)
#
# small.file.sf <- st_as_sf(small.file, coords = c("long","lat"), crs = crs) %>%
# mutate(
# long = as.data.frame(st_coordinates(.))$X,
# lat = as.data.frame(st_coordinates(.))$Y)
#
# large.file.sf <- st_as_sf(large.file, coords = c("long","lat"), crs = crs) %>%
# mutate(
# long = as.data.frame(st_coordinates(.))$X,
# lat = as.data.frame(st_coordinates(.))$Y)
#
# # Rename variables
# names(large.file)[match(variable, names(large.file) )] <- "variable"
# names(large.file.sf)[match(variable, names(large.file.sf) )] <- "variable"
#
# # Initialize variables
# n <- mean <- max <- min <- sd <- numeric(0)
#
# # For each point to compute statistics
# for(i in 1:nrow(small.file)){
# print(i)
#
# # Create temporary file
# small.file.temp <- small.file[i, ] # temporary file
#
# # Create circular polygons using math
# polygon.x <- c(small.file.temp$long + seq(-radius, radius, l =length)/60/cos(small.file.temp$lat*pi/180),
# small.file.temp$long + rev(seq(-radius, radius, l =length)/60/cos(small.file.temp$lat*pi/180)))
# polygon.y <- c(small.file.temp$lat + sqrt(radius^2 - seq(-radius, radius, l =length)^2)/60,
# small.file.temp$lat - sqrt(radius^2 - rev(seq(-radius, radius, l =length))^2)/60)
#
# # Create circular polygon using a mathematical method
# polygon.xy <- data.frame(polygon.x, polygon.y) %>%
# st_as_sf(coords = c("polygon.x","polygon.y"), crs = 4326) %>%
# summarise(do_union = F) %>%
# st_cast("POLYGON") %>%
# st_make_valid()
#
# # Create circular polygon using a spatial buffer
# polygon.i <- small.file.sf[i, ] %>% st_buffer(radius*1852)
#
# # Combine polygon objects
# if (exists("polygon.xy.sf")) {
# polygon.xy.sf <- bind_rows(polygon.xy, polygon.xy.sf)
# } else {
# polygon.xy.sf <- polygon.xy
# }
#
# if (exists("polygon.i.sf")) {
# polygon.i.sf <- bind_rows(polygon.i.sf, polygon.i)
# } else {
# polygon.i.sf <- polygon.i
# }
#
# # Get number of intersecting points
# n <- c(n, length(st_intersection(large.file.sf, polygon.i)$variable))
#
# # Compute mean
# mean <- c(mean, mean(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
#
# # Compute min
# min <- c(min, min(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
#
# # Compute max
# max <- c(max, max(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
#
# # Compute standard deviation
# sd <- c(sd, sd(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
# }
#
# if (plot.data) {
# # Add polygon to plot
# var.plot <- ggplot() +
# geom_sf(data = large.file.sf, aes(size = variable/max(lat)*10),
# shape = 21, show.legend = FALSE) +
# geom_sf(data = small.file.sf, shape = 21, fill = "red", size = 3) +
# geom_sf(data = polygon.i.sf, fill = NA, linetype = "dotted") +
# geom_sf(data = polygon.xy.sf, colour = "red", fill = NA, linetype = "dashed") +
# coord_sf() +
# theme_bw()
#
# ggsave(var.plot, filename = "variable-plot-sf.png")
# }
#
# # Return results
# return(data.frame(n, mean, min, max, sd))
# }
kstierhoff/atm documentation built on June 10, 2025, 8:55 a.m.
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# A function for calculating spatial statistics around points
# Juan Zwolinski and Kevin Stierhoff
# 12 March 2025
library(tidyverse)
library(sf)
library(sp)
library(mapview)
# Simulated data, 3 trawls, 2 transects
trawl <- data.frame(long = c(-125, -125.5, -124.3),
lat = c( 35, 36, 35.5))
sA <- data.frame(long = rep(seq(-126, -124, l = 100), 2),
lat = c(rep(35.3, 100), rep(36, 100)) ,
nasc = exp(rnorm(200)))
# Final function, contains both sp and sf methods
neighbor_quantifier <- function(small.file, large.file, variable = NULL,
method = "sp", crs = 4326, radius = 20, length = 100,
plot.fig = TRUE, save.fig = TRUE, fig.name = NULL){
# small.file is the file to average to
# large.file is the file that provides the quantitative information of the required quantitative "variable"
# Both small.file and large.file must have long and lat
# method is either "sp" (cartesian space) or "sf" (cartesian or geodetic space)
# crs is the coordinate reference system; 4326 for lat/long data, e.g., from a GPS
# radius is in nautical miles
# length is the length of half circle, in meters?
# Rename columns in large file
names(large.file)[match(variable, names(large.file) )] <- "variable"
# Initialize vectors for storing results
n <- mean <- max <- min <- sd <- numeric(0)
if (method == "sp") {
for(i in 1:nrow(small.file)){
# Create temporary file
small.file.temp <- small.file[i, ]
# Create nodes for polygon used by {sp}
polygon.x <- c(small.file.temp$long + seq(-radius, radius, l = length) / 60 / cos(small.file.temp$lat*pi / 180),
small.file.temp$long + rev(seq(-radius, radius, l = length) / 60 / cos(small.file.temp$lat*pi / 180)))
polygon.y <- c(small.file.temp$lat + sqrt(radius^2 - seq(-radius, radius, l = length)^2) / 60,
small.file.temp$lat - sqrt(radius^2 - rev(seq(-radius, radius, l = length))^2) / 60)
# Put polygon coordinates into a data frame
polygon.i <- data.frame(polygon = i, X = polygon.x, Y = polygon.y)
if (exists("polygon.i.xy")) {
polygon.i.xy <- dplyr::bind_rows(polygon.i.xy, polygon.i)
} else {
polygon.i.xy <- polygon.i
}
if (exists("large.file.intersects")) {
# Get intervals that intersect polygons, for plotting
large.file.intersects <- large.file.intersects %>%
bind_rows(large.file[
sp::point.in.polygon(large.file$long, large.file$lat,
polygon.x, polygon.y) == 1, ])
} else {
large.file.intersects <- large.file[
sp::point.in.polygon(large.file$long, large.file$lat,
polygon.x, polygon.y) == 1, ]
}
# Compute number of intervals within each polygon
n <- c(n, sum(sp::point.in.polygon(large.file$long[!is.na(large.file$variable)],
large.file$lat[!is.na(large.file$variable)],
polygon.x, polygon.y) == 1))
# Compute mean values within each polygon
mean <- c(mean,
mean(large.file$variable[
sp::point.in.polygon(large.file$long, large.file$lat,
polygon.x, polygon.y) == 1], na.rm = TRUE))
# Compute min values within each polygon
min <- c(min,
min(large.file$variable[
sp::point.in.polygon(large.file$long, large.file$lat,
polygon.x, polygon.y) == 1], na.rm = TRUE))
# Compute max values within each polygon
max <- c(max,
max(large.file$variable[
sp::point.in.polygon(large.file$long, large.file$lat,
polygon.x, polygon.y) == 1], na.rm = TRUE))
# Compute standard deviation values within each polygon
sd <- c(sd,
sd(large.file$variable[
sp::point.in.polygon(large.file$long, large.file$lat,
polygon.x, polygon.y) == 1], na.rm = TRUE))
}
if (plot.fig) {
# Add polygon to plot
var.plot <- ggplot2::ggplot() +
ggplot2::geom_point(data = large.file, aes(long, lat, size = variable/max(lat)*10),
shape = 21, show.legend = FALSE) +
ggplot2::geom_point(data = polygon.i.xy, aes(X, Y, group = polygon), shape = 21, fill = NA) +
ggplot2::geom_point(data = large.file.intersects,
aes(long, lat, size = variable/max(lat)*10),
colour = "green", show.legend = FALSE) +
ggplot2::geom_point(data = small.file, aes(long, lat), shape = 21, fill = "red", size = 3) +
ggplot2::theme_bw()
if (save.fig) {
if (is.null(fig.name)) {
ggplot2::ggsave(var.plot, filename = "variable-plot-sp.png")
} else {
ggplot2::ggsave(var.plot, filename = fig.name)
}
}
}
} else if (method == "sf") {
# Convert files to sf objects
small.file.sf <- sf::st_as_sf(small.file, coords = c("long", "lat"), crs = crs) %>%
dplyr::mutate(
long = as.data.frame(sf::st_coordinates(.))$X,
lat = as.data.frame(sf::st_coordinates(.))$Y)
large.file.sf <- sf::st_as_sf(large.file, coords = c("long", "lat"), crs = crs) %>%
mutate(
long = as.data.frame(sf::st_coordinates(.))$X,
lat = as.data.frame(sf::st_coordinates(.))$Y)
# Rename columns in large file
names(large.file.sf)[match(variable, names(large.file.sf) )] <- "variable"
for(i in 1:nrow(small.file.sf)){
# Create circular polygon using a spatial buffer
polygon.i <- small.file.sf[i, ] %>%
sf::st_buffer(radius*1852)
if (exists("polygon.i.sf")) {
polygon.i.sf <- dplyr::bind_rows(polygon.i.sf, polygon.i)
} else {
polygon.i.sf <- polygon.i
}
# Get number of intersecting points
n <- c(n, length(st_intersection(large.file.sf, polygon.i)$variable))
# Compute mean values within each polygon
mean <- c(mean, mean(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
# Compute min values within each polygon
min <- c(min, min(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
# Compute max values within each polygon
max <- c(max, max(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
# Compute standard deviation values within each polygon
sd <- c(sd, sd(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
}
# Get intervals that intersect polygons, for plotting
large.file.intersects <- st_intersection(large.file.sf, polygon.i.sf)
if (plot.fig) {
# Add polygon to plot
var.plot <- ggplot2::ggplot() +
ggplot2::geom_sf(data = large.file.sf, aes(size = variable/max(lat)*10),
shape = 21, show.legend = FALSE) +
ggplot2::geom_sf(data = large.file.intersects,
aes(size = variable/max(long)*10),
colour = "green", show.legend = FALSE) +
ggplot2::geom_sf(data = small.file.sf, shape = 21, fill = "red", size = 3) +
ggplot2::geom_sf(data = polygon.i.sf, fill = NA, linetype = "dashed") +
ggplot2::coord_sf() +
ggplot2::theme_bw()
if (save.fig) {
if (is.null(fig.name)) {
ggplot2::ggsave(var.plot, filename = "variable-plot-sf.png")
} else {
ggplot2::ggsave(var.plot, filename = fig.name)
}
}
}
}
# Return results
return(data.frame(n, mean, min, max, sd))
}
# Clear graphics window
graphics.off()
# Small radius, no samples in two of the trawls
test.sp <- neighbor_quantifier(small.file = trawl, large.file = sA, variable ="nasc", method = "sp",
radius = 10, save.fig = TRUE)
test.sp
test.sf <- neighbor_quantifier(small.file = trawl, large.file = sA, variable ="nasc", method = "sf",
radius = 10, save.fig = TRUE)
test.sf
# Increased range, 18, 41, and 32 samples
test.sp <- neighbor_quantifier(small.file = trawl, large.file = sA, variable ="nasc", method = "sp",
radius = 20, save.fig = TRUE)
test.sp
test.sf <- neighbor_quantifier(small.file = trawl, large.file = sA, variable ="nasc", method = "sf",
radius = 20, save.fig = TRUE)
test.sf
# Increased range even more, 34, 51, 38 samples
test.sp <- neighbor_quantifier(small.file = trawl, large.file = sA, variable ="nasc", method = "sp",
radius = 30, save.fig = TRUE)
test.sp
test.sf <- neighbor_quantifier(small.file = trawl, large.file = sA, variable ="nasc", method = "sf",
radius = 30, save.fig = TRUE)
test.sf
# Unused functions ---------------------------------------------------------
# # Revised function (uses sf)
# neighbor_quantifier_sf <- function(small.file, large.file, variable = NULL, radius = 20, plot.data = TRUE, length = 100, crs = 4326) {
# # small.file is the file to average to
# # large.file is the file that provides the quantitative information of the required quantitative "variable"
# # Both small.file and large.file must have long and lat
# # radius is in nautical miles
# # length is the number of nodes on the polygon around small.file locations
#
# # Require the {sf} package
# require(sf)
# require(tidyverse)
#
# small.file.sf <- st_as_sf(small.file, coords = c("long","lat"), crs = crs) %>%
# mutate(
# long = as.data.frame(st_coordinates(.))$X,
# lat = as.data.frame(st_coordinates(.))$Y)
#
# large.file.sf <- st_as_sf(large.file, coords = c("long","lat"), crs = crs) %>%
# mutate(
# long = as.data.frame(st_coordinates(.))$X,
# lat = as.data.frame(st_coordinates(.))$Y)
#
# # Rename variables
# names(large.file)[match(variable, names(large.file) )] <- "variable"
# names(large.file.sf)[match(variable, names(large.file.sf) )] <- "variable"
#
# # Initialize variables
# n <- mean <- max <- min <- sd <- numeric(0)
#
# # For each point to compute statistics
# for(i in 1:nrow(small.file)){
# print(i)
#
# # Create temporary file
# small.file.temp <- small.file[i, ] # temporary file
#
# # Create circular polygons using math
# polygon.x <- c(small.file.temp$long + seq(-radius, radius, l =length)/60/cos(small.file.temp$lat*pi/180),
# small.file.temp$long + rev(seq(-radius, radius, l =length)/60/cos(small.file.temp$lat*pi/180)))
# polygon.y <- c(small.file.temp$lat + sqrt(radius^2 - seq(-radius, radius, l =length)^2)/60,
# small.file.temp$lat - sqrt(radius^2 - rev(seq(-radius, radius, l =length))^2)/60)
#
# # Create circular polygon using a mathematical method
# polygon.xy <- data.frame(polygon.x, polygon.y) %>%
# st_as_sf(coords = c("polygon.x","polygon.y"), crs = 4326) %>%
# summarise(do_union = F) %>%
# st_cast("POLYGON") %>%
# st_make_valid()
#
# # Create circular polygon using a spatial buffer
# polygon.i <- small.file.sf[i, ] %>% st_buffer(radius*1852)
#
# # Combine polygon objects
# if (exists("polygon.xy.sf")) {
# polygon.xy.sf <- bind_rows(polygon.xy, polygon.xy.sf)
# } else {
# polygon.xy.sf <- polygon.xy
# }
#
# if (exists("polygon.i.sf")) {
# polygon.i.sf <- bind_rows(polygon.i.sf, polygon.i)
# } else {
# polygon.i.sf <- polygon.i
# }
#
# # Get number of intersecting points
# n <- c(n, length(st_intersection(large.file.sf, polygon.i)$variable))
#
# # Compute mean
# mean <- c(mean, mean(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
#
# # Compute min
# min <- c(min, min(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
#
# # Compute max
# max <- c(max, max(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
#
# # Compute standard deviation
# sd <- c(sd, sd(st_intersection(large.file.sf, polygon.i)$variable, na.rm = TRUE))
# }
#
# if (plot.data) {
# # Add polygon to plot
# var.plot <- ggplot() +
# geom_sf(data = large.file.sf, aes(size = variable/max(lat)*10),
# shape = 21, show.legend = FALSE) +
# geom_sf(data = small.file.sf, shape = 21, fill = "red", size = 3) +
# geom_sf(data = polygon.i.sf, fill = NA, linetype = "dotted") +
# geom_sf(data = polygon.xy.sf, colour = "red", fill = NA, linetype = "dashed") +
# coord_sf() +
# theme_bw()
#
# ggsave(var.plot, filename = "variable-plot-sf.png")
# }
#
# # Return results
# return(data.frame(n, mean, min, max, sd))
# }
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