In WDNR-Water-Use/CSLSscenarios: CSLS MODFLOW Scenario Analysis
This Rmd plots the median level (solid line) and range between the infrequent
high and infrequent low levels (shaded bar) for the no-irrigated-agriculture
scenario (blue) significant thresholds (grey), and current-irrigated-agriculture
scenario (yellow) for each of the the three lakes relative to their average lake
profile. These plots do not have the same scale across lakes, but the code is
set up to facilitate saving as .svg files at approximately the same scale for
each lake.
library(CSLSscenarios)
library(dplyr)
library(NISTunits)
library(ggplot2)
library(extrafont)
library(reshape2)
save_on <- TRUE
bathymetry <- CSLSdata::bathymetry %>%
mutate(y = NISTmeterTOft(.data$elev_m),
x = NISTmeterTOft(.data$horiz_dist_m)) %>%
select(.data$lake, .data$y, .data$x)
# Most limiting by hydrologic metric -------------------------------------------
most_limiting <- CSLSscenarios::MODFLOW_comparison %>%
group_by(.data$sim_type,
.data$lake,
.data$hydrology,
.data$metric,
.data$variable,
.data$value1,
.data$value2,
.data$diff,
.data$significant_if) %>%
mutate(limit_threshold = ifelse(.data$significant_if == "lower",
max(.data$threshold, na.rm = TRUE),
min(.data$threshold, na.rm = TRUE))) %>%
filter(.data$limit_threshold == .data$threshold) %>%
ungroup() %>%
filter(.data$scenario != "wells_off",
.data$sim == 1,
.data$metric == "exceedance_level") %>%
mutate(probs = .data$variable) %>%
select(.data$scenario,
.data$lake,
.data$probs,
.data$value1,
.data$threshold,
.data$value2)
# exceeds <- most_limiting %>%
# filter(.data$scenario == "cur_irr",
# .data$probs %in% c("10","90")) %>%
# select(.data$lake,
# .data$probs,
# .data$value1,
# .data$threshold,
# .data$value2) %>%
# melt(id.vars = c("lake", "probs")) %>%
# mutate(y = .data$value,
# scenario = .data$variable) %>%
# select(.data$lake, .data$probs, .data$scenario, .data$y)
# median <- most_limiting %>%
# filter(.data$probs %in% c("50")) %>%
# melt(id.vars = c("lake", "probs")) %>%
# mutate(y = .data$value,
# scenario = .data$variable) %>%
# select(.data$lake, .data$probs, .data$scenario, .data$y)
exceeds1 <- most_limiting %>%
filter(.data$scenario == "cur_irr",
.data$probs %in% c("10","50", "90")) %>%
select(lake = .data$lake,
probs = .data$probs,
no_irr = .data$value1,
sig_threshold = .data$threshold,
cur_irr = .data$value2)
exceeds2 <- most_limiting %>%
filter(.data$scenario == "fut_irr",
.data$probs %in% c("10","50", "90")) %>%
select(lake = .data$lake,
probs = .data$probs,
no_irr = .data$value1,
sig_threshold = .data$threshold,
fut_irr = .data$value2)
exceeds <- left_join(exceeds1, exceeds2,
by = c("lake", "probs", "no_irr", "sig_threshold")) %>%
melt(id.vars = c("lake", "probs")) %>%
mutate(y = .data$value,
scenario = .data$variable) %>%
select(.data$lake, .data$probs, .data$scenario, .data$y)
median <- exceeds %>% filter(.data$probs == "50")
exceeds <- exceeds %>% filter(.data$probs != "50")
draw_rect_lines <- function(bathymetry, exceeds, median, lake, scenario) {
this_bathy <- bathymetry %>%
filter(.data$lake == !!lake,
!is.na(.data$x)) %>%
arrange(.data$x)
fn_elev_horiz <- approxfun(this_bathy$y, this_bathy$x)
# Shaded Rectangle
shaded_rect <- exceeds %>%
filter(.data$lake == !!lake,
scenario == !!scenario) %>%
mutate(x = fn_elev_horiz(.data$y),
x = ifelse(is.na(.data$x),
max(this_bathy$x, na.rm = TRUE),
.data$x)) %>%
select(.data$x, .data$y)
add_bathy <- this_bathy %>%
filter(.data$y < max(shaded_rect$y),
.data$y > min(shaded_rect$y)) %>%
select(.data$x, .data$y)
shaded_rect <- rbind(shaded_rect, add_bathy) %>%
arrange(desc(.data$y))
shaded_rect2 <- shaded_rect %>%
mutate(x = max(this_bathy$x)) %>%
arrange(.data$y)
shaded_rect <- rbind(shaded_rect, shaded_rect2)
# Median Line
median_line <- median %>%
filter(.data$lake == !!lake,
scenario == !!scenario) %>%
mutate(x = fn_elev_horiz(.data$y)) %>%
select(.data$x, .data$y)
median_line2 <- median_line %>%
mutate(x = max(this_bathy$x))
median_line <- rbind(median_line, median_line2) %>%
arrange(desc(.data$x))
return(list(shaded_rect = shaded_rect, median_line = median_line))
}
plot_profile <- function(bathymetry, lake) {
this_bathy <- bathymetry %>%
filter(.data$lake == !!lake,
!is.na(.data$x)) %>%
arrange(.data$x)
min_level <- min(this_bathy$y)
min_break <- ceiling(min_level/2)*2
max_level <- max(this_bathy$y)
max_horiz <- max(this_bathy$x)
plot_obj <- ggplot() +
geom_line(data = this_bathy,
aes(x = .data$x,
y = .data$y),
color = "black",
size = 1.2) +
scale_y_continuous(expand = c(0,0),
# limits = c(min_level, max_level),
breaks = seq(min_break, max_level, 2)) +
scale_x_reverse(expand = c(0,0),
# limits = c(0,max_horiz),
breaks = seq(0, max_horiz, 100),
minor_breaks = seq(0, max_horiz, 20)) +
labs(x = "Distance (ft)", y = "Elevation (ft)") +
theme_bw()
return(plot_obj)
}
add_shapes <- function(plot_obj, shape_obj, color_val, title) {
plot_obj <- plot_obj +
geom_polygon(data = shape_obj$shaded_rect,
aes(x = .data$x,
y = .data$y),
fill = color_val,
color = NA,
alpha = 0.33) +
geom_line(data = shape_obj$median_line,
aes(x = .data$x,
y = .data$y),
color = color_val) +
labs(title = title) +
theme(plot.title = element_text(family = "Segoe UI Semibold",
size = 18,
hjust = 0.5))
return(plot_obj)
}
add_lake_bottom <- function(plot_obj, bathymetry, lake) {
this_bathy <- bathymetry %>%
filter(.data$lake == !!lake,
!is.na(.data$x)) %>%
arrange(.data$x)
plot_obj <- plot_obj +
geom_line(data = this_bathy,
aes(x = .data$x,
y = .data$y),
color = "black",
size = 1.2)
return(plot_obj)
}
Pleasant Lake
lake <- "Pleasant"
no_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "no_irr")
threshold_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "sig_threshold")
cur_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "cur_irr")
fut_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "fut_irr")
p1 <- plot_profile(bathymetry, lake)
p1 <- add_shapes(p1, no_irr_shapes, "steelblue4", "No Irrigated Agriculture")
p1 <- add_lake_bottom(p1, bathymetry, lake)
p1
p2 <- plot_profile(bathymetry, lake)
p2 <- add_shapes(p2, threshold_shapes, "grey40", "Significant Thresholds")
p2 <- add_lake_bottom(p2, bathymetry, lake)
p2
p3 <- plot_profile(bathymetry, lake)
p3 <- add_shapes(p3, cur_irr_shapes, "goldenrod1", "Current Irrigated Agriculture")
p3 <- add_lake_bottom(p3, bathymetry, lake)
p3
p4 <- plot_profile(bathymetry, lake)
p4 <- add_shapes(p4, fut_irr_shapes, "darkred", "Future Irrigated Agriculture")
p4 <- add_lake_bottom(p4, bathymetry, lake)
p4
fig_width <- 21.57
fig_height <- 7.2
if (save_on) {
ggsave("psnt_no_irr_distances.svg", p1, device = "svg",
width = fig_width, height = fig_height, units = "in", dpi = 300)
ggsave("psnt_sig_threshold_distances.svg", p2, device = "svg",
width = fig_width, height = fig_height, units = "in", dpi = 300)
ggsave("psnt_cur_irr_distances.svg", p3, device = "svg",
width = fig_width, height = fig_height, units = "in", dpi = 300)
ggsave("psnt_fut_irr_distances.svg", p4, device = "svg",
width = fig_width, height = fig_height, units = "in", dpi = 300)
}
Long Lake
lake <- "Long"
no_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "no_irr")
threshold_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "sig_threshold")
cur_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "cur_irr")
fut_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "fut_irr")
p1 <- plot_profile(bathymetry, lake)
p1 <- add_shapes(p1, no_irr_shapes, "steelblue4", "No Irrigated Agriculture")
p1 <- add_lake_bottom(p1, bathymetry, lake)
p1
p2 <- plot_profile(bathymetry, lake)
p2 <- add_shapes(p2, threshold_shapes, "grey40", "Significant Thresholds")
p2 <- add_lake_bottom(p2, bathymetry, lake)
p2
p3 <- plot_profile(bathymetry, lake)
p3 <- add_shapes(p3, cur_irr_shapes, "goldenrod1", "Current Irrigated Agriculture")
p3 <- add_lake_bottom(p3, bathymetry, lake)
p3
p4 <- plot_profile(bathymetry, lake)
p4 <- add_shapes(p4, fut_irr_shapes, "darkred", "Future Irrigated Agriculture")
p4 <- add_lake_bottom(p4, bathymetry, lake)
p4
fig_width <- 9.39
fig_height <- 3.23
if (save_on) {
ggsave("long_no_irr_distances.svg", p1, device = "svg",
width = fig_width, height = fig_height, units = "in", dpi = 300)
ggsave("long_sig_threshold_distances.svg", p3, device = "svg",
width = fig_width, height = fig_height, units = "in", dpi = 300)
ggsave("long_cur_irr_distances.svg", p2, device = "svg",
width = fig_width, height = fig_height, units = "in", dpi = 300)
ggsave("long_fut_irr_distances.svg", p4, device = "svg",
width = fig_width, height = fig_height, units = "in", dpi = 300)
}
Plainfield Lake
lake <- "Plainfield"
no_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "no_irr")
threshold_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "sig_threshold")
cur_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "cur_irr")
fut_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "fut_irr")
p1 <- plot_profile(bathymetry, lake)
p1 <- add_shapes(p1, no_irr_shapes, "steelblue4", "No Irrigated Agriculture")
p1 <- add_lake_bottom(p1, bathymetry, lake)
p1
p2 <- plot_profile(bathymetry, lake)
p2 <- add_shapes(p2, threshold_shapes, "grey40", "Significant Thresholds")
p2 <- add_lake_bottom(p2, bathymetry, lake)
p2
p3 <- plot_profile(bathymetry, lake)
p3 <- add_shapes(p3, cur_irr_shapes, "goldenrod1", "Current Irrigated Agriculture")
p3 <- add_lake_bottom(p3, bathymetry, lake)
p3
p4 <- plot_profile(bathymetry, lake)
p4 <- add_shapes(p4, fut_irr_shapes, "darkred", "Future Irrigated Agriculture")
p4 <- add_lake_bottom(p4, bathymetry, lake)
p4
fig_width <- 10.47
fig_height <- 4.13
if (save_on) {
ggsave("pfl_no_irr_distances.svg", p1, device = "svg",
width = fig_width, height = fig_height, units = "in", dpi = 300)
ggsave("pfl_sig_threshold_distances.svg", p2, device = "svg",
width = fig_width, height = fig_height, units = "in", dpi = 300)
ggsave("pfl_cur_irr_distances.svg", p3, device = "svg",
width = fig_width, height = fig_height, units = "in", dpi = 300)
ggsave("pfl_fut_irr_distances.svg", p4, device = "svg",
width = fig_width, height = fig_height, units = "in", dpi = 300)
}
WDNR-Water-Use/CSLSscenarios documentation built on Nov. 10, 2021, 4:14 p.m.
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This Rmd plots the median level (solid line) and range between the infrequent high and infrequent low levels (shaded bar) for the no-irrigated-agriculture scenario (blue) significant thresholds (grey), and current-irrigated-agriculture scenario (yellow) for each of the the three lakes relative to their average lake profile. These plots do not have the same scale across lakes, but the code is set up to facilitate saving as .svg files at approximately the same scale for each lake.
library(CSLSscenarios) library(dplyr) library(NISTunits) library(ggplot2) library(extrafont) library(reshape2) save_on <- TRUE bathymetry <- CSLSdata::bathymetry %>% mutate(y = NISTmeterTOft(.data$elev_m), x = NISTmeterTOft(.data$horiz_dist_m)) %>% select(.data$lake, .data$y, .data$x) # Most limiting by hydrologic metric ------------------------------------------- most_limiting <- CSLSscenarios::MODFLOW_comparison %>% group_by(.data$sim_type, .data$lake, .data$hydrology, .data$metric, .data$variable, .data$value1, .data$value2, .data$diff, .data$significant_if) %>% mutate(limit_threshold = ifelse(.data$significant_if == "lower", max(.data$threshold, na.rm = TRUE), min(.data$threshold, na.rm = TRUE))) %>% filter(.data$limit_threshold == .data$threshold) %>% ungroup() %>% filter(.data$scenario != "wells_off", .data$sim == 1, .data$metric == "exceedance_level") %>% mutate(probs = .data$variable) %>% select(.data$scenario, .data$lake, .data$probs, .data$value1, .data$threshold, .data$value2) # exceeds <- most_limiting %>% # filter(.data$scenario == "cur_irr", # .data$probs %in% c("10","90")) %>% # select(.data$lake, # .data$probs, # .data$value1, # .data$threshold, # .data$value2) %>% # melt(id.vars = c("lake", "probs")) %>% # mutate(y = .data$value, # scenario = .data$variable) %>% # select(.data$lake, .data$probs, .data$scenario, .data$y) # median <- most_limiting %>% # filter(.data$probs %in% c("50")) %>% # melt(id.vars = c("lake", "probs")) %>% # mutate(y = .data$value, # scenario = .data$variable) %>% # select(.data$lake, .data$probs, .data$scenario, .data$y) exceeds1 <- most_limiting %>% filter(.data$scenario == "cur_irr", .data$probs %in% c("10","50", "90")) %>% select(lake = .data$lake, probs = .data$probs, no_irr = .data$value1, sig_threshold = .data$threshold, cur_irr = .data$value2) exceeds2 <- most_limiting %>% filter(.data$scenario == "fut_irr", .data$probs %in% c("10","50", "90")) %>% select(lake = .data$lake, probs = .data$probs, no_irr = .data$value1, sig_threshold = .data$threshold, fut_irr = .data$value2) exceeds <- left_join(exceeds1, exceeds2, by = c("lake", "probs", "no_irr", "sig_threshold")) %>% melt(id.vars = c("lake", "probs")) %>% mutate(y = .data$value, scenario = .data$variable) %>% select(.data$lake, .data$probs, .data$scenario, .data$y) median <- exceeds %>% filter(.data$probs == "50") exceeds <- exceeds %>% filter(.data$probs != "50")
draw_rect_lines <- function(bathymetry, exceeds, median, lake, scenario) { this_bathy <- bathymetry %>% filter(.data$lake == !!lake, !is.na(.data$x)) %>% arrange(.data$x) fn_elev_horiz <- approxfun(this_bathy$y, this_bathy$x) # Shaded Rectangle shaded_rect <- exceeds %>% filter(.data$lake == !!lake, scenario == !!scenario) %>% mutate(x = fn_elev_horiz(.data$y), x = ifelse(is.na(.data$x), max(this_bathy$x, na.rm = TRUE), .data$x)) %>% select(.data$x, .data$y) add_bathy <- this_bathy %>% filter(.data$y < max(shaded_rect$y), .data$y > min(shaded_rect$y)) %>% select(.data$x, .data$y) shaded_rect <- rbind(shaded_rect, add_bathy) %>% arrange(desc(.data$y)) shaded_rect2 <- shaded_rect %>% mutate(x = max(this_bathy$x)) %>% arrange(.data$y) shaded_rect <- rbind(shaded_rect, shaded_rect2) # Median Line median_line <- median %>% filter(.data$lake == !!lake, scenario == !!scenario) %>% mutate(x = fn_elev_horiz(.data$y)) %>% select(.data$x, .data$y) median_line2 <- median_line %>% mutate(x = max(this_bathy$x)) median_line <- rbind(median_line, median_line2) %>% arrange(desc(.data$x)) return(list(shaded_rect = shaded_rect, median_line = median_line)) } plot_profile <- function(bathymetry, lake) { this_bathy <- bathymetry %>% filter(.data$lake == !!lake, !is.na(.data$x)) %>% arrange(.data$x) min_level <- min(this_bathy$y) min_break <- ceiling(min_level/2)*2 max_level <- max(this_bathy$y) max_horiz <- max(this_bathy$x) plot_obj <- ggplot() + geom_line(data = this_bathy, aes(x = .data$x, y = .data$y), color = "black", size = 1.2) + scale_y_continuous(expand = c(0,0), # limits = c(min_level, max_level), breaks = seq(min_break, max_level, 2)) + scale_x_reverse(expand = c(0,0), # limits = c(0,max_horiz), breaks = seq(0, max_horiz, 100), minor_breaks = seq(0, max_horiz, 20)) + labs(x = "Distance (ft)", y = "Elevation (ft)") + theme_bw() return(plot_obj) } add_shapes <- function(plot_obj, shape_obj, color_val, title) { plot_obj <- plot_obj + geom_polygon(data = shape_obj$shaded_rect, aes(x = .data$x, y = .data$y), fill = color_val, color = NA, alpha = 0.33) + geom_line(data = shape_obj$median_line, aes(x = .data$x, y = .data$y), color = color_val) + labs(title = title) + theme(plot.title = element_text(family = "Segoe UI Semibold", size = 18, hjust = 0.5)) return(plot_obj) } add_lake_bottom <- function(plot_obj, bathymetry, lake) { this_bathy <- bathymetry %>% filter(.data$lake == !!lake, !is.na(.data$x)) %>% arrange(.data$x) plot_obj <- plot_obj + geom_line(data = this_bathy, aes(x = .data$x, y = .data$y), color = "black", size = 1.2) return(plot_obj) }
Pleasant Lake
lake <- "Pleasant" no_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "no_irr") threshold_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "sig_threshold") cur_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "cur_irr") fut_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "fut_irr") p1 <- plot_profile(bathymetry, lake) p1 <- add_shapes(p1, no_irr_shapes, "steelblue4", "No Irrigated Agriculture") p1 <- add_lake_bottom(p1, bathymetry, lake) p1 p2 <- plot_profile(bathymetry, lake) p2 <- add_shapes(p2, threshold_shapes, "grey40", "Significant Thresholds") p2 <- add_lake_bottom(p2, bathymetry, lake) p2 p3 <- plot_profile(bathymetry, lake) p3 <- add_shapes(p3, cur_irr_shapes, "goldenrod1", "Current Irrigated Agriculture") p3 <- add_lake_bottom(p3, bathymetry, lake) p3 p4 <- plot_profile(bathymetry, lake) p4 <- add_shapes(p4, fut_irr_shapes, "darkred", "Future Irrigated Agriculture") p4 <- add_lake_bottom(p4, bathymetry, lake) p4 fig_width <- 21.57 fig_height <- 7.2 if (save_on) { ggsave("psnt_no_irr_distances.svg", p1, device = "svg", width = fig_width, height = fig_height, units = "in", dpi = 300) ggsave("psnt_sig_threshold_distances.svg", p2, device = "svg", width = fig_width, height = fig_height, units = "in", dpi = 300) ggsave("psnt_cur_irr_distances.svg", p3, device = "svg", width = fig_width, height = fig_height, units = "in", dpi = 300) ggsave("psnt_fut_irr_distances.svg", p4, device = "svg", width = fig_width, height = fig_height, units = "in", dpi = 300) }
Long Lake
lake <- "Long" no_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "no_irr") threshold_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "sig_threshold") cur_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "cur_irr") fut_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "fut_irr") p1 <- plot_profile(bathymetry, lake) p1 <- add_shapes(p1, no_irr_shapes, "steelblue4", "No Irrigated Agriculture") p1 <- add_lake_bottom(p1, bathymetry, lake) p1 p2 <- plot_profile(bathymetry, lake) p2 <- add_shapes(p2, threshold_shapes, "grey40", "Significant Thresholds") p2 <- add_lake_bottom(p2, bathymetry, lake) p2 p3 <- plot_profile(bathymetry, lake) p3 <- add_shapes(p3, cur_irr_shapes, "goldenrod1", "Current Irrigated Agriculture") p3 <- add_lake_bottom(p3, bathymetry, lake) p3 p4 <- plot_profile(bathymetry, lake) p4 <- add_shapes(p4, fut_irr_shapes, "darkred", "Future Irrigated Agriculture") p4 <- add_lake_bottom(p4, bathymetry, lake) p4 fig_width <- 9.39 fig_height <- 3.23 if (save_on) { ggsave("long_no_irr_distances.svg", p1, device = "svg", width = fig_width, height = fig_height, units = "in", dpi = 300) ggsave("long_sig_threshold_distances.svg", p3, device = "svg", width = fig_width, height = fig_height, units = "in", dpi = 300) ggsave("long_cur_irr_distances.svg", p2, device = "svg", width = fig_width, height = fig_height, units = "in", dpi = 300) ggsave("long_fut_irr_distances.svg", p4, device = "svg", width = fig_width, height = fig_height, units = "in", dpi = 300) }
Plainfield Lake
lake <- "Plainfield" no_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "no_irr") threshold_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "sig_threshold") cur_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "cur_irr") fut_irr_shapes <- draw_rect_lines(bathymetry, exceeds, median, lake, "fut_irr") p1 <- plot_profile(bathymetry, lake) p1 <- add_shapes(p1, no_irr_shapes, "steelblue4", "No Irrigated Agriculture") p1 <- add_lake_bottom(p1, bathymetry, lake) p1 p2 <- plot_profile(bathymetry, lake) p2 <- add_shapes(p2, threshold_shapes, "grey40", "Significant Thresholds") p2 <- add_lake_bottom(p2, bathymetry, lake) p2 p3 <- plot_profile(bathymetry, lake) p3 <- add_shapes(p3, cur_irr_shapes, "goldenrod1", "Current Irrigated Agriculture") p3 <- add_lake_bottom(p3, bathymetry, lake) p3 p4 <- plot_profile(bathymetry, lake) p4 <- add_shapes(p4, fut_irr_shapes, "darkred", "Future Irrigated Agriculture") p4 <- add_lake_bottom(p4, bathymetry, lake) p4 fig_width <- 10.47 fig_height <- 4.13 if (save_on) { ggsave("pfl_no_irr_distances.svg", p1, device = "svg", width = fig_width, height = fig_height, units = "in", dpi = 300) ggsave("pfl_sig_threshold_distances.svg", p2, device = "svg", width = fig_width, height = fig_height, units = "in", dpi = 300) ggsave("pfl_cur_irr_distances.svg", p3, device = "svg", width = fig_width, height = fig_height, units = "in", dpi = 300) ggsave("pfl_fut_irr_distances.svg", p4, device = "svg", width = fig_width, height = fig_height, units = "in", dpi = 300) }
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Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.