analyze_results.R
In Rosenkrands/dz: Dynamic Zoning
library(dz)
library(tidyverse)
library(xtable)
combined_results <- readRDS("combined_results.rds") |>
# filter(clustering_method != "heuristic") |>
bind_rows(
readRDS("combined_results_relevancy.rds") |> filter(clustering_method == "heuristic relevancy")
) |> ungroup()
combined_results <- combined_results |>
mutate(mean_ur_score = sapply(ur_score, mean),
median_ur_score = sapply(ur_score, median),
mean_candidate_outside = sapply(candidate_outside, mean)) |>
select(clustering_method, k, L, sr_score, mean_ur_score, median_ur_score, mean_candidate_outside, p_inst, clust, sr, scenarios, ur_score, candidate_outside) |>
mutate(clustering_method = factor(clustering_method, levels = c("routing-based", "heuristic", "heuristic relevancy"),
labels = c("RB", "HC", "HR")))
# Comparison of clustering method by sr_score
combined_results |>
mutate(`Number of agents` = factor(k)) |>
ggplot(aes(x = L, y = sr_score, color = clustering_method)) +
geom_point(aes(shape = clustering_method)) +
geom_line(aes(linetype = clustering_method, group = paste(factor(k), clustering_method))) +
facet_wrap(~`Number of agents`, labeller = label_both) +
theme_bw() + labs(x = "Total L across team",
y = "Total score of starting routes",
color = "Solution method",
linetype = "Solution method",
shape = "Solution method") +
theme(legend.position = "top")
ggsave("./figures_for_report/starting_routes.pdf", width = 8, height = 3.5)
# Comparison of clustering method by mean_ur_score
combined_results |>
mutate(`Number of agents` = factor(k)) |>
ggplot(aes(x = L, y = mean_ur_score, color = clustering_method)) +
geom_point(aes(shape = clustering_method)) +
geom_line(aes(linetype = clustering_method, group = paste(factor(k), clustering_method))) +
facet_wrap(~`Number of agents`, labeller = label_both) +
theme_bw() + labs(x = "Total L across team",
y = "Total mean score of updated routes",
color = "Solution method",
linetype = "Solution method",
shape = "Solution method") +
theme(legend.position = "top")
ggsave("./figures_for_report/updated_routes.pdf", width = 8, height = 3.5)
# Comparison of clustering method by mean_ur_score
combined_results |>
mutate(`Number of agents` = factor(k)) |>
rowwise() |>
mutate(
ur_score = list(sapply(scenarios, function(x) do.call(sum, x$total_realized_score)))
) |>
unnest(cols = ur_score) |>
ungroup() |>
group_by(clustering_method, `Number of agents`, L) |>
summarise(mean_ur = mean(ur_score),
min_ur = min(ur_score),
max_ur = max(ur_score)) |>
ggplot(aes(x = L, color = clustering_method)) +
# geom_point(aes(y = mean_ur), size = 1) +
geom_errorbar(aes(ymin = min_ur, ymax = max_ur), width = 6)+#, position = position_dodge(.5)) +
# geom_line(aes(y = mean_ur, linetype = clustering_method, group = paste(`Number of agents`, clustering_method))) +
facet_wrap(~`Number of agents`, labeller = label_both) +
theme_bw() + labs(x = "Total L across team",
y = "Total mean score of updated routes",
color = "Solution method",
linetype = "Solution method",
shape = "Solution method") +
theme(legend.position = "top")
ggsave("./figures_for_report/updated_routes_w_more_points.pdf", width = 8, height = 3.5)
# Regression test for difference in updated route score
ur_data <- combined_results |>
ungroup() |>
select(clustering_method, k, L, scenarios) |>
rowwise() |>
mutate(
ur_score = list(sapply(scenarios, function(x) do.call(sum, x$total_realized_score)))
) |>
unnest(cols = ur_score) |>
select(-scenarios)
sum_score <- summary(lm(ur_score ~ clustering_method, data = ur_data))
xtable(sum_score)
summary(lm(ur_score ~ clustering_method, data = ur_data |> filter(k == 3)))
summary(lm(ur_score ~ clustering_method, data = ur_data |> filter(k == 3, L < 300)))
summary(lm(ur_score ~ clustering_method, data = ur_data |> filter(k == 4)))
summary(lm(ur_score ~ clustering_method, data = ur_data |> filter(k == 4, L < 300)))
# Regression test for difference in candidates outside
cu_data <- combined_results |>
ungroup() |>
select(clustering_method, k, L, scenarios) |>
rowwise() |>
mutate(
cu = list(sapply(scenarios, function(x) do.call(sum, x$candidate_outside)))
) |>
unnest(cols = cu) |>
select(-scenarios)
sum_cu <- summary(lm(cu ~ clustering_method, data = cu_data))
xtable(sum_cu)
summary(lm(cu ~ clustering_method, data = cu_data |> filter(k == 3)))
summary(lm(cu ~ clustering_method, data = cu_data |> filter(k == 4)))
sum_cu_300 <- summary(lm(cu ~ clustering_method, data = cu_data |> filter(L < 300)))
xtable(sum_cu_300)
summary(lm(cu ~ clustering_method, data = cu_data |> filter(k == 3, L <= 300)))
summary(lm(cu ~ clustering_method, data = cu_data |> filter(k == 4, L < 300)))
summary(lm(cu ~ clustering_method, data = cu_data))
# Comparison of clustering method by mean number of candidates outside the zone
combined_results |>
mutate(`Number of agents` = factor(k)) |>
ggplot(aes(x = L, y = mean_candidate_outside, color = clustering_method)) +
geom_point(aes(shape = clustering_method)) +
geom_line(aes(linetype = clustering_method, group = paste(factor(k), clustering_method))) +
facet_wrap(~`Number of agents`, labeller = label_both) +
theme_bw() +
theme(legend.position = "top") +
labs(x = "Total L across team", y = "Mean total number of candidates outside",
color = "Solution method", shape = "Solution method", linetype = "Solution method")
ggsave("./figures_for_report/Outside_clsuter_graph.pdf", width = 8, height = 3.5)
# Effect of adding another UAV
uav_effect_data <- combined_results |>
filter(L/k == 100)
uav_effect_data |>
pivot_longer(cols = c(sr_score, mean_ur_score)) |>
mutate(name = factor(name,
levels = c("sr_score", "mean_ur_score"),
labels = c("Starting", "Updated")
)) |>
ggplot(aes(x = k, y = value, color = clustering_method, linetype = name, group = paste(clustering_method, name))) +
geom_point(aes(shape = clustering_method)) +
geom_line() +
facet_wrap(~clustering_method) +
scale_x_continuous(n.breaks = 3) +
theme_bw() + theme(legend.position = "top") +
labs(x = "Number of agents", y = "Total score", linetype = "Route type", color = "Solution method", shape = "Solution method")
ggsave("./figures_for_report/uav_effect.pdf", width = 8, height = 3.5)
uav_effect_minmax <- combined_results |>
filter(L/k == 100) |>
mutate(`Number of agents` = factor(k)) |>
rowwise() |>
mutate(
ur_score = list(sapply(scenarios, function(x) do.call(sum, x$total_realized_score)))
) |>
unnest(cols = ur_score) |>
ungroup() |>
group_by(clustering_method, `Number of agents`, L) |>
summarise(mean_ur = mean(ur_score),
min_ur = min(ur_score),
max_ur = max(ur_score))
uav_effect_data |>
mutate(`Number of agents` = factor(k)) |>
ggplot(aes(x = `Number of agents`, group = paste(clustering_method))) +
geom_point(aes(y = sr_score, color = clustering_method), size = .8) +
geom_line(aes(y = sr_score, linetype = "Starting", color = clustering_method)) +
# geom_point(data = uav_effect_minmax, aes(y = mean_ur), size = 1) +
geom_line(data = uav_effect_minmax, aes(y = mean_ur, linetype = "Updated")) +
geom_errorbar(data = uav_effect_minmax, aes(ymin = min_ur, ymax = max_ur), width = .2)+
facet_wrap(~clustering_method) +
# scale_x_continuous(n.breaks = 3) +
theme_bw() + theme(legend.position = "top") +
labs(x = "Number of agents", y = "Total score", linetype = "Route type", color = "Solution method", shape = "Solution method")
ggsave("./figures_for_report/uav_effect_w_error_new_sr.pdf", width = 8, height = 3.5)
cowplot::plot_grid(
plot(uav_effect_data$sr[[1]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[4]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[7]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[2]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[5]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[8]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[3]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[6]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[9]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
nrow = 3
)
ggsave("./figures_for_report/compare_clustering_methods_sr.png", width = 13, height = 16.5)
find_best_sc <- function(row_id) {
sc_score <- sapply(
uav_effect_data$scenarios[[row_id]],
function(x) do.call(sum, x$total_realized_score)
)
which.max(sc_score)
}
sapply(1:9, find_best_sc)
7
6
2
1
1
37
7
7
50
# cowplot::plot_grid(
# plot(uav_effect_data$scenarios[[1]][[31]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[4]][[16]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[7]][[16]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[2]][[4]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[5]][[28]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[8]][[1]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[3]][[1]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[6]][[1]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[9]][[39]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# nrow = 3,
# labels = "AUTO"
# )
cowplot::plot_grid(
plot(uav_effect_data$scenarios[[1]][[7]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[2]][[6]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[3]][[2]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[4]][[1]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[5]][[1]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[6]][[37]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[7]][[7]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[8]][[7]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[9]][[50]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
nrow = 3,
labels = "AUTO"
)
ggsave("./figures_for_report/compare_clustering_methods_ur.png", width = 13, height = 16.5)
Rosenkrands/dz documentation built on June 26, 2022, 10:43 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
library(dz)
library(tidyverse)
library(xtable)
combined_results <- readRDS("combined_results.rds") |>
# filter(clustering_method != "heuristic") |>
bind_rows(
readRDS("combined_results_relevancy.rds") |> filter(clustering_method == "heuristic relevancy")
) |> ungroup()
combined_results <- combined_results |>
mutate(mean_ur_score = sapply(ur_score, mean),
median_ur_score = sapply(ur_score, median),
mean_candidate_outside = sapply(candidate_outside, mean)) |>
select(clustering_method, k, L, sr_score, mean_ur_score, median_ur_score, mean_candidate_outside, p_inst, clust, sr, scenarios, ur_score, candidate_outside) |>
mutate(clustering_method = factor(clustering_method, levels = c("routing-based", "heuristic", "heuristic relevancy"),
labels = c("RB", "HC", "HR")))
# Comparison of clustering method by sr_score
combined_results |>
mutate(`Number of agents` = factor(k)) |>
ggplot(aes(x = L, y = sr_score, color = clustering_method)) +
geom_point(aes(shape = clustering_method)) +
geom_line(aes(linetype = clustering_method, group = paste(factor(k), clustering_method))) +
facet_wrap(~`Number of agents`, labeller = label_both) +
theme_bw() + labs(x = "Total L across team",
y = "Total score of starting routes",
color = "Solution method",
linetype = "Solution method",
shape = "Solution method") +
theme(legend.position = "top")
ggsave("./figures_for_report/starting_routes.pdf", width = 8, height = 3.5)
# Comparison of clustering method by mean_ur_score
combined_results |>
mutate(`Number of agents` = factor(k)) |>
ggplot(aes(x = L, y = mean_ur_score, color = clustering_method)) +
geom_point(aes(shape = clustering_method)) +
geom_line(aes(linetype = clustering_method, group = paste(factor(k), clustering_method))) +
facet_wrap(~`Number of agents`, labeller = label_both) +
theme_bw() + labs(x = "Total L across team",
y = "Total mean score of updated routes",
color = "Solution method",
linetype = "Solution method",
shape = "Solution method") +
theme(legend.position = "top")
ggsave("./figures_for_report/updated_routes.pdf", width = 8, height = 3.5)
# Comparison of clustering method by mean_ur_score
combined_results |>
mutate(`Number of agents` = factor(k)) |>
rowwise() |>
mutate(
ur_score = list(sapply(scenarios, function(x) do.call(sum, x$total_realized_score)))
) |>
unnest(cols = ur_score) |>
ungroup() |>
group_by(clustering_method, `Number of agents`, L) |>
summarise(mean_ur = mean(ur_score),
min_ur = min(ur_score),
max_ur = max(ur_score)) |>
ggplot(aes(x = L, color = clustering_method)) +
# geom_point(aes(y = mean_ur), size = 1) +
geom_errorbar(aes(ymin = min_ur, ymax = max_ur), width = 6)+#, position = position_dodge(.5)) +
# geom_line(aes(y = mean_ur, linetype = clustering_method, group = paste(`Number of agents`, clustering_method))) +
facet_wrap(~`Number of agents`, labeller = label_both) +
theme_bw() + labs(x = "Total L across team",
y = "Total mean score of updated routes",
color = "Solution method",
linetype = "Solution method",
shape = "Solution method") +
theme(legend.position = "top")
ggsave("./figures_for_report/updated_routes_w_more_points.pdf", width = 8, height = 3.5)
# Regression test for difference in updated route score
ur_data <- combined_results |>
ungroup() |>
select(clustering_method, k, L, scenarios) |>
rowwise() |>
mutate(
ur_score = list(sapply(scenarios, function(x) do.call(sum, x$total_realized_score)))
) |>
unnest(cols = ur_score) |>
select(-scenarios)
sum_score <- summary(lm(ur_score ~ clustering_method, data = ur_data))
xtable(sum_score)
summary(lm(ur_score ~ clustering_method, data = ur_data |> filter(k == 3)))
summary(lm(ur_score ~ clustering_method, data = ur_data |> filter(k == 3, L < 300)))
summary(lm(ur_score ~ clustering_method, data = ur_data |> filter(k == 4)))
summary(lm(ur_score ~ clustering_method, data = ur_data |> filter(k == 4, L < 300)))
# Regression test for difference in candidates outside
cu_data <- combined_results |>
ungroup() |>
select(clustering_method, k, L, scenarios) |>
rowwise() |>
mutate(
cu = list(sapply(scenarios, function(x) do.call(sum, x$candidate_outside)))
) |>
unnest(cols = cu) |>
select(-scenarios)
sum_cu <- summary(lm(cu ~ clustering_method, data = cu_data))
xtable(sum_cu)
summary(lm(cu ~ clustering_method, data = cu_data |> filter(k == 3)))
summary(lm(cu ~ clustering_method, data = cu_data |> filter(k == 4)))
sum_cu_300 <- summary(lm(cu ~ clustering_method, data = cu_data |> filter(L < 300)))
xtable(sum_cu_300)
summary(lm(cu ~ clustering_method, data = cu_data |> filter(k == 3, L <= 300)))
summary(lm(cu ~ clustering_method, data = cu_data |> filter(k == 4, L < 300)))
summary(lm(cu ~ clustering_method, data = cu_data))
# Comparison of clustering method by mean number of candidates outside the zone
combined_results |>
mutate(`Number of agents` = factor(k)) |>
ggplot(aes(x = L, y = mean_candidate_outside, color = clustering_method)) +
geom_point(aes(shape = clustering_method)) +
geom_line(aes(linetype = clustering_method, group = paste(factor(k), clustering_method))) +
facet_wrap(~`Number of agents`, labeller = label_both) +
theme_bw() +
theme(legend.position = "top") +
labs(x = "Total L across team", y = "Mean total number of candidates outside",
color = "Solution method", shape = "Solution method", linetype = "Solution method")
ggsave("./figures_for_report/Outside_clsuter_graph.pdf", width = 8, height = 3.5)
# Effect of adding another UAV
uav_effect_data <- combined_results |>
filter(L/k == 100)
uav_effect_data |>
pivot_longer(cols = c(sr_score, mean_ur_score)) |>
mutate(name = factor(name,
levels = c("sr_score", "mean_ur_score"),
labels = c("Starting", "Updated")
)) |>
ggplot(aes(x = k, y = value, color = clustering_method, linetype = name, group = paste(clustering_method, name))) +
geom_point(aes(shape = clustering_method)) +
geom_line() +
facet_wrap(~clustering_method) +
scale_x_continuous(n.breaks = 3) +
theme_bw() + theme(legend.position = "top") +
labs(x = "Number of agents", y = "Total score", linetype = "Route type", color = "Solution method", shape = "Solution method")
ggsave("./figures_for_report/uav_effect.pdf", width = 8, height = 3.5)
uav_effect_minmax <- combined_results |>
filter(L/k == 100) |>
mutate(`Number of agents` = factor(k)) |>
rowwise() |>
mutate(
ur_score = list(sapply(scenarios, function(x) do.call(sum, x$total_realized_score)))
) |>
unnest(cols = ur_score) |>
ungroup() |>
group_by(clustering_method, `Number of agents`, L) |>
summarise(mean_ur = mean(ur_score),
min_ur = min(ur_score),
max_ur = max(ur_score))
uav_effect_data |>
mutate(`Number of agents` = factor(k)) |>
ggplot(aes(x = `Number of agents`, group = paste(clustering_method))) +
geom_point(aes(y = sr_score, color = clustering_method), size = .8) +
geom_line(aes(y = sr_score, linetype = "Starting", color = clustering_method)) +
# geom_point(data = uav_effect_minmax, aes(y = mean_ur), size = 1) +
geom_line(data = uav_effect_minmax, aes(y = mean_ur, linetype = "Updated")) +
geom_errorbar(data = uav_effect_minmax, aes(ymin = min_ur, ymax = max_ur), width = .2)+
facet_wrap(~clustering_method) +
# scale_x_continuous(n.breaks = 3) +
theme_bw() + theme(legend.position = "top") +
labs(x = "Number of agents", y = "Total score", linetype = "Route type", color = "Solution method", shape = "Solution method")
ggsave("./figures_for_report/uav_effect_w_error_new_sr.pdf", width = 8, height = 3.5)
cowplot::plot_grid(
plot(uav_effect_data$sr[[1]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[4]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[7]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[2]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[5]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[8]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[3]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[6]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$sr[[9]], inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
nrow = 3
)
ggsave("./figures_for_report/compare_clustering_methods_sr.png", width = 13, height = 16.5)
find_best_sc <- function(row_id) {
sc_score <- sapply(
uav_effect_data$scenarios[[row_id]],
function(x) do.call(sum, x$total_realized_score)
)
which.max(sc_score)
}
sapply(1:9, find_best_sc)
7
6
2
1
1
37
7
7
50
# cowplot::plot_grid(
# plot(uav_effect_data$scenarios[[1]][[31]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[4]][[16]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[7]][[16]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[2]][[4]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[5]][[28]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[8]][[1]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[3]][[1]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[6]][[1]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# plot(uav_effect_data$scenarios[[9]][[39]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
# nrow = 3,
# labels = "AUTO"
# )
cowplot::plot_grid(
plot(uav_effect_data$scenarios[[1]][[7]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[2]][[6]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[3]][[2]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[4]][[1]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[5]][[1]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[6]][[37]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[7]][[7]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[8]][[7]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
plot(uav_effect_data$scenarios[[9]][[50]]$ur, inst = uav_effect_data$p_inst[[1]]) + theme(legend.position = "none"),
nrow = 3,
labels = "AUTO"
)
ggsave("./figures_for_report/compare_clustering_methods_ur.png", width = 13, height = 16.5)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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.