Nothing
#
# # Visible global definition
# ne_coastline <- ne_countries <- st_wrap <- NULL
#
# # Needed objects ----------------------------------------------------------
# library("dplyr")
# library("ggplot2")
# library("sf")
# library("rnaturalearth")
# library("rnaturalearthdata")
# library("tidyr")
# library("patchwork")
#
# world <- rnaturalearth::ne_coastline(scale = "medium", returnclass = "sf")
# world_countries <- rnaturalearth::ne_countries(scale = "medium",
# returnclass = "sf")
# # Fixing polygons crossing dateline
# world <- sf::st_wrap_dateline(world)
# world_countries <- sf::st_wrap_dateline(world_countries)
#
# # Eckert IV projection
# eckertIV <-
# "+proj=eck4 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"
#
# # Background box
# xmin <- sf::st_bbox(world)[["xmin"]]; xmax <- st_bbox(world)[["xmax"]]
# ymin <- sf::st_bbox(world)[["ymin"]]; ymax <- st_bbox(world)[["ymax"]]
# bb <- sf::st_union(sf::st_make_grid(st_bbox(c(xmin = xmin,
# xmax = xmax,
# ymax = ymax,
# ymin = ymin),
# crs = st_crs(4326)),
# n = 100))
#
# # Equator line
# equator <- sf::st_linestring(matrix(c(-180, 0, 180, 0), ncol = 2,
# byrow = TRUE))
# equator <- sf::st_sfc(equator, crs = st_crs(world))
#
# # Color code from Barthlott 2007
# hexcode_barthlott2007 <- c("#fbf9ed", "#f3efcc", "#f6e39e", "#cbe784",
# "#65c66a", "#0e8d4a", "#4a6fbf",
# "#b877c2", "#f24dae", "#ed1c24")
#
# # Retrieving all shapefiles -----------------------------------------------
# gift_shapes <- GIFT_shapes()
#
# # Angiosperm richness map -------------------------------------------------
# angio_rich <- GIFT_richness(taxon_name = "Angiospermae")
#
# rich_map <- dplyr::left_join(gift_shapes, angio_rich, by = "entity_ID") %>%
# dplyr::filter(stats::complete.cases(total))
#
# angio_rich_map <- ggplot2::ggplot(world) +
# ggplot2::geom_sf(color = "gray50") +
# ggplot2::geom_sf(data = rich_map, aes(fill = total + 1)) +
# ggplot2::scale_fill_viridis_c(
# "Species number\n(log-transformed)",
# trans = "log10", labels = scales::number_format(accuracy = 1)) +
# ggplot2::labs(title = "Angiosperms", subtitle = "Projection EckertIV") +
# ggplot2::coord_sf(crs = eckertIV) +
# ggplot2::theme_void()
#
# ggplot2::ggsave("man/figures/angio_rich_map.png",
# plot = angio_rich_map,
# width = 20, height = 10, dpi = 150, units = "cm",
# bg = "white")
#
# ## Fancy version ----
# angio_rich_map2 <- ggplot2::ggplot(world) +
# ggplot2::geom_sf(data = bb, fill = "aliceblue") +
# ggplot2::geom_sf(data = equator, color = "gray50", linetype = "dashed",
# linewidth = 0.1) +
# ggplot2::geom_sf(data = world_countries, fill = "antiquewhite1",
# color = NA) +
# ggplot2::geom_sf(color = "gray50", linewidth = 0.1) +
# ggplot2::geom_sf(data = bb, fill = NA) +
# ggplot2::geom_sf(data = rich_map,
# aes(fill = ifelse(rich_map$entity_class %in%
# c("Island/Mainland", "Mainland",
# "Island Group", "Island Part"),
# total + 1, NA)),
# size = 0.1) +
# ggplot2::geom_point(data = rich_map,
# aes(color = ifelse(rich_map$entity_class %in%
# c("Island"),
# total + 1, NA),
# geometry = geometry),
# stat = "sf_coordinates", size = 1, stroke = 0.5) +
# ggplot2::scale_color_gradientn(
# "Species number", trans = "log10", limits = c(1, 40000),
# colours = hexcode_barthlott2007,
# # colours = RColorBrewer::brewer.pal(5, name = "Greens"),
# breaks = c(1, 10, 100, 1000, 10000, 40000),
# labels = c(1, 10, 100, 1000, 10000, 40000),
# na.value = "transparent") +
# ggplot2::scale_fill_gradientn(
# "Species number", trans = "log10", limits = c(1, 40000),
# colours = hexcode_barthlott2007,
# # colours = RColorBrewer::brewer.pal(5, name = "Greens"),
# breaks = c(1, 10, 100, 1000, 10000, 40000),
# labels = c(1, 10, 100, 1000, 10000, 40000),
# na.value = "transparent") +
# ggplot2::labs(title = "Angiosperms", subtitle = "Projection EckertIV") +
# ggplot2::coord_sf(crs = eckertIV) +
# ggplot2::theme_void()
#
# ggplot2::ggsave("man/figures/angio_rich_map2.png",
# plot = angio_rich_map2,
# width = 20, height = 10, dpi = 150, units = "cm",
# bg = "white")
#
# ## Mediterranean subset ---------------------------------------------------
# med_shape <- gift_shapes[which(gift_shapes$entity_ID %in%
# unique(medit[[2]]$entity_ID)), ]
#
# med_rich <- angio_rich[which(angio_rich$entity_ID %in%
# unique(medit[[2]]$entity_ID)), ]
#
# med_map <- dplyr::left_join(med_shape, med_rich, by = "entity_ID") %>%
# dplyr::filter(stats::complete.cases(total))
#
# angio_medit <- ggplot2::ggplot(world) +
# ggplot2::geom_sf(color = "gray50") +
# ggplot2::geom_sf(data = western_mediterranean,
# fill = "darkblue", color = "black", alpha = 0.1,
# size = 1) +
# ggplot2::geom_sf(data = med_map, aes(fill = total)) +
# ggplot2::scale_fill_viridis_c("Species number") +
# ggplot2::labs(title = "Angiosperms in the Western Mediterranean basin") +
# ggplot2::lims(x = c(-20, 20), y = c(24, 48)) +
# ggplot2::theme_void()
#
# ggplot2::ggsave("man/figures/angio_medit.png",
# plot = angio_medit,
# width = 20, height = 10, dpi = 150, units = "cm",
# bg = "white")
#
# # Anemone nemorosa --------------------------------------------------------
# anemone_distr <- GIFT_species_distribution(
# genus = "Anemone", epithet = "nemorosa", aggregation = TRUE)
#
# anemone_statuses <- anemone_distr %>%
# dplyr::mutate(native = ifelse(native == 1, "native", "non-native"),
# naturalized = ifelse(naturalized == 1, "naturalized",
# "non-naturalized"),
# endemic_list = ifelse(endemic_list == 1, "endemic_list",
# "non-endemic_list")) %>%
# dplyr::select(entity_ID, native, naturalized, endemic_list)
#
# # We rename the statuses based on the distinct combinations
# anemone_statuses <- anemone_statuses %>%
# dplyr::mutate(Status = case_when(
# native == "native" & naturalized == "non-naturalized" ~ "native",
# native == "native" & is.na(naturalized) ~ "native",
# native == "non-native" & is.na(naturalized) ~ "non-native",
# native == "non-native" & naturalized == "naturalized" ~ "naturalized",
# native == "non-native" & naturalized == "non-naturalized" ~ "non-native",
# is.na(native) & is.na(naturalized) ~ "unknown"
# ))
#
# # Merge with the shapes
# anemone_shape <- gift_shapes[which(gift_shapes$entity_ID %in%
# unique(anemone_distr$entity_ID)), ]
# anemone_map <- dplyr::left_join(anemone_shape, anemone_statuses,
# by = "entity_ID")
#
# # Area of distribution with floristic status
# anemone_plot <- ggplot2::ggplot(world) +
# ggplot2::geom_sf(color = "gray70") +
# ggplot2::geom_sf(data = anemone_map, color = "black",
# aes(fill = as.factor(Status))) +
# ggplot2::scale_fill_brewer("Status", palette = "Set2") +
# ggplot2::labs(title = expression(paste("Distribution map of ",
# italic("Anemone nemorosa"))),
# subtitle = "Unprojected (GCS: WGS84)") +
# ggplot2::lims(x = c(-65, 170), y = c(-45, 70)) +
# ggplot2::theme_void()
#
# ggplot2::ggsave("man/figures/anemone_map.png",
# plot = anemone_plot,
# width = 20, height = 10, dpi = 150, units = "cm",
# bg = "white")
#
# ## Fancy version ----------------------------------------------------------
# anemone_map_plot_bg_parts <-
# ggplot2::ggplot(world) +
# ggplot2::geom_sf(data = bb, fill = "aliceblue", color = NA) +
# ggplot2::geom_sf(data = equator, color = "gray50", linetype = "dashed",
# linewidth = 0.1) +
# ggplot2::geom_sf(data = world_countries, fill = "antiquewhite1",
# color = NA) +
# ggplot2::geom_sf(color = "gray50", linewidth = 0.1) +
# ggplot2::geom_sf(data = bb, fill = NA) +
# ggplot2::geom_sf(data = anemone_map, color = "black",
# aes(fill = as.factor(Status))) +
# ggplot2::scale_fill_manual("Status",
# values = c("native" = "#2c7bb6",
# "naturalized" = "#d7191c",
# "non-native" = "#fdae61",
# "unknown" = "#abd9e9")) +
# ggplot2::labs(title = expression(paste("b) Distribution map of ",
# italic("Anemone nemorosa")))) +
# ggplot2::theme_void() +
# ggplot2::theme(axis.title = element_blank(),
# axis.text = element_blank(),
# axis.ticks = element_blank())
#
# anemone_plot2 <-
# (anemone_map_plot_bg_parts +
# ggplot2::lims(x = c(-69, 61), y = c(37, 70)) + # Europe & Newfoundland
# ggplot2::theme(panel.border = element_rect(fill = NA,
# linewidth = 1)) +
# ggplot2::theme(legend.position = "bottom")) +
# (anemone_map_plot_bg_parts +
# ggplot2::lims(x = c(165, 178), y = c(-47, -35)) + # new zealand
# ggplot2::labs(title = "") +
# ggplot2::guides(fill = "none") +
# ggplot2::theme(panel.border = element_rect(fill = NA,
# linewidth = 1))) +
# patchwork::plot_layout()
#
# ggplot2::ggsave("man/figures/anemone_map2.png",
# plot = anemone_plot2,
# width = 20, height = 10, dpi = 150, units = "cm",
# bg = "white")
#
# # Height coverage ---------------------------------------------------------
# angio_height <- GIFT_coverage(what = "trait_coverage",
# taxon_name = "Angiospermae",
# trait_ID = "1.6.2")
#
# angio_height_shape <-
# gift_shapes[which(gift_shapes$entity_ID %in%
# unique(angio_height$entity_ID)), ]
#
# angio_height_map <- dplyr::left_join(
# angio_height_shape, angio_height, by = "entity_ID")
#
# angio_height_map <-
# angio_height_map[complete.cases(angio_height_map$native), ]
#
# angio_height_plot <- ggplot2::ggplot(world) +
# ggplot2::geom_sf(color = "gray50") +
# ggplot2::geom_sf(data = angio_height_map[complete.cases(
# angio_height_map$native), ],
# aes(fill = native)) +
# ggplot2::scale_fill_viridis_c("Coverage (%)") +
# ggplot2::labs(
# title = "Coverage for maximal vegetative height of Angiosperms",
# subtitle = "Projection EckertIV") +
# ggplot2::coord_sf(crs = eckertIV) +
# ggplot2::theme_void()
#
# ggplot2::ggsave("man/figures/angio_height_plot.png",
# plot = angio_height_plot,
# width = 20, height = 10, dpi = 150, units = "cm",
# bg = "white")
#
# ## Fancy version ----------------------------------------------------------
# angio_height_plot2 <- ggplot2::ggplot(world) +
# ggplot2::geom_sf(data = bb, fill = "aliceblue") +
# ggplot2::geom_sf(data = equator, color = "gray50", linetype = "dashed",
# linewidth = 0.1) +
# ggplot2::geom_sf(data = world_countries, fill = "antiquewhite1",
# color = NA) +
# ggplot2::geom_sf(color = "gray50", linewidth = 0.1) +
# ggplot2::geom_sf(data = bb, fill = NA) +
# ggplot2::geom_sf(data = angio_height_map,
# aes(fill = ifelse(angio_height_map$entity_class %in%
# c("Island/Mainland", "Mainland",
# "Island Group", "Island Part"),
# 100*native, NA)), size = 0.1) +
# ggplot2::geom_point(data = angio_height_map,
# aes(color = ifelse(angio_height_map$entity_class %in%
# c("Island"),
# 100*native, NA),
# geometry = geometry),
# stat = "sf_coordinates", size = 1, stroke = 0.5) +
# ggplot2::scale_color_gradientn(
# "Coverage (%)",
# colours = rev(RColorBrewer::brewer.pal(9, name = "PuBuGn")),
# limits = c(0, 100),
# na.value = "transparent") +
# ggplot2::scale_fill_gradientn(
# "Coverage (%)",
# colours = rev(RColorBrewer::brewer.pal(9, name = "PuBuGn")),
# limits = c(0, 100),
# na.value = "transparent") +
# ggplot2::labs(title = "Coverage for maximal vegetative height of Angiosperms",
# subtitle = "Projection EckertIV") +
# ggplot2::coord_sf(crs = eckertIV) +
# ggplot2::theme_void()
#
# ggplot2::ggsave("man/figures/angio_height_plot2.png",
# plot = angio_height_plot2,
# width = 20, height = 10, dpi = 150, units = "cm",
# bg = "white")
#
# # MAT ---------------------------------------------------------------------
# world_temp <- GIFT_env(entity_ID = unique(angio_rich$entity_ID),
# rasterlayer = c("wc2.0_bio_30s_01"),
# sumstat = c("mean"))
#
# temp_shape <- gift_shapes[which(gift_shapes$entity_ID %in%
# unique(angio_rich$entity_ID)), ]
#
# temp_map <- dplyr::left_join(temp_shape, world_temp, by = "entity_ID")
#
# temp_plot <- ggplot2::ggplot(world) +
# ggplot2::geom_sf(color = "gray50") +
# ggplot2::geom_sf(data = temp_map, aes(fill = mean_wc2.0_bio_30s_01)) +
# ggplot2::scale_fill_viridis_c("Celsius degrees") +
# ggplot2::labs(title = "Average temperature",
# subtitle = "Projection EckertIV") +
# ggplot2::coord_sf(crs = eckertIV) +
# ggplot2::theme_void()
#
# ggplot2::ggsave("man/figures/temp_plot.png",
# plot = temp_plot,
# width = 20, height = 10, dpi = 150, units = "cm",
# bg = "white")
#
# ## Fancy version ----------------------------------------------------------
#
# temp_plot2 <- ggplot2::ggplot(world) +
# ggplot2::geom_sf(data = bb, fill = "aliceblue") +
# ggplot2::geom_sf(data = equator, color = "gray50", linetype = "dashed",
# linewidth = 0.1) +
# ggplot2::geom_sf(data = world_countries, fill = "antiquewhite1",
# color = NA) +
# ggplot2::geom_sf(color = "gray50", linewidth = 0.1) +
# ggplot2::geom_sf(data = bb, fill = NA) +
# ggplot2::geom_sf(data = temp_map,
# aes(fill = ifelse(temp_map$entity_class %in%
# c("Island/Mainland", "Mainland",
# "Island Group", "Island Part"),
# mean_wc2.0_bio_30s_01, NA)), size = 0.1) +
# ggplot2::geom_point(data = temp_map,
# aes(color = ifelse(temp_map$entity_class %in%
# c("Island"),
# mean_wc2.0_bio_30s_01, NA),
# geometry = geometry),
# stat = "sf_coordinates", size = 1, stroke = 0.5) +
# ggplot2::scale_color_gradientn(
# "°C",
# colours = RColorBrewer::brewer.pal(9, name = "Reds"),
# limits = c(-20, 30),
# na.value = "transparent") +
# ggplot2::scale_fill_gradientn(
# "°C",
# colours = RColorBrewer::brewer.pal(9, name = "Reds"),
# limits = c(-20, 30),
# na.value = "transparent") +
# ggplot2::labs(title = "Average temperature",
# subtitle = "Projection EckertIV") +
# ggplot2::coord_sf(crs = eckertIV) +
# ggplot2::theme_void()
#
# ggplot2::ggsave("man/figures/temp_plot2.png",
# plot = temp_plot2,
# width = 20, height = 10, dpi = 150, units = "cm",
# bg = "white")
#
# # Phylogeny ---------------------------------------------------------------
# phy <- GIFT_phylogeny(clade = "Tracheophyta", GIFT_version = "beta")
# tax <- GIFT_taxonomy(GIFT_version = "beta")
# gift_sp <- GIFT_species(GIFT_version = "beta")
#
# gf <- GIFT_traits(trait_IDs = "1.2.1", agreement = 0.66, bias_ref = FALSE,
# bias_deriv = FALSE, GIFT_version = "beta")
#
# # Replacing space with _ for the species names
# gf$work_species <- gsub(" ", "_", gf$work_species, fixed = TRUE)
#
# # Retrieving family of each species
# sp_fam <- GIFT_taxgroup(work_ID = unique(gift_sp$work_ID),
# taxon_lvl = "family", GIFT_version = "beta")
# sp_genus_fam <- data.frame(
# work_ID = unique(gift_sp$work_ID),
# work_species = unique(gift_sp$work_species),
# family = sp_fam)
# sp_genus_fam <- dplyr::left_join(sp_genus_fam,
# gift_sp[, c("work_ID", "work_genus")],
# by = "work_ID")
# colnames(sp_genus_fam)[colnames(sp_genus_fam) == "work_genus"] <- "genus"
#
# # Problem with hybrid species on the tip labels of the phylo tree
# phy$tip.label[substring(phy$tip.label, 1, 2) == "x_"] <-
# substring(phy$tip.label[substring(phy$tip.label, 1, 2) == "x_"],
# 3,
# nchar(phy$tip.label[substring(phy$tip.label, 1, 2) == "×_"]))
#
# phy$tip.label[substring(phy$tip.label, 1, 2) == "×_"] <-
# substring(phy$tip.label[substring(phy$tip.label, 1, 2) == "×_"],
# 3,
# nchar(phy$tip.label[substring(phy$tip.label, 1, 2) == "×_"]))
#
# sp_genus_fam <- dplyr::left_join(sp_genus_fam,
# gf[, c("work_ID", "trait_value_1.2.1")],
# by = "work_ID")
#
# genus_gf <- sp_genus_fam %>%
# dplyr::group_by(genus) %>%
# dplyr::mutate(prop_gf = round(100*sum(is.na(trait_value_1.2.1))/n(),
# 2)) %>%
# dplyr::ungroup() %>%
# dplyr::select(-work_ID, -work_species, -family, -trait_value_1.2.1) %>%
# dplyr::distinct(.keep_all = TRUE)
#
# fam_gf <- sp_genus_fam %>%
# dplyr::group_by(family) %>%
# dplyr::mutate(prop_gf = round(100*sum(is.na(trait_value_1.2.1))/n(),
# 2)) %>%
# dplyr::ungroup() %>%
# dplyr::select(-work_ID, -work_species, -genus, -trait_value_1.2.1) %>%
# dplyr::distinct(.keep_all = TRUE)
#
# sp_genus_fam$species <- gsub("([[:punct:]])|\\s+", "_",
# sp_genus_fam$work_species)
#
# # Keeping one species per genus only
# one_sp_per_gen <- data.frame()
# for(i in 1:n_distinct(sp_genus_fam$genus)){ # loop over genera
# # Focal genus
# focal_gen <- unique(sp_genus_fam$genus)[i]
# # All species in that genus
# gen_sp_i <- sp_genus_fam[which(sp_genus_fam$genus == focal_gen),
# "species"]
# # Species from the genus available in the phylogeny
# gen_sp_i <- gen_sp_i[gen_sp_i %in% phy$tip.label]
# # Taking the first one (if at least one is available)
# gen_sp_i <- gen_sp_i[1]
#
# one_sp_per_gen <- rbind(one_sp_per_gen,
# data.frame(species = gen_sp_i,
# genus = focal_gen))
# }
#
# # Adding the trait coverage per genus
# one_sp_per_gen <- dplyr::left_join(one_sp_per_gen, genus_gf, by = "genus")
#
# # Adding the trait coverage per family
# one_sp_per_gen <- dplyr::left_join(
# one_sp_per_gen,
# sp_genus_fam[!duplicated(sp_genus_fam$genus),
# c("genus", "family")],
# by = "genus")
# colnames(one_sp_per_gen)[colnames(one_sp_per_gen) == "prop_gf"] <-
# "prop_gf_gen"
# one_sp_per_gen <- left_join(one_sp_per_gen, fam_gf, by = "family")
# colnames(one_sp_per_gen)[colnames(one_sp_per_gen) == "prop_gf"] <-
# "prop_gf_fam"
#
# phy_gen <- ape::keep.tip(
# phy = phy,
# tip = one_sp_per_gen[complete.cases(one_sp_per_gen$species), "species"])
#
# library("BiocManager")
# install("ggtree")
# library("ggtree")
# library("tidytree")
# install("ggtreeExtra")
# library("ggtreeExtra")
#
# phy_tree_plot <- ggtree(phy_gen, color = "grey70",
# layout = "circular") %<+% one_sp_per_gen +
# geom_fruit(geom = geom_tile,
# mapping = aes(fill = prop_gf_gen),
# width = 50,
# offset = 0.1) +
# geom_fruit(geom = geom_tile,
# mapping = aes(color = prop_gf_fam, fill = prop_gf_fam),
# width = 50,
# offset = 0.1,
# show.legend = FALSE) +
# scale_color_viridis_c() +
# scale_fill_viridis_c("Growth form availability per genus (%)") +
# theme(legend.position = "bottom")
#
# ggplot2::ggsave("man/figures/phy_tree_plot.png",
# plot = phy_tree_plot,
# width = 20, height = 10, dpi = 150, units = "cm",
# bg = "white")
#
# # Advanced vignette -----------------------------------------------------------
# med_centroid_inside <- GIFT_spatial(shp = western_mediterranean,
# overlap = "centroid_inside")
# med_extent_intersect <- GIFT_spatial(shp = western_mediterranean,
# overlap = "extent_intersect")
# med_shape_intersect <- GIFT_spatial(shp = western_mediterranean,
# overlap = "shape_intersect")
# med_shape_inside <- GIFT_spatial(shp = western_mediterranean,
# overlap = "shape_inside")
#
# geodata_extent_intersect <- GIFT_shapes(med_extent_intersect$entity_ID)
#
# geodata_shape_inside <-
# geodata_extent_intersect[which(geodata_extent_intersect$entity_ID %in%
# med_shape_inside$entity_ID), ]
# geodata_centroid_inside <-
# geodata_extent_intersect[which(geodata_extent_intersect$entity_ID %in%
# med_centroid_inside$entity_ID), ]
# geodata_shape_intersect <-
# geodata_extent_intersect[which(geodata_extent_intersect$entity_ID %in%
# med_shape_intersect$entity_ID), ]
#
# png("man/figures/advanced_overlap.png",
# width = 1440, height = 1440, units = "px",
# bg = "white")
# par_overlap <- par(mfrow = c(2, 2), mai = c(0, 0.5, 1.5, 0.5))
# plot(sf::st_geometry(geodata_shape_inside),
# col = geodata_shape_inside$entity_ID,
# main = paste("shape inside\n",
# length(unique(med_shape_inside$entity_ID)),
# "polygons"),
# cex.main = 5)
# plot(sf::st_geometry(western_mediterranean), lwd = 2, add = TRUE)
#
# plot(sf::st_geometry(geodata_centroid_inside),
# col = geodata_centroid_inside$entity_ID,
# main = paste("centroid inside\n",
# length(unique(med_centroid_inside$entity_ID)),
# "polygons"),
# cex.main = 5)
# points(geodata_centroid_inside$point_x, geodata_centroid_inside$point_y)
# plot(sf::st_geometry(western_mediterranean), lwd = 2, add = TRUE)
#
# plot(sf::st_geometry(geodata_shape_intersect),
# col = geodata_shape_intersect$entity_ID,
# main = paste("shape intersect\n",
# length(unique(med_shape_intersect$entity_ID)),
# "polygons"),
# cex.main = 5)
# plot(sf::st_geometry(western_mediterranean), lwd = 2, add = TRUE)
#
# plot(sf::st_geometry(geodata_extent_intersect),
# col = geodata_extent_intersect$entity_ID,
# main = paste("extent intersect\n",
# length(unique(med_extent_intersect$entity_ID)),
# "polygons"),
# cex.main = 5)
# plot(sf::st_geometry(western_mediterranean), lwd = 2, add = TRUE)
# par(par_overlap)
# dev.off()
## Overlap GMBA ---------------------------------------------------------------
# library("GIFT")
#
# # Overlap table between GIFT and GMBA regions
# gmba_overlap <- GIFT_overlap(resource = "gmba", GIFT_version = "beta")
# library("sf")
# gmba <- st_read(
# "../../polygon_resources/GMBA/GMBA_mountain_inventory_V1.0_ID.shp")
# geoentities <- st_read(
# "../../polygon_resources/geoentities/geoentities_simple.shp")
#
# st_crs(gmba) <- st_crs(geoentities)
#
# gmba_overlap[which(gmba_overlap$entity_ID == 11861 &
# gmba_overlap$gmba_ID == 731), ]
#
# library("ggplot2")
# ggplot(gmba[which(gmba$ID == 731), ]) +
# geom_sf(color = "black", linewidth = 1) +
# geom_sf(data = geoentities[which(geoentities$entt_ID == 11861), ],
# fill = "black", alpha = 0.5) +
# labs(title = paste0("GIFT region: ",
# geoentities[which(geoentities$entt_ID == 11861), ]$ge_ntty)) +
# theme_void()
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