## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 4 ) library(ggplot2) library(ggmapcn) world_files <- c("world_countries.rda", "world_coastlines.rda", "world_boundaries.rda") data_dirs <- c(system.file("extdata", package = "ggmapcn"), tools::R_user_dir("ggmapcn", "cache"), tools::R_user_dir("ggmapcn", "data")) available <- vapply(world_files, function(f) { any(file.exists(file.path(data_dirs, f))) }, logical(1)) knitr::opts_chunk$set(eval = all(available)) ## ----basic_map, fig.alt="A standard world map using WGS84 projection with default styling."---- # ggplot() + # geom_world() + # theme_void() ## ----explicit_crs, fig.alt="A world map explicitly set to EPSG:4326 projection."---- # ggplot() + # geom_world(crs = 4326) + # coord_sf(crs = 4326) + # theme_void() ## ----hide_ocean, fig.alt="A world map with the blue ocean layer removed, showing grey countries on a white background."---- # ggplot() + # geom_world( # show_ocean = FALSE, # country_fill = "grey90" # ) + # theme_minimal() ## ----hide_admin, fig.alt="A world map showing continental landmasses without internal country borders."---- # ggplot() + # geom_world( # show_admin_boundaries = FALSE, # country_fill = "white" # ) + # theme_minimal() ## ----minimal_map, fig.alt="A minimalist world map showing only land shapes with no ocean or borders."---- # ggplot() + # geom_world( # show_ocean = FALSE, # show_admin_boundaries = FALSE # ) + # theme_minimal() ## ----robinson, fig.alt="A world map using the Robinson projection."----------- # crs_robin <- "+proj=robin +datum=WGS84" # # ggplot() + # geom_world(crs = crs_robin) + # coord_sf(crs = crs_robin) + # theme_void() ## ----robin_pacific, fig.alt="A Robinson projection world map centered on the Pacific Ocean (150 degrees East)."---- # crs_robin_150 <- "+proj=robin +lon_0=150 +datum=WGS84" # # ggplot() + # geom_world(crs = crs_robin_150) + # coord_sf(crs = crs_robin_150) + # theme_void() ## ----wgs84_pacific, fig.alt="A rectangular projection world map centered on 150 degrees East."---- # crs_wgs84_150 <- "+proj=longlat +datum=WGS84 +lon_0=150" # # ggplot() + # geom_world(crs = crs_wgs84_150) + # coord_sf(crs = crs_wgs84_150) + # theme_void() ## ----axis_labels, fig.alt="A world map with clear longitude and latitude axis labels and gridlines drawn on top of the land layer."---- # ggplot() + # geom_world() + # coord_sf( # crs = 4326, # expand = FALSE, # datum = sf::st_crs(4326) # ) + # theme_minimal() + # theme(panel.ontop = TRUE) ## ----graticule_global, fig.alt="A world map with custom graticule lines labeled every 60 degrees longitude and 30 degrees latitude."---- # ggplot() + # geom_world() + # annotation_graticule( # lon_step = 60, # lat_step = 30, # label_offset = 5 # ) + # coord_sf( # crs = 4326, # expand = FALSE, # datum = sf::st_crs(4326) # ) + # theme_void() + # theme(panel.ontop = TRUE) ## ----graticule_robin, fig.alt="A Robinson projection map with curved graticule lines."---- # crs_robin <- "+proj=robin +datum=WGS84" # # ggplot() + # geom_world(crs = crs_robin) + # annotation_graticule( # crs = crs_robin, # lon_step = 30, # lat_step = 15, # label_offset = 3e5 # ) + # coord_sf(crs = crs_robin) + # theme_void() ## ----region_cn, fig.alt="A regional map of China and surrounding areas with clean axis labels and specific graticule limits."---- # cn_xlim <- c(70, 140) # cn_ylim <- c(0, 60) # # ggplot() + # geom_world() + # annotation_graticule( # xlim = cn_xlim, # ylim = cn_ylim, # crs = 4326, # lon_step = 10, # lat_step = 10, # label_color = NA, # label_offset = 1, # label_size = 3.5 # ) + # coord_sf( # xlim = cn_xlim, # ylim = cn_ylim, # expand = FALSE # ) + # labs( # x = "Longitude", # y = "Latitude" # ) + # theme_bw() ## ----highlight_cn, fig.alt="A world map with China highlighted in red."------- # ggplot() + # geom_world( # country_fill = "white", # show_frame = TRUE # ) + # geom_world( # filter_attribute = "SOC", # filter = "CHN", # country_fill = "red" # ) + # theme_void() ## ----highlight_multi, fig.alt="A world map highlighting China, Japan, and South Korea in orange."---- # focus <- c("CHN", "JPN", "KOR") # # ggplot() + # geom_world( # country_fill = "grey95", # show_frame = TRUE # ) + # geom_world( # filter_attribute = "SOC", # filter = focus, # country_fill = "#f57f17" # ) + # theme_void() ## ----custom_data_gdp, fig.alt="A world map with countries colored by GDP using a continuous color scale."---- # # 1. Ensure data availability and GET FILE PATHS # map_files <- check_geodata(c("world_countries.rda", "world_coastlines.rda")) # # # 2. Load the world countries data (object name: 'countries') # load(map_files[1]) # # # 3. Create custom data: Real 2023 Population Estimates (Top 25+ major nations) # # Unit: Millions # custom_data <- data.frame( # iso_code = c("CHN", "IND", "USA", "IDN", "PAK", "NGA", "BRA", "BGD", # "RUS", "MEX", "JPN", "ETH", "PHL", "EGY", "VNM", "COD", # "TUR", "IRN", "DEU", "THA", "GBR", "FRA", "ITA", "ZAF", # "KOR", "ESP", "COL", "CAN", "AUS", "SAU"), # pop_mil = c(1425.7, 1428.6, 339.9, 277.5, 240.5, 223.8, 216.4, 172.9, # 144.4, 128.5, 123.3, 126.5, 117.3, 112.7, 98.9, 102.3, # 85.8, 89.2, 83.2, 71.8, 67.7, 64.7, 58.9, 60.4, # 51.7, 47.5, 52.1, 38.8, 26.6, 36.9) # ) # # # 4. Merge custom data with the 'countries' object # # Note: Use 'all.x = TRUE' to preserve the map geometry for all countries # merged_data <- merge( # countries, # custom_data, # by.x = "SOC", # by.y = "iso_code", # all.x = TRUE # ) # # # 5. Plot with layering strategy # ggplot() + # # Layer 1: Data Fill (No borders, just color) # geom_sf( # data = merged_data, # aes(fill = pop_mil), # color = "transparent" # ) + # # Layer 2: World Boundaries (Transparent fill, standard borders) # geom_world( # country_fill = NA, # show_ocean = FALSE # ) + # # Styling # scale_fill_viridis_c( # option = "plasma", # na.value = "grey95", # direction = -1, # Reverse color scale so dark = high population # name = "Population (Millions)" # ) + # theme_void() + # theme(legend.position = "bottom")