R语言ggplot2绘制规范的中国地图（省市县三级）

1 如何获取规范的地图矢量数据获取

1.1 AntV L7

可以在该网站下载json格式的地图数据，包括省市县三级的数据以及国界线、海南线等等。数据是符合民政部规范。

1.2 省市县数据CTAmap

2 R语言省市县三级地图可视化

2.1 省级地图可视化

library(tidyverse)
library(sf)
library(ggspatial)
library(RColorBrewer)
library(sjmisc)


##中国行政区划编码
##数据在公众号玩蛇皮SnakeCharmer回复“教程数据”获取
prov = readxl::read_xlsx("./data/china_map/中国行政区划-省.xlsx") 
prov

##---------------------------------------------------------------
##1.省级地图
#数据准备
#业务数据
##数据在公众号玩蛇皮SnakeCharmer回复“教程数据”获取
data = read_csv("./data/2022分省粮食产量_万吨.csv", locale = locale(encoding = 'gbk'))

##地图区矢量数据
china_prov =  read_sf("./data/china_map/2023/china_prov.geojson") %>% 
  left_join(data, join_by(name==region)) %>% 
  mutate(prod_lev = cut(product,
                        breaks = c(0, 100, 1000, 2000, 4000, 6000, 8000))) %>% 
  left_join(prov, by="adcode")

china_line = read_sf("./data/china_map/2023/china_line.geojson")

#作图
pal = c("#50b98f", "#92ce6e", "#edda0e", "#e5b107", "#d25c02", "#c01f33")
ggplot() +
  geom_sf(data=china_prov,
          aes(fill=prod_lev),
          color = "#011910", linewidth=0.15) +
  geom_sf(data=china_line,
          color = "#011910", linewidth=0.5)+
  geom_sf_text(data = china_prov,
               aes(label = str_c("\n", short.name)),
               size=2) +
  geom_sf_text(data = china_prov,
               aes(label = round(product)),
               size=3, nudge_y = 50000) +
  coord_sf(crs = "+proj=laea +lat_0=40 +lon_0=104") +
  
  scale_fill_manual(values = pal, 
                    guide = guide_bins(label.position = 'bottom',
                                       axis = FALSE,
                                       show.limits = TRUE,
                                       keywidth = unit(2, 'lines'),
                                       keyheight = unit(0.75, 'lines'),
                                       nrow = 1),
                    na.value = 'grey95',
                    name = '') +
  
  annotation_scale(location = "bl") + #添加比例尺
  annotation_north_arrow(location = "tl", which_north = "false",
                         style = north_arrow_fancy_orienteering) + #添加指北针

  theme_minimal() +
  theme(legend.position = c(0.25, 0.1),
        legend.direction = 'horizontal',
        axis.title = element_blank(),
        plot.title = element_text(size = 22),
        plot.background = element_rect(fill = "white", color = "white"))

2.2 市级地图可视化

##2.市级地图
##数据在公众号玩蛇皮SnakeCharmer回复“教程数据”获取
data = readxl::read_xls("./data/七普人口基本状况.xls") 
data %>% 
  set_names(nm = names(data) %>% str_remove_all(" |\n")) %>% 
  select(地区, 总人口)-> df


china_city = read_sf("./data/china_map/2020/2020年地级/2020年地级.shp") %>% 
  left_join(df, join_by(地名==地区)) %>% 
  mutate(总人口=as.double(总人口)/10000,
         class = cut(总人口,
                     breaks = c(0, 50, 100, 200, 300, 400, 500, 600, 1000, 1500, 2000, 3206)))

ggplot() +
  geom_sf(data = china_city, 
          aes(fill = class),
          linewidth=0.075, color = "#241E3E") +
  geom_sf(data = china_line, fill=NA, linewidth=0.5, color="#241E3E") +
  coord_sf(crs = "+proj=laea +lat_0=40 +lon_0=104") +
  scale_fill_manual(values = rev(brewer.pal(11, "Spectral")),
                    name = "",
                    na.value = "grey95") +
  annotation_scale(location = "bl") + #添加比例尺
  annotation_north_arrow(location = "tl", which_north = "false",
                         style = north_arrow_fancy_orienteering) + #添加指北针
  theme_minimal() +
  theme(plot.background = element_rect(fill = "white", color = NA),
        legend.position = "bottom",
        legend.key.height = unit(2, "mm"))

2.3县级地图可视化

##3.县级地图
##业务数据处理
rec = "繁昌县=繁昌区;海门市=海门区;监利县=监利市;水城县=水城区;同仁县=同仁市;芜湖县=湾沚区;抚远市=抚远县;else=copy"
data %>% 
  set_names(nm = names(data) %>% str_remove_all(" |\n")) %>% 
  filter(!(str_starts(地区, "表")), 
         !is.na(地区), 
         !(地区%in%c("地 区", "市辖区"))) %>%
  mutate(city = case_when(str_starts(地区, "  ") ~ NA,
                          地区 %in% c("省直辖县级行政区划", "自治区直辖县级行政区划", "县") ~ "prov",
                          .default = 地区)) %>% 
  fill(city, .direction="down") %>% 
  left_join(prov %>% select(name), join_by(city==name), keep = TRUE) %>% 
  fill(name, .direction = "down") %>% 
  filter(str_starts(地区, "  ")) %>% 
  left_join(prov, by="name") %>% 
  mutate(地区 = str_remove_all(地区, " "),
         地区 = case_when(地区=="市辖区" ~ city,
                        .default = 地区),
         地区 = rec(地区, rec=rec),
         city = if_else(city=="prov", name, city),
         总人口 = round(as.double(总人口)/10000),
         class = cut(总人口,
                     breaks = c(0, 1, 10, 20, 30, 40, 50, 75, 100, 300, 600, 1047),
                     include.lowest = TRUE)) %>% 
  rename(prov.name = name)-> df


##县级地图数据处理
china_district = read_sf("./data/china_map/2020/2020年县级/2020年县级.shp") %>% 
  mutate(city = if_else(地级类=="不统计", 省级, 地级)) %>% 
  left_join(df, join_by(地名==地区, city==city), keep=TRUE) 

##作图
ggplot() +
  geom_sf(data = china_district, 
          aes(fill = class),
          linewidth=0.075, color = "#241E3E") +
  geom_sf(data = china_line, fill=NA, linewidth=0.5, color="#241E3E") +
  coord_sf(crs = "+proj=laea +lat_0=40 +lon_0=104") +
  scale_fill_manual(values = rev(brewer.pal(11, "Spectral")),
                    name = "",
                    na.value = "grey95") +
  annotation_scale(location = "bl") + #添加比例尺
  annotation_north_arrow(location = "tl", which_north = "false",
                         style = north_arrow_fancy_orienteering) + #添加指北针
  theme_minimal() +
  theme(plot.background = element_rect(fill = "white", color = NA),
        legend.position = "bottom",
        legend.key.height = unit(2, "mm"))