mapchina

An R package storing the geospatial shapefile (vector data) of China administrative divisions to the county/district-level.

中华人民共和国区县级行政区划矢量地图数据

Installation

To install CRAN version 安装CRAN正式版本:

install.packages("mapchina")

To install the most updated version of the 'dev' branch 安装开发中的最新版本:

if (!require(devtools)) {
  install.packages("devtools")
}

devtools::install_github("xmc811/mapchina", ref = "dev")

Examples 使用示例

1. Browsing the dataframe of the shapefile

查看矢量地图数据

library(mapchina)
head(china)

# Output 输出
Simple feature collection with 6 features and 13 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: 115.4248 ymin: 39.44473 xmax: 116.8805 ymax: 41.05936
geographic CRS: WGS 84
# A tibble: 6 x 14
  Code_County Code_Perfecture Code_Province Name_Province Name_Perfecture Name_County Pinyin Pop_2000 Pop_2010 Pop_2017 Pop_2018   Area Density
  <chr>       <chr>           <chr>         <chr>         <chr>           <chr>       <chr>     <dbl>    <dbl>    <dbl>    <dbl>  <dbl>   <dbl>
1 110101      1101            11            北京市        NA              东城区      Dōngc…   881763   919253       NA   822000   41.8  19670.
2 110102      1101            11            北京市        NA              西城区      Xīché…  1232823  1243315       NA  1179000   50.5  23360.
3 110114      1101            11            北京市        NA              昌平区      Chāng…   614821  1660501       NA  2108000 1342     1571.
4 110115      1101            11            北京市        NA              大兴区      Dàxīn…   671444  1365112       NA  1796000 1053     1706.
5 110111      1101            11            北京市        NA              房山区      Fángs…   814367   944832       NA  1188000 1995      595.
6 110116      1101            11            北京市        NA              怀柔区      Huáir…   296002   372887       NA   414000 2123      195.
# … with 1 more variable: geometry <MULTIPOLYGON [°]>

The main data object china is a dataframe, with each row as one county/district level administrative division of China. To plot the map of a particular region, you can filter() codes or names to subset the dataframe. The codes are stored in variables Code_Province, Code_Perfecture, and Code_County, and the names are stored in Name_Province, Name_Perfecture, and Name_County. The codes and names follow the 3-level hierarchy: Province (2-digit), Perfecture (4-digit), and County (6-digit).

在矢量数据china中，行政区划的中文名与代码均分为三级：省级、地级与县级，可使用filter()进行任意筛选。

注意：直辖市、特别行政区、**地区、省直管市以及其他特殊行政区域的地级区域名称Name_Perfecture为NA。用户可根据作图需要进行修改。

2. Export the data to shapefile

将数据导出为矢量文件

library(sf)
st_write(china, "your/path/to/china.shp", layer_options = "ENCODING=UTF-8")

3. Plotting the population density rank of Beijing, Tianjin, and Hebei

京津冀县级人口密度排名作图

Since the shapefile data is also a dataframe, it can be plotted by ggplot grammer of graphics. The geometric object is geom_sf().

library(tidyverse)
library(sf)

df <- china %>%
        filter(Code_Province %in% c("11","12","13"))

ggplot(data = df) +
        geom_sf(aes(fill = rank(Density))) +
        scale_fill_distiller(palette = "BuPu", direction = 1) +
        theme_bw() +
        theme(legend.position = "none")

4. Plotting the map with customized data

使用新加入的数据作图

New data can be added to the shapefile dataframe as new variables

df$Var <- runif(nrow(df))

ggplot(data = df) +
        geom_sf(aes(fill = Var)) +
        scale_fill_distiller(palette = "YlOrRd") +
        theme_bw() +
        theme(legend.position = "none")

5. Plotting the map with random color, but no two adjacent regions have the same color.

随机颜色作图并使得相邻区域颜色不一样

We use greedy coloring algorithm to solve the problem. The function generate_map_colors() takes a shapefile dataframe as input and outputs a list of index for filling colors.

df2 <- china %>%
        filter(Code_Province %in% c("32"))

ggplot(data = df2) +
        geom_sf(aes(fill = factor(generate_map_colors(df2)))) +
        scale_fill_brewer(palette = "Set3") +
        theme_bw() +
        theme(legend.position = "none")

6. Plotting the administrative divisions at higher levels (province or perfecture level)

对省地级行政区作图

The geometry of county-level divisions can be merged to higher level divisions by functions group_by(), summarise(), and sf::st_union().

县级行政区可被合并为地级或省级行政区进行作图。

df3 <- china %>%
        filter(Code_Province %in% as.character(31:36))

df3 <- df3 %>%
        group_by(Name_Province) %>%
        summarise(geometry = st_union(geometry))

ggplot(data = df3) +
        geom_sf(aes(fill = Name_Province)) +
        scale_fill_brewer(palette = "Set3") +
        theme_bw() +
        theme(legend.position = "none")

7. Adding Chinese characters to the map

在地图中加入汉字标记

To add Chinese characters for the map, R package showtext is required.

if (!require("showtext")) {
install.packages("showtext")

showtext::showtext_auto()

ggplot(data = df3) +
        geom_sf(aes(fill = Name_Province)) +
        scale_fill_brewer(palette = "Set3") +
        theme_bw() +
        theme(legend.position = "none") +
        geom_sf_label(aes(label = Name_Province))

8. A comprehensive example

综合示例：多省地图，按地级行政区划随机着色并加汉字标记，各级区划分界线不同

showtext::showtext_auto()

df4 <- china %>%
        filter(Code_Province %in% c("32","34"))

df4_prov <- df4 %>%
        group_by(Name_Province) %>%
        summarise(geometry = st_union(geometry))

df4_perf <- df4 %>%
        group_by(Name_Perfecture) %>%
        summarise(geometry = st_union(geometry))

ggplot() +
        geom_sf(data = df4_perf,
                aes(fill = factor(generate_map_colors(df4_perf))),
                linetype = "solid", size = 0.5) +
        scale_fill_brewer(palette = "Pastel1") +
        geom_sf(data = df4, alpha = 0, linetype = "dashed", size = 0.2) +
        geom_sf(data = df4_prov, alpha = 0, linetype = "solid", size = 1.2) +
        geom_sf_label(data = df4_perf, aes(label = Name_Perfecture)) +
        theme_bw() +
        theme(legend.position = "none")

NotebookOFXiaoMing/mapchina