使用geom_polygon

mymap <- rgdal::readOGR("/Users/appe/Nutstore Files/01-dir_for_work/3-source/4.map/China/Province/省级行政区.shp")
plot(mymap)
map_pal <- paletteer_c("grDevices::Terrain 2", 40)[1:34]
library(ggplot2)
set_peng_theme()
mymap2 <- ggplot(data = fortify(mymap)) +
  geom_polygon(aes(x = long, y = lat, group = id), colour = "black",
               fill = NA)
mymap2 + theme(axis.text = element_blank(), 
               axis.ticks = element_blank() ) + labs(x = "", y = "", title = "Template fot my ggplot", tag = "Fig. 1", 
                                                     caption = "By: Mug Peng")

但发现这个数据并不是很懂，很不方便后期进行处理。

> head(mymap_df)
     long     lat order  hole piece id group
1 1329152 5619034     1 FALSE     1  0   0.1
2 1323328 5628245     2 FALSE     1  0   0.1
3 1317709 5635382     3 FALSE     1  0   0.1
4 1314315 5641343     4 FALSE     1  0   0.1
5 1312338 5643597     5 FALSE     1  0   0.1
6 1311923 5644256     6 FALSE     1  0   0.1

这时候主要需要获取一下id 对应的省份名称：

> df_map <- mymap@data[,c('PINYIN_NAM', 'NAME')]
> df_map$id <- as.character(0:33)
> head(df_map)
    PINYIN_NAM   NAME id
0 Heilongjiang 黑龙江  0
1 Xinjiang Wei   新疆  1
2       Shanxi   山西  2
3  Ningxia Hui   宁夏  3
4       Xizang   西藏  4
5     Shandong   山东  5

接下来就可以按照省份快乐画图了：

df_map <- mymap@data[,c('PINYIN_NAM', 'NAME')]
df_map$id <- as.character(0:33)
head(df_map)
mymap_df <- fortify(mymap)
head(mymap_df)
df_map$PINYIN_NAM[33] <- "Macau"
mymap_df_all <- merge(df_map, mymap_df, by = "id")
ggplot(data = mymap_df_all, mapping = aes(x = long, y = lat)) + 
  geom_polygon(colour = 'black', aes(fill = PINYIN_NAM)) + 
  scale_fill_manual(values = map_pal) + theme(axis.text = element_blank(), 
                                              axis.ticks = element_blank(), 
                                              legend.title = element_blank()) + 
  labs(x = "", y = "", title = "Template fot my ggplot", tag = "Fig. 1", caption = "By: Mug Peng")

我们还可以获得每个省份的中间位置坐标，获得一个标记文件，比如各个省份的文盲率信息：

p1 + geom_point(mapping = aes(x = longitude, y = latitude, size = Illiterate_rate), data = info_df, colour = 'red')