首先需要明确一点,一定要使用真正的中国地图!!
可以直接获取:http://gaohr.win/site/blogs/2017/2017-04-18-GIS-basic-data-of-China.html
下载好的GIS 数据即可以用于绘图。
这里我选择省级行政区划:
直接读取shp 文件即可:
my_packages <- c("maptools")
boost_install_packages(my_packages = my_packages, jobs = T, loaded = T)
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)
我们还可以添加一些其他元素,这里我选择的是Terrain 2 调色板:
plot(mymap, col = paletteer_c("grDevices::Terrain 2", 40)[1:34], main = "Chinese map")
除此之外,发现这个地图数据存储的内容远远不止地图数据,还包括其他许许多多的信息:
> colnames(mymap@data)
[1] "Z120401" "Z121301" "Z120402" "Z120602" "Z121102" "Z121302" "Z120403" "Z120603"
[9] "Z120703" "Z120803" "Z121103" "Z121303" "Z120404" "Z120604" "Z120804" "Z121104"
[17] "Z121304" "Z120405" "Z120605" "Z120705" "Z120805" "Z121105" "Z121305" "Z120406"
[25] "Z120806" "Z121106" "Z121306" "Z120307" "Z120607" "Z121107" "Z120308" "Z120608"
[33] "Z121108" "Z120609" "Z120709" "Z121109" "Z120610" "Z120711" "Agro_Colle" "Annual_Gro"
[41] "AREA" "Art_Instit" "Com_Univer" "Eco_Fin_Co" "Female_to_" "Forestry_C" "GDP_1994." "GDP_1997."
[49] "GDP_1998." "GDP_1999." "GDP_2000." "Growth_in_" "Growth_Rat" "Han_to_Tot" "HighSchool" "HighScho_1"
[57] "id" "Illiterate" "Illitera_1" "Illitera_2" "Illitera_3" "Illitera_4" "Index_1997" "Index_1998"
[65] "Index_1999" "Index_2000" "Index_Pri_" "Index_Sec_" "Index_Teri" "Language_C" "Male_to_To" "Medicine_C"
[73] "Nationalit" "Normal_Col" "PERIMETER" "Physical_C" "PINYIN_NAM" "Polity_Law" "Pop_0_14." "Pop_15_64"
[81] "Pop_1990." "Pop_65Plus" "Pop_Female" "Pop_Han.1" "Pop_Male." "Pop_Minori" "Pop_Mino_1" "Pop_Rural"
[89] "Pop_Urban" "POPU" "POPU_94" "POPU_PER_L" "POPU_PER_1" "Pri_Indust" "PRODUCT" "PRODUCT_1"
[97] "PRODUCT_1_" "PRODUCT_2" "PRODUCT_2_" "Product_Bu" "Product_Fi" "Product_Ge" "Product_He" "Product_In"
[105] "Product_Ot" "Product_re" "Product_Sc" "Product_se" "Product_So" "PROVINCE_" "PROVINCE_I" "Rural_to_T"
[113] "Sci_Eng_Co" "Sec_Indust" "Teri_Indus" "Total_Coll" "Urban_to_T" "Vocation" "DZM" "NAME"
甚至还有文盲率、GDP 等等等等。