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：

    01. 制作简单的中国地图 - 图1

    直接读取shp 文件即可：

    1. my_packages <- c("maptools")
    2. boost_install_packages(my_packages = my_packages, jobs = T, loaded = T)
    4. mymap <- rgdal::readOGR("/Users/appe/Nutstore Files/01-dir_for_work/3-source/4.map/China/Province/省级行政区.shp")
    5. plot(mymap)

    01. 制作简单的中国地图 - 图2

    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：

    1. plot(mymap, col = paletteer_c("grDevices::Terrain 2", 40)[1:34], main = "Chinese map")

    01. 制作简单的中国地图 - 图3

    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：

    1. > colnames(mymap@data)
    2.   [1] "Z120401"    "Z121301"    "Z120402"    "Z120602"    "Z121102"    "Z121302"    "Z120403"    "Z120603"   
    3.   [9] "Z120703"    "Z120803"    "Z121103"    "Z121303"    "Z120404"    "Z120604"    "Z120804"    "Z121104"   
    4.  [17] "Z121304"    "Z120405"    "Z120605"    "Z120705"    "Z120805"    "Z121105"    "Z121305"    "Z120406"   
    5.  [25] "Z120806"    "Z121106"    "Z121306"    "Z120307"    "Z120607"    "Z121107"    "Z120308"    "Z120608"   
    6.  [33] "Z121108"    "Z120609"    "Z120709"    "Z121109"    "Z120610"    "Z120711"    "Agro_Colle" "Annual_Gro"
    7.  [41] "AREA"       "Art_Instit" "Com_Univer" "Eco_Fin_Co" "Female_to_" "Forestry_C" "GDP_1994."  "GDP_1997." 
    8.  [49] "GDP_1998."  "GDP_1999."  "GDP_2000."  "Growth_in_" "Growth_Rat" "Han_to_Tot" "HighSchool" "HighScho_1"
    9.  [57] "id"         "Illiterate" "Illitera_1" "Illitera_2" "Illitera_3" "Illitera_4" "Index_1997" "Index_1998"
    10.  [65] "Index_1999" "Index_2000" "Index_Pri_" "Index_Sec_" "Index_Teri" "Language_C" "Male_to_To" "Medicine_C"
    11.  [73] "Nationalit" "Normal_Col" "PERIMETER"  "Physical_C" "PINYIN_NAM" "Polity_Law" "Pop_0_14."  "Pop_15_64" 
    12.  [81] "Pop_1990."  "Pop_65Plus" "Pop_Female" "Pop_Han.1"  "Pop_Male."  "Pop_Minori" "Pop_Mino_1" "Pop_Rural" 
    13.  [89] "Pop_Urban"  "POPU"       "POPU_94"    "POPU_PER_L" "POPU_PER_1" "Pri_Indust" "PRODUCT"    "PRODUCT_1" 
    14.  [97] "PRODUCT_1_" "PRODUCT_2"  "PRODUCT_2_" "Product_Bu" "Product_Fi" "Product_Ge" "Product_He" "Product_In"
    15. [105] "Product_Ot" "Product_re" "Product_Sc" "Product_se" "Product_So" "PROVINCE_"  "PROVINCE_I" "Rural_to_T"
    16. [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。