用R绘制世界地图及中国地图
在如今这个数据可视化无处不在的世界里，R语言不再只是统计分析的工具，它更像是一支画笔，能为你描绘出一幅幅地图。如果你曾经幻想过用代码勾勒出世界的轮廓，或者展现中国的山川河流，那么今天的探索正适合你~
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.2.3library(dplyr)## Warning: package 'dplyr' was built under R version 4.2.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionrequire(maps)## Loading required package: maps## Warning: package 'maps' was built under R version 4.2.3require(viridis)## Loading required package: viridis## Warning: package 'viridis' was built under R version 4.2.3## Loading required package: viridisLite## Warning: package 'viridisLite' was built under R version 4.2.3##
## Attaching package: 'viridis'## The following object is masked from 'package:maps':
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## unemp1.世界地图绘制
绘制简单地图
world <- map_data("world")
worldplot <- ggplot() +geom_polygon(data = world, aes(x=long, y = lat, group = group),fill="lightgray",color="white") + coord_fixed(1.3)
worldplot
根据指标填充颜色，如想绘制全球妊娠期糖尿病发病情况的分布比较图，可下载IDF文件。
加载数据
world_gdm <- read.csv("IDF_2021.csv", header = TRUE) #下载文件到本地文件夹head(world_gdm)## X. Region Country.Territory X2000 X2011
## 1 1 Africa Africa N/A N/A
## 2 2 Europe Europe N/A N/A
## 3 3 Middle East and North Africa Middle East and North Africa N/A N/A
## 4 4 North America and Caribbean North America and Caribbean N/A N/A
## 5 5 South and Central America South and Central America N/A N/A
## 6 6 South-East Asia South-East Asia N/A N/A
## X2021 X2030 X2045 Type Country
## 1 13 N/A N/A Region Africa
## 2 15 N/A N/A Region Europe
## 3 14.1 N/A N/A Region Middle East and North Africa
## 4 20.7 N/A N/A Region North America and Caribbean
## 5 15.8 N/A N/A Region South and Central America
## 6 25.9 N/A N/A Region South-East Asia##查看两个数据集中变量名称不一致的部分
diff <- setdiff(world$region, world_gdm$Country)
diff## [1] "Antarctica" "French Southern and Antarctic Lands"
## [3] "Barbuda" "Saint Barthelemy"
## [5] "Brunei" "Ivory Coast"
## [7] "Cape Verde" "Czech Republic"
## [9] "Canary Islands" "Falkland Islands"
## [11] "Guernsey" "Heard Island"
## [13] "Cocos Islands" "Christmas Island"
## [15] "Chagos Archipelago" "Jersey"
## [17] "Siachen Glacier" "Nevis"
## [19] "Kosovo" "Saint Martin"
## [21] "Montserrat" "Norfolk Island"
## [23] "Bonaire" "Sint Eustatius"
## [25] "Saba" "Pitcairn Islands"
## [27] "Madeira Islands" "Azores"
## [29] "Western Sahara" "South Sandwich Islands"
## [31] "South Georgia" "Saint Helena"
## [33] "Ascension Island" "Saint Pierre and Miquelon"
## [35] "Swaziland" "Sint Maarten"
## [37] "Turks and Caicos Islands" "Tobago"
## [39] "Vatican" "Grenadines"
## [41] "Wallis and Futuna"通常需要对不一致的变量进行重命名，以使得world map数据和GDM发病数据中的国家名称一致，这里仅用作示例，不做修改。
## 将数据设置为数值型变量
world_gdm$X2021 <- as.numeric(as.character(world_gdm$X2021))## Warning: NAs introduced by coercionworld_gdm$prevalence<-world_gdm$X2021
world_gdm$prevalence<-gsub("-","",world_gdm$prevalence)合并数据集
worldSubset <- inner_join(world, world_gdm, by = c("region"="Country"))
head(worldSubset)## long lat group order region subregion X.
## 1 -69.89912 12.45200 1 1 Aruba <NA> 18
## 2 -69.89571 12.42300 1 2 Aruba <NA> 18
## 3 -69.94219 12.43853 1 3 Aruba <NA> 18
## 4 -70.00415 12.50049 1 4 Aruba <NA> 18
## 5 -70.06612 12.54697 1 5 Aruba <NA> 18
## 6 -70.05088 12.59707 1 6 Aruba <NA> 18
## Region Country.Territory X2000 X2011 X2021 X2030 X2045
## 1 North America and Caribbean Aruba - - NA - -
## 2 North America and Caribbean Aruba - - NA - -
## 3 North America and Caribbean Aruba - - NA - -
## 4 North America and Caribbean Aruba - - NA - -
## 5 North America and Caribbean Aruba - - NA - -
## 6 North America and Caribbean Aruba - - NA - -
## Type prevalence
## 1 Country <NA>
## 2 Country <NA>
## 3 Country <NA>
## 4 Country <NA>
## 5 Country <NA>
## 6 Country <NA>绘制最终数据
## 设置绘图主题
plain <- theme(axis.text = element_blank(),axis.line = element_blank(),axis.ticks = element_blank(),panel.border = element_blank(),panel.grid = element_blank(),axis.title = element_blank(),panel.background = element_rect(fill = "white"),plot.title = element_text(hjust = 0.5)
)worldSubset$prevalence<-as.numeric(worldSubset$prevalence)#绘图
worldHDI <- ggplot(data = worldSubset, mapping = aes(x = long, y = lat, group = group)) + coord_fixed(1.3) +geom_polygon(aes(fill = prevalence)) +# scale_fill_distiller(palette ="RdBu", direction = -1) + # or direction=1scale_fill_gradient2(low = "#ADC6AD", mid = "#e7b800", high = "red", midpoint = 10)+ggtitle("Prevalence of Gestational Diabetes in 2021 by WHO region") +plainworldHDI
2.绘制特定区域的地图
获取特定国家的地图数据
# Some EU Contries
some.eu.countries <- c("Portugal", "Spain", "France", "Switzerland", "Germany","Austria", "Belgium", "UK", "Netherlands","Denmark", "Poland", "Italy", "Croatia", "Slovenia", "Hungary", "Slovakia","Czech republic"
)
# Retrievethe map data
some.eu.maps <- map_data("world", region = some.eu.countries)
# Compute the centroid as the mean longitude and lattitude
# Used as label coordinate for country's names
region.lab.data <- some.eu.maps %>%dplyr::group_by(region) %>%dplyr::summarise(long = mean(long), lat = mean(lat))region.lab.data## # A tibble: 17 × 3
## region long lat
## <chr> <dbl> <dbl>
## 1 Austria 13.5 47.6
## 2 Belgium 4.73 50.6
## 3 Croatia 16.3 44.6
## 4 Czech Republic 15.4 49.9
## 5 Denmark 10.7 55.7
## 6 France 3.23 46.2
## 7 Germany 10.4 51.2
## 8 Hungary 19.4 47.2
## 9 Italy 11.8 42.2
## 10 Netherlands 5.32 52.1
## 11 Poland 19.1 51.4
## 12 Portugal -7.89 39.9
## 13 Slovakia 19.6 48.8
## 14 Slovenia 14.8 46.1
## 15 Spain -2.91 40.7
## 16 Switzerland 8.31 46.7
## 17 UK -4.10 55.6可视化
ggplot(some.eu.maps, aes(x = long, y = lat)) +geom_polygon(aes( group = group, fill = region))+geom_text(aes(label = region), data = region.lab.data, size = 3, hjust = 0.5)+scale_fill_viridis_d()+theme_void()+theme(legend.position = "none")
3.中国地图绘制
下载中国地图json文件
DataV.GeoAtlas地理小工具系列
读取地图文件，绘制基础图形
library(sf)## Warning: package 'sf' was built under R version 4.2.3## Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUElibrary(ggplot2)
library(cowplot)## Warning: package 'cowplot' was built under R version 4.2.3library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.2.3## Warning: package 'tibble' was built under R version 4.2.3## Warning: package 'tidyr' was built under R version 4.2.3## Warning: package 'readr' was built under R version 4.2.3## Warning: package 'purrr' was built under R version 4.2.3## Warning: package 'stringr' was built under R version 4.2.3## Warning: package 'forcats' was built under R version 4.2.3## Warning: package 'lubridate' was built under R version 4.2.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
## ✔ readr 2.1.5## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::map() masks maps::map()
## ✖ lubridate::stamp() masks cowplot::stamp()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggspatial)## Warning: package 'ggspatial' was built under R version 4.2.3library(dplyr)
China_map=read_sf("China.json")
# 地图散点图2
p2=ggplot(China_map)+geom_sf(color='white',fill="lightgray",size=0.8)+#地图线条粗细annotation_scale(location = "bl", width_hint = 0.3) +#添加比例尺并调整位置及长度annotation_north_arrow(location = "tl", which_north = F, pad_x = unit(0.05, "in"), pad_y = unit(0.05, "in"),style = north_arrow_nautical)+#添加指北针，指北针类型style有north_arrow_orienteering；north_arrow_fancy_orienteering；north_arrow_minimal与north_arrow_nautical四种类型theme_map()
p2## Scale on map varies by more than 10%, scale bar may be inaccurate
提取各个省份经纬度坐标
city_name<-China_map$name
location_center<-China_map$centerdata_province<-matrix(nrow = 34,ncol = 2)
colnames(data_province)<-c("LON","LAT")
for (i in 1:34) {data_province[i,1]<-location_center[[i]][1]data_province[i,2]<-location_center[[i]][2]# rownames(data_province[i,])<-city_name[i]
}
rownames(data_province)<-city_name[1:34]
#write.csv(data_province,file = "Data/Map/data_province_center.csv")
head(data_province)## LON LAT
## 北京市 116.4053 39.90499
## 天津市 117.1902 39.12560
## 河北省 114.5025 38.04547
## 山西省 112.5492 37.85701
## 内蒙古自治区 111.6708 40.81831
## 辽宁省 123.4291 41.79677更简单点的，使用mapdata包中的map()函数，即可绘制中国地图。
获取经纬度坐标之后，就可以根据自己的数据填充感兴趣的数值了，具体方法同前，感兴趣的大家可以尝试一下。
希望可以通过今天的博文，大家可以稍微获得一点代码与地理的浪漫邂逅。
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