library(maps)
library(ggplot2)
library(RColorBrewer)
color1<-brewer.pal(9,"YlOrRd")[c(3,4,5,6,7,8,9)]
color2<-brewer.pal(9,"Greens")[c(4,6)]
color(<-crev(color2),color1)
p<-file.choose()
mydata<-read.csv(p,stringsAsFactors=FALSE) #这里读取的就是我们上面的population.csv
names(mydata)[1]<-c("Country" ,"Scale" ,"million","fan" ) #重新定义列明,并在原基础上新加了million和fan列
mydata$million<-mydata$Scale/1000000
mydata$fan<-cut(mydata$million,
breaks=c(min(mydata$million,na.rm=TRUE),
0,300,600,900,1200,1500,1800,2100,2400,
max(mydata$million,na.rm=TRUE)),
labels=c(" <=0","0~300","300~600","600~900","900~1200","1200~1500",
"1500~1800","1800~2100","2100~2400"," >=2400"),
order=TRUE)
world_map <- map_data("world") #读取R maps自带的包文件world,这样我们就可以直接绘制地图了
-mercator--------------------------------------
ggplot()+
geom_map(data=mydata,aes(map_id=Country,fill=fan),map=world_map)+
geom_path(data=world_map,aes(x=long,y=lat,group=group),colour="black",size=.2)+
coord_map("mercator",xlim=c(-180,180), ylim=c(-90, 90))+
scale_y_continuous(breaks=(-3:3)*30) +
scale_x_continuous(breaks=(-6:6)*30) +
scale_fill_manual(name="million dollars",values=color,na.value="grey75")+
guides(fill=guide_legend(reverse=TRUE)) +
theme_minimal()+
theme(
text=element_text(size=15)#
)
ggplot()+
geom_map(data=mydata,aes(map_id=Country,fill=fan),map=world_map)+
geom_path(data=world_map,aes(x=long,y=lat,group=group),colour="black",size=.2)+
coord_map("albers", parameters = c(0, 0))+
scale_y_continuous(breaks=(-3:3)*30) +
scale_fill_manual(name="million($)",values=color,na.value="grey80")+
guides(fill=guide_legend(reverse=TRUE))+
theme_minimal()+
theme(axis.text.x=element_blank())
原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。
原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。