等高线图
contour(x=10*1:nrow(volcano), y=10*1:ncol(volcano), z=volcano,#内置数据集
xlab="Metres West",ylab="Metres North",
main="Topography of Maunga Whau Volcano")#对X,Y*10以后，z仍会自动对应。(试一下不*10的结果
优化等高线图
par(las=1)
plot(0,0,xlim=c(0,10*nrow(volcano)),ylim=c(0,10*ncol(volcano)),
type="n",xlab="Metres West",
ylab="Metres North",main="Topography of Maunga Whau Volcano")#不画图，只是一个框架
u<-par("usr")
rect(u[1],u[3],u[2],u[4],col="lightgreen")#颜色
contour(x=10*1:nrow(volcano),y=10*1:ncol(volcano),
volcano,col="red",add=TRUE)#add 加到现有的图上
等高线之间添加颜色
filled.contour(x = 10*1:nrow(volcano),y = 10*1:ncol(volcano),
z = volcano, color.palette = terrain.colors,#选择调色盘，会自动计算所需颜色的数目。也可以指定颜色数，
plot.title = title(main = "The Topography of Maunga Whau",
xlab = "Meters North",ylab = "Meters West"),
plot.axes = {axis(1, seq(100, 800, by = 100))
axis(2, seq(100, 600, by = 100))},
key.title = title(main="Height\n(meters)"),
key.axes = axis(4, seq(90, 190, by = 10)))#这一画图方法，会画出两幅图，主图（plot)和图例(key)，所以需要分别指定title和坐标.所以需要使用plot.title key.title plot.axes key.axes。 在这里，我们不能把axes=False 然后画完图以后再用axis（）添加坐标轴。因为这一的话坐标轴会超过主图，涵盖图例。
filled.contour(x = 10*1:nrow(volcano), y = 10*1:ncol(volcano), z = volcano,
color.palette = terrain.colors,
plot.title = title(main = "The Topography of Maunga Whau",
xlab = "Meters North",ylab = "Meters West"),nlevels=100,#确定颜色的数目。会比上图更平滑，默认是20.但这样图例会比较难看
plot.axes = {axis(1, seq(100, 800, by = 100))
axis(2, seq(100, 600, by = 100))},
key.title = title(main="Height\n(meters)"),
key.axes = axis(4, seq(90, 190, by = 10)))
三维地形图#更多内容参见帮助
install.packages("rgl")
library(rgl)
z <- 2 * volcano#3D效果更明显
x <- 10 * (1:nrow(z))
y <- 10 * (1:ncol(z))
zlim <- range(z)
zlen <- zlim[2] - zlim[1] + 1
colorlut <- terrain.colors(zlen)
col <- colorlut[ z-zlim[1]+1 ]#这里，每个z都有自己的颜色。
rgl.open()
rgl.surface(x, y, z, color=col, back="lines")#可见的区域下面是线。
时间序列可视化，在日历热力图
source("calendarHeat.R")
stock.data <- read.csv("google.csv")
install.packages("chron")
library("chron")
calendarHeat(dates=stock.data$Date,#date.form的默认格式是YYYY-MM-DD，和我们这个股票数据匹配。如果数据的日期格式不一样，也可以更改。
values=stock.data$Adj.Close,
varname="Google Adjusted Close")
install.packages("openair")#另一种方法，openair本来是用来分析空气污染的
library(openair)
calendarPlot(mydata)#最简单,mydata是内置数据集。
mydata$sales<-rnorm(length(mydata$nox),mean=1000,sd=1500)#向mydata中添加数据
calendarPlot(mydata,pollutant="sales",main="Daily Sales in 2003")#因为这个包本来是为了处理空气污染的，所以这边是把 pollutant改掉。
地图
install.packages("maps")#地图包
library(maps)
install.packages("WDI")#世界银行关于各个国家的数据（好像是个可以联网更新的API）
library(WDI)
install.packages("RColorBrewer")#调色盘
library(RColorBrewer)
colors = brewer.pal(7,"PuRd")
wgdp<-WDIsearch("gdp")#会得到一堆与GDP相关的参数
w<-WDI(country="all", indicator=wgdp[4,1], start=2005, end=2005)#选择相应需要的参数
w[63,1] <- "USA"
x<-map(plot=FALSE)
x$measure<-array(NA,dim=length(x$names))#添加一个列，命名为measure
for(i in 1:length(w$country)) {
for(j in 1:length(x$names)) {
if(grepl(w$country[i],x$names[j],ignore.case=T))#这边，查找x中的names中是否有w中的country（这边名字的比较并不精确，因为同一个国家在两个数据集的名字可能不同）。忽略大小写差异
x$measure[j]<-w[i,3]#如果有w中的country和x中的name对应，则赋值。
}
}
sd <- data.frame(col=colors,
values <- seq(min(x$measure[!is.na(x$measure)]),
max(x$measure[!is.na(x$measure)]) *1.0001,
length.out=7))
sc<-array("#FFFFFF",dim=length(x$names))
for (i in 1:length(x$measure))
if(!is.na(x$measure[i]))
sc[i]=as.character(sd$col[findInterval(x$measure[i],
sd$values)])
#2-column layout with color scale to the right of the map
layout(matrix(data=c(2,1), nrow=1, ncol=2), widths=c(8,1),#c(2,1)就是先画2，后画1.
heights=c(8,1))
# Color Scale first
breaks<-sd$values
par(mar = c(20,1,20,7),oma=c(0.2,0.2,0.2,0.2),mex=0.5)
image(x=1, y=0:length(breaks),z=t(matrix(breaks))*1.001,
col=colors[1:length(breaks)-1],axes=FALSE
breaks=breaks,xlab="",ylab="",xaxt="n")
axis(side=4,at=0:(length(breaks)-1),
labels=round(breaks),col="white",las=1)
abline(h=c(1:length(breaks)),col="white",lwd=2,xpd=F)
#Map
map(col=sc,fill=TRUE,lty="blank")
# If you get a figure margins error while running the above code,
enlarge the plot device or adjust the margins so that the graph and
scale fit within the device.
map(add=TRUE,col="gray",fill=FALSE)
title("CO2 emissions (kg per 2000 US$ of GDP)")
区域地图
library(maps)
library(RColorBrewer)
x<-map("state",plot=FALSE)#现在x就包含一个state的轮廓图。可以看看x是这样的形式。
for(i in 1:length(rownames(USArrests))) {
for(j in 1:length(x$names)) {
if(grepl(rownames(USArrests)[i],x$names[j],ignore.case=T))
x$measure[j]<-as.double(USArrests$Murder[i])#向x中添加数据
}
}
colors <- brewer.pal(7,"Reds")
sd <- data.frame(col=colors,
values=seq(min(x$measure[!is.na(x$measure)]),
max(x$measure[!is.na(x$measure)])*1.0001,
length.out=7))#从小到大，分成7分。
breaks<-sd$values
matchcol<-function(y) {
as.character(sd$col[findInterval(y,sd$values)])
}
layout(matrix(data=c(2,1), nrow=1, ncol=2),
widths=c(8,1), heights=c(8,1))
# Color Scale first
par(mar = c(20,1,20,7),oma=c(0.2,0.2,0.2,0.2),mex=0.5)
image(x=1, y=0:length(breaks),z=t(matrix(breaks))*1.001,
col=colors[1:length(breaks)-1],axes=FALSE,breaks=breaks,
xlab="", ylab="", xaxt="n")
axis(4,at=0:(length(breaks)-1),
labels=round(breaks),col="white",las=1)
abline(h=c(1:length(breaks)),col="white",lwd=2,xpd=F)
#Map
map("state", boundary = FALSE,col=matchcol(x$measure),#这边就是使得col成为一个变量
fill=TRUE,lty="blank")
map("state", col="white",add = TRUE)
title("Murder Rates by US State in 1973 \n
(arrests per 100,000 residents)", line=2)#两行的形式
map("county", "new york")# draw a county map of New York
map("state", region = c("california", "oregon", "nevada"))#draw a map with three states
map('italy', fill = TRUE, col = brewer.pal(7,"Set1"))
install.packages("sp")
library(sp)
load(url("http://gadm.org/data/rda/FRA_adm1.RData"))#maps数据包中，只有地图，但没有数据。这个网有很多地理数据
gadm$rainfall<-rnorm(length(gadm$NAME_1),mean=50,sd=15)#假设的降雨量
spplot(gadm,"rainfall", col.regions = rev(terrain.colors(gadm$rainfall)),
main="Rainfall (simulated) in French administrative regions")
把数据画在google地图上
install.packages("rgdal")
library(rgdal)
install.packages("RgoogleMaps")
library(RgoogleMaps)
air<-read.csv("londonair.csv")
london<-GetMap(center=c(51.51,-0.116),#确定地图中心点的经纬度。zoom的数字越大，越详细。
zoom =10, destfile = "London.png",maptype = "mobile")#选择一般的路网图
PlotOnStaticMap(london,lat = air$lat, lon = air$lon,
cex=2,pch=19,col=as.character(air$color))
london<-GetMap(center=c(51.51,-0.116),zoom =13,
destfile = "London_satellite.png",maptype = "satellite")#选择卫星图，有"roadmap", "mobile", "satellite", "terrain", "hybrid", "mapmaker-roadmap", and "mapmaker-hybrid".
PlotOnStaticMap(london,lat = air$lat, lon = air$lon,
cex=2,pch=19,col=as.character(air$color))
#前面，add=True可以在原来的图上加图，默认为False。Fun=lines可以划线。fun=function。
GetMap(center=c(40.714728,-73.99867), zoom =14,
destfile = "Manhattan.png", maptype = "hybrid");
GetMap.OSM(lonR= c(-74.67102, -74.63943),#另一个地图来源网址
latR = c(40.33804,40.3556),scale = 7500,
destfile = "PrincetonOSM.png")
KML数据(创建和读取）
install.packages("rgdal")
library(rgdal)
cities <- readOGR(system.file("vectors", package = "rgdal")[1],"cities")
writeOGR(cities, "cities.kml", "cities", driver="KML")
df <- readOGR("cities.kml", "cities")
deal with ESRI
install.packages("maptools")
library(maptools)
sfdata <- readShapeSpatial(system.file("shapes/sids.shp",
package="maptools")[1], proj4string=CRS("+proj=longlat"))
plot(sfdata, col="orange", border="white", axes=TRUE)
writeSpatialShape(sfdata,"xxpoly")
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