几个R语言画图小程序分享

• 三维图

x=seq(-5,5,by=0.1) #步长很小时画的图就是黑色的了，因为都是画格子的黑线的颜色y=xx1=dnorm(x,0,1)  #dnorm()为正态分布密度函数z=outer(x1,x1)persp(x,y,z,theta =30,phi = 25,expand = 0.5,col = "Blue2")

• 等高线图

library(MASS)Sigma <- matrix(c(1,0.7,0.7,1),2,2)Sigmar=mvrnorm(n=1000, c(0,3), Sigma)par(mfrow = c(2, 2))#density plotplot(density(r))#kernel density estimatebivn.kde <- kde2d(r[,1], r[,2], n = 50)#perspective plotpersp(bivn.kde, phi = 45, theta = 30)#contour plot contour(bivn.kde)#contour plot with imageimage(bivn.kde,col = terrain.colors(100)); contour(bivn.kde, add = T)

• 区间概率变化

library(animation)oopt = ani.options(interval = 0.5)for (r2 in seq(-4,4,l=20)) {x=c(seq(-4,4,l=1000))r1=-3 x2=c(r1,r1,x[x<r2&x>r1],r2,r2) y2=c(0,dnorm(c(r1,x[x<r2&x>r1],r2)),0) plot(x,dnorm(x),type="l",ylab=expression(phi(x))) abline(h=0);polygon(x2,y2,col="red")text(-3,0.3,r2) ani.pause()}ani.options(oopt)

• 大物实验报告数据分析

x <-c(2.55,2.67,3.00,3.66,3.99,4.73,5.39,6.29,6.94,6.96,7.37,7.69,8.00,8.37,9.51,10.98,12.40)y <- c(0.84,0.84,0.83,0.80,0.60,0.38,0.06,0.07,0.06,0.05,0.06,0.05,0.04,0.03,0.02,0.01,0)plot(x,y,pch=20)lines(x,y)

• 饼图

x=c(98,99,60,48)y=c("概率论","数理统计","复变函数","实变函数")pie(x,labels=y,main="刘青总成绩分布")

• 两侧分位数

              #画t分布密度及双侧分位数p=0.05qt(0.025,19)  #已知概率，计算对应的分位数x=seq(-6,6,length=1000)y=dt(x,19)r1=-6         r2=-2.093r3=2.093r4=6              #范围x2=c(r1,r1,x[x<r2&x>r1],r2,r2)y2=c(0,dt(c(r1,x[x<r2&x>r1],r2),19),0)#对应具体的分布x3=c(r3,r3,x[x<r4&x>r3],r4,r4)y3=c(0,dt(c(r3,x[x<r4&x>r3],r4),19),0)#对应具体的分布plot(x,y,type="l",ylab="Density of t(19)",xlim=c(-5,5))              #画出t的密度函数图像abline(h=0)              #图颜色#polygon(x2,y2,col="red")#polygon(x3,y3,col="red")polygon(c(x2,x3),c(y2,y3),col="red")              #加标题title("Tail Probability for t(19)")text(c(-3.9,-1.3,3.5,4.1),c(0.03,0.01,0.05,0.01),c("p-value/2=0.05","t=-2.093","p-value/2=0.05","t=2.093"))              #画F（2,2）分布密度及双侧分位数qf(0.2,2,2)  #已知概率，计算对应的分位数x<-seq(0,15,length=1000)y<-df(x,2,2)plot(x,y,type="l")r1<-0r2<-0.111r3<-9r4<-15x2<-c(r1,r1,x[x<r2&x>r1],r2,r2)y2<-c(0,df(c(r1,x[x<r2&x>r1],r2),2,2),0)        #对应具体的分布x3<-c(r3,r3,x[x<r4&x>r3],r4,r4)y3<-c(0,dt(c(r3,x[x<r4&x>r3],r4),2,2),0)         #对应具体的分布plot(x,y,type="l",ylab="density of F(2,2)")abline(h=0)polygon(c(x2,x3),c(y2,y3),col="red")              #加标题title("Tail Probability for F(2,2)")text(c(1.9,0.8,9,10),c(0.1,-0.01,0.05,-0.018),c("p-value/2=0.2","t=-2.093","p-value/2=0.2","t=2.093"))

• 分位数图

                  #t(19)的分位数qt(0.005,19)      #计算分位数x<-seq(-6,6,length=1000)y<-dt(x,19)plot(x,y,type="l",ylab="density of t(19)",xlim=c(-5,5),ylim=c(0,0.5))r1<--6r2<--2.8609x2<-c(r1,r1,x[x<r2&x>r1],r2,r2)           #选择区域y2<-c(0,dt(c(r1,x[x<r2&x>r1],r2),19),0)   #要对应具体的分布abline(h=0)polygon(x2,y2,col="red")                  #加上标题title("Tail Probability for t(19)")text(c(-4.1,-2.5),c(0.02,-0.007),c("p-value=0.005","t=-2.8609"))                #标准正态分布的分位数qnorm(0.005,0,1)#计算分位数x<-seq(-6,6,length=1000)y<-dnorm(x,0,1)plot(x,y,type="l",ylab="density of N(0,1)",xlim=c(-5,5),ylim=c(0,0.5))r1<--6r2<--2.575829x2<-c(r1,r1,x[x<r2&x>r1],r2,r2)                #选择区域y2<-c(0,dnorm(c(r1,x[x<r2&x>r1],r2),0,1),0)    #要对应具体的分布abline(h=0)polygon(x2,y2,col="red")title("Tail Probability for N(0,1)")text(c(-4.1,-2.5),c(0.02,-0.007),c("p-value=0.005","t=-2.8609"))                   #卡方（5）的分位数qchisq(0.025,5)#计算分位数x<-seq(0,10,length=1000)y<-dchisq(x,5)plot(x,y,type="l",ylab="density of X^2(5)")r1<-0r2<-0.8312x2<-c(r1,r1,x[x<r2&x>r1],r2,r2)                 #选择区域y2<-c(0,dchisq(c(r1,x[x<r2&x>r1],r2),5),0)      #对应具体的分布abline(h=0)polygon(x2,y2,col="red")title("Tail Probability for X^2(5)")text(c(0.4,0.5),c(0.02,-0.007),c("p-value=0.005","t=0.8312"))                         #F（2,2）的分位数qf(0.9,2,2)              #计算分位数x<-seq(0,15,length=1000)y<-df(x,2,2)plot(x,y,type="l",ylab="density of F(2,2)")r1<-0r2<-9x2<-c(r1,r1,x[x<r2&x>r1],r2,r2)                 #选择区域y2<-c(0,df(c(r1,x[x<r2&x>r1],r2),2,2),0)        #对应具体的分布abline(h=0)polygon(x2,y2,col="red")title("Tail Probability for F(2,2)")text(c(7.8,9.1),c(0.05,0.1),c("p-value=0.9","t=9"))

• k线图

#采用默认的ChartSeries绘制K线图  library(quantmod)  data_origin=read.csv("data.csv",header = F)data<-data.frame(Open=data_origin[,2],High=data_origin[,3],Low=data_origin[,4],Close=data_origin[,5],Volume=data_origin[,7],Adjusted=data_origin[,6])rownames(data)<-as.Date(as.character(data_origin$V1),"%Y%m%d")chartSeries(data)

PS：获取当前工作目录和设置工作目录的方法
getwd()
setwd(‘E:\R工作目录’)