# 数据读入
C=read.csv(file=file.choose(),head=T)
C=read.csv("2011Dhs300.csv",header=T)
colMeans(C)
cov(C)
cor(C)

#例1.1
C1=C[1:12,1:3]
write.table(C1,file ="C1.txt")   #把表格写到当前目录的文件中
colMeans(C1)
cov(C1)
cor(C1)
cor.test(~X1+X2, data=C1)#相关性检验
cor.test(~X1+X3, data=C1)
cor.test(~X2+X3, data=C1)


#多元数据的图表示
D=read.csv("hn.csv",header=F)
outline<-function(x, txt=TRUE){if (is.data.frame(x)==TRUE)#轮廓图
   x<-as.matrix(x);m<-nrow(x); n<-ncol(x)
   plot(c(1,n), c(min(x),max(x)), type="n", main="The outline graph of Data",xlab="Number",ylab="Value")
   for(i in 1:m){lines(x[i,], col=i)
   if (txt==TRUE){k<-dimnames(x)[[1]][i]
   text(1+(i-1)%%n, x[i,1+(i-1)%%n], k)}}}
outline(D)#轮廓图
stars(D)#星图
stars(D,full=FALSE, draw.segments = TRUE,key.loc = c(5,0.5), mar = c(2,0,0,0))#半幅星图

#脸谱图
D=read.csv("hn.csv",header=F)
library(aplpack)
faces(D)
library(tcltk2)
faces(D,face.type=2)
faces(D,face.type=2)

#调和曲线图
unison<-function(x){ if (is.data.frame(x)==TRUE)
      x<-as.matrix(x); t<-seq(-pi, pi, pi/30); m<-nrow(x); n<-ncol(x); f<-array(0, c(m,length(t)))
   for(i in 1:m){f[i,]<-x[i,1]/sqrt(2)
      for( j in 2:n){ if (j%%2==0) f[i,]<-f[i,]+x[i,j]*sin(j/2*t)
          else f[i,]<-f[i,]+x[i,j]*cos(j%/%2*t) } }
   plot(c(-pi,pi), c(min(f),max(f)), type="n", main="The Unison graph of Data", xlab="t", ylab="f(t)")
  for(i in 1:m) lines(t, f[i,] , col=i)}
unison(D)