.Platform
  setwd('/BiO/example/dataset')
  raw_dat = read.csv(file="Ex_data.csv",
                     header=T,stringsAsFactors=F)
  head(raw_dat[,1:20]) # 열의수가 많아 1:20의 열만 선택
  dim(raw_dat)
  
  # gl ( group의 수, group의 크기 )
  # 1,2,group의 수 해당하는 Factor 형 벡터 생성
  gr_ind = gl(2, 221) # 1 x 221, 2 x 221 = c(1,...,1,2,...,2)
  
  dat_mat <- t(as.matrix(raw_dat[,-1]))
  dim(dat_mat)
  rownames(dat_mat) <- paste0("S",1:nrow(dat_mat))
  colnames(dat_mat) <- raw_dat[,1]
  head(dat_mat[,1:20])
  
  indx <- which(is.na(dat_mat),T)
  indx
  col_ind = indx[,2]
  col_m = apply(dat_mat[,col_ind],2,mean,na.rm=T)
  col_m
  dat_mat[indx] = col_m
  sum(is.na(dat_mat))
  dim(dat_mat)
  
  uq_names <- unique(colnames(dat_mat))
  p <- length(uq_names) ; n <- dim(dat_mat)[1]
  expr_dat <- matrix(0,n,p)
  for(i in 1:p) {
    expr_dat[,i] = apply(as.matrix(
      dat_mat[,colnames(dat_mat)==uq_names[i]]),1,mean)
    cat('\n',i,'-th step') }
  colnames(expr_dat) <- uq_names
  rownames(expr_dat) <- rownames(dat_mat)
  head(expr_dat[,1:20])
  dim(expr_dat); sum(is.na(expr_dat))
  
  #Set working directory
  setwd('./')
  # Read a dataset
  dat = read.table("Ex211.txt",header=T)
  head(dat)
  dat$Job
  attach(dat)
  Job
  plot(Job, main="직업의 막대그림", ylab="인원수(명)", ylim=c(0,15))
  box()
  freq = table(dat[,6])
  barplot(freq)
  
  
  data(VADeaths)
  library(gplots)
  #x11()
  barplot2(VADeaths, beside = TRUE, 
           col = gray(seq(0.4,0.9,length=5)),
           legend = rownames(VADeaths),
           ylim = c(0, 100))
  title(main = "Death Rates in Virginia",font.main = 4)
  
  hh <- t(VADeaths)[,5:1]
  mybarcol <- "gray20"
  ci.l <- hh * 0.85
  ci.u <- hh * 1.15
  mp <- barplot2(hh, beside = TRUE, 
        col = gray(seq(0.4,0.9,length=5)), 
        legend = colnames(VADeaths), 
        ylim = c(0, 100), 
        main = "Death Rates in Virginia", 
        font.main = 4,
        sub = "Faked 95 percent error bars", 
        cex.names = 1.5, 
        plot.ci = TRUE, 
        ci.l = ci.l, ci.u = ci.u, plot.grid = TRUE)
  box()
  
  
  
  #x11()
  pie(freq, main="직업의 원그림")
  pie(rep(1, 24), col = rainbow(24), radius = 0.9)
  pie.sales <- c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12)
  lbl =  c("Blueberry", "Cherry",
           "Apple", "Boston Cream", "Other", "Vanilla Cream")
  names(pie.sales) = paste0(lbl," (",pie.sales*100,"%)")
  pie(pie.sales, col=rainbow(length(pie.sales)))
  
  
  # histogram
  x <- expr_dat[,10]
  #x11()
  hist(x,breaks= 20,col="gray",main=uq_names[10])
  hist(x,breaks= 40,freq=F,col="lightblue",main=uq_names[10])
  hist(x,breaks= 40,plot=F)
  
  
  #boxplot
  mat = expr_dat[,c(3,4,7,8)]
  #x11() # 비어있는 그림 창 생성
  boxplot(mat)
  res = boxplot(mat,plot=F)
  res
  
  #x11()
  c_name = colnames(expr_dat)
  plot(expr_dat[,1],expr_dat[,2],type='l',xlab=c_name[1],ylab=c_name[2])
  #windows()
  plot(expr_dat[,3],expr_dat[,4],type='l',xlab=c_name[1],ylab=c_name[2])
  
  # plot 
  pop_dat = read.csv(file='table_2_2.csv')
  #x11()
  plot(pop_dat[,1],pop_dat[,2],type='l',xlab='연도',ylab='인구수')
  #windows()
  plot(pop_dat[,1],pop_dat[,2],type='b',xlab='연도',ylab='인구수')
  
  # scatter plot
  #x11()
  # pch는 점의 모양 선택, (e.g., pch=16 => 채워진 원)
  ind1 = 8; ind2=12
  plot(expr_dat[,ind1],expr_dat[,ind2],type='p',pch=16,
       xlab=uq_names[ind1],ylab=uq_names[ind2])
  cor_mat = cor(expr_dat)
  which.max(cor_mat[ind1,-ind1])
  ind1 = 8; ind2=200
  plot(expr_dat[,ind1],expr_dat[,ind2],type='p',pch=16,
       xlab=uq_names[ind1],ylab=uq_names[ind2])
  
  
  #x11()
  #pairs example
  ind = c(2,8,12,200)
  pairs(expr_dat[,ind])
  pairs(expr_dat[,ind], "Expression Data",
        pch = 21, bg = c("red", "blue")[gr_ind])
  
  
  # Stat
  mean(expr_dat[,10])
  median(expr_dat[,10])
  
  x = c(1,2,3,1,2,5,5,3,3,3,2)
  tb_x = table(x); tb_x
  as.numeric(names(tb_x)[which.max(tb_x)])
  Mode = function(vec) {
    tb = table(vec)
    return(as.numeric(names(tb)[which.max(tb)])) }
  Mode(x)
  
  quantile(expr_dat[,1],0.25)
  quantile(expr_dat[,1],c(0.25,0.5,0.75))
  
  min(expr_dat[,1])
  max(expr_dat[,1])
  range(expr_dat[,1])
  
  x <- rnorm(100)
  summary(x) # 수치형 자료의 summary 
  y <- c('red','blue','red','white')
  summary(y) # 문자형 자료의 summary
  f.y <- factor(y); summary(f.y) # 요인의 summary
  
  var(expr_dat[,1])
  sum((expr_dat[,1]-mean(expr_dat[,1]))^2)/(n-1)
  sd(expr_dat[,1])
  
  # CV
  height=c(72, 74, 68, 76, 74, 69, 72, 79, 70, 69, 77, 73)
  sd(height)/mean(height)*100
  
  
  install.packages("moments")
  library(moments)
  skewness(expr_dat[,1])
  kurtosis(expr_dat[,1])
  hist(expr_dat[,1],freq=F)
  x = seq(5.5,8.5,length=100)
  lines(x,dnorm(x,mean=mean(expr_dat[,1]),sd(expr_dat[,1])))
  
  # contingency table
  #1차원 도수분포표
  table(mtcars$cyl)
  table(mtcars$am)
  #2차원 분할표
  table(mtcars$cyl,mtcars$am)
  #3차원 분할표
  table(mtcars$cyl,mtcars$am,mtcars$gear)
  
  # cov , cor
  cov(expr_dat[,1],expr_dat[,5])
  cov(expr_dat[,c(1,5,8)])
  var(expr_dat[,1])
  
  cor(expr_dat[,1],expr_dat[,5])
  cor(expr_dat[,c(1,5,8)])