dta <-  read.table("hs0.txt", sep='\t', header = T)

原for-loop

dta.asian <- subset(dta, race=="asian")
# 切割race="asian"

r0 <- cor(dta.asian$math, dta.asian$socst)
# math和socst的相關係數

cnt <- 0
nIter <- 1001
for (i in 1:nIter) # i依序帶入1~1001
  {
    new <- sample(dta.asian$read) # new=read的隨機抽樣
    r <- cor(new, dta.asian$math) # r=new和math的相關
    if ( r0 <= r ) cnt <- cnt+1 # 如果r0小於或等於r，cnt+1
}

cnt/nIter
## [1] 0.03196803

使用eval = F，先不讓chunk輸出

newread <- replicate(nIter, sample(dta.asian$read))
# newread= 抽樣1001次read分數

newread <- data.frame(unlist(newread))
# newread轉換為資料框

with(newread, lapply(names(newread), function(x)
cor(dta.asian$math, eval(substitute(tmp, list(tmp=as.name(x))))))
)
# 計算相關係數的檢定
cor.test(dta[dta$race=="asian", "math"], dta[dta$race=="asian", "socst"])

改while-loop

dta.asian <- subset(dta, race=="asian")
# 切割race="asian"

r0 <- cor(dta.asian$math, dta.asian$socst)
# math和socst的相關係數

cnt <- 0
nIter <- 1001
i <- 0
while (i <= nIter) # 當i小於1001時，依序帶入
  {
  i <- i+1
    new <- sample(dta.asian$read) # new=read的隨機抽樣
    r <- cor(new, dta.asian$math) # r=new和math的相關
    if ( r0 <= r ) cnt <- cnt+1 # 如果r0小於或等於r，cnt+1
}

cnt/nIter
## [1] 0.03596404

改repeat-loop

dta.asian <- subset(dta, race=="asian")
# 切割race="asian"

r0 <- cor(dta.asian$math, dta.asian$socst)
# math和socst的相關係數

cnt <- 0
nIter <- 1001
i <- 0
repeat # 重複執行，但當i大於1001時，則停止
  {
  if(i>nIter) break
  i <- i+1
    new <- sample(dta.asian$read) # new=read的隨機抽樣
    r <- cor(new, dta.asian$math) # r=new和math的相關
    if ( r0 <= r ) cnt <- cnt+1 # 如果r0小於或等於r，cnt+1
}

cnt/nIter
## [1] 0.04895105