有些向量是在R中随机创建的，而有些不是在R中随机创建的，但是我们可以对这两种类型的向量进行随机化处理。随机化可确保无偏性，因此，特别是在创建具有易于改变分析结果的目标的矢量时，这是必要的。R中的随机化可以简单地借助样本函数完成。
不是随机创建的向量的随机化-
> x1<-1:30 > x1 [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 [26] 26 27 28 29 30 > sample(x1) [1] 18 24 20 2 26 15 14 9 13 1 16 27 30 29 6 22 3 12 5 10 19 8 17 21 7 [26] 25 11 23 28 4 > x2<-letters[1:26] > x2 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" [20] "t" "u" "v" "w" "x" "y" "z" > sample(x2) [1] "s" "f" "z" "w" "k" "c" "e" "m" "b" "t" "x" "d" "v" "y" "r" "g" "i" "o" "p" [20] "h" "u" "n" "j" "a" "l" "q" > x3<-rep(c(1,2,3,4,5),each=10) > x3 [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 [39] 4 4 5 5 5 5 5 5 5 5 5 5 > sample(x3) [1] 5 4 2 1 1 4 3 3 2 1 3 5 4 5 5 1 2 1 3 5 2 1 3 4 5 3 1 2 4 3 4 5 2 4 3 5 2 2 [39] 5 4 1 2 5 1 3 1 3 4 2 4
随机创建的向量的随机化-
> x4<-rnorm(20,0.5)
> x4
[1] 0.46076000 1.18973936 0.52800216 -0.24327321 0.68879230 -1.30495863
[7] 1.96555486 0.65325334 2.67261167 0.97550953 -0.20994643 1.11072635
[13] -0.43409763 -0.75363340 0.79144624 0.05670813 0.50110535 0.57434132
[19] -0.08952095 -0.06866873
> sample(x4)
[1] -0.75363340 0.50110535 0.52800216 0.57434132 1.96555486 -0.06866873
[7] -0.08952095 0.79144624 1.11072635 0.46076000 2.67261167 1.18973936
[13] 0.65325334 -1.30495863 -0.20994643 0.97550953 -0.43409763 -0.24327321
[19] 0.05670813 0.68879230
> x5<-rpois(30,2)
> x5
[1] 5 3 1 2 5 5 1 1 1 1 2 4 2 1 0 2 3 1 0 1 2 1 3 3 2 2 2 1 2 4
> sample(x5)
[1] 3 5 1 3 1 5 3 1 5 2 4 1 2 2 2 2 1 2 1 1 1 2 0 3 1 4 2 2 1 0
> x6<-runif(30,2,5)
> x6
[1] 3.119190 2.143877 2.415885 2.964476 2.464495 2.396685 2.663918 2.679142
[9] 2.394250 4.944690 2.981041 3.520818 4.044328 2.297507 2.356708 2.151319
[17] 4.787762 4.021137 2.284574 3.477788 3.384656 3.125650 4.973298 2.529052
[25] 4.440306 2.205340 3.201349 2.423433 2.579930 4.524055
> sample(x6)
[1] 4.044328 2.394250 4.440306 2.663918 2.423433 2.297507 2.464495 3.201349
[9] 3.477788 3.125650 4.944690 2.679142 3.119190 2.205340 2.356708 3.520818
[17] 4.524055 2.151319 3.384656 2.143877 4.787762 4.021137 2.579930 2.964476
[25] 4.973298 2.529052 2.284574 2.981041 2.396685 2.415885
> x7<-rep(c("Apple","Guava","Banana","Kiwi","Mango","Orange"),times=10)
> x7
[1] "Apple" "Guava" "Banana" "Kiwi" "Mango" "Orange" "Apple" "Guava"
[9] "Banana" "Kiwi" "Mango" "Orange" "Apple" "Guava" "Banana" "Kiwi"
[17] "Mango" "Orange" "Apple" "Guava" "Banana" "Kiwi" "Mango" "Orange"
[25] "Apple" "Guava" "Banana" "Kiwi" "Mango" "Orange" "Apple" "Guava"
[33] "Banana" "Kiwi" "Mango" "Orange" "Apple" "Guava" "Banana" "Kiwi"
[41] "Mango" "Orange" "Apple" "Guava" "Banana" "Kiwi" "Mango" "Orange"
[49] "Apple" "Guava" "Banana" "Kiwi" "Mango" "Orange" "Apple" "Guava"
[57] "Banana" "Kiwi" "Mango" "Orange"
> sample(x7)
[1] "Apple" "Guava" "Banana" "Guava" "Mango" "Mango" "Guava" "Orange"
[9] "Banana" "Guava" "Guava" "Orange" "Banana" "Apple" "Banana" "Apple"
[17] "Banana" "Guava" "Kiwi" "Orange" "Mango" "Mango" "Guava" "Banana"
[25] "Kiwi" "Kiwi" "Mango" "Mango" "Banana" "Apple" "Orange" "Orange"
[33] "Apple" "Apple" "Guava" "Apple" "Kiwi" "Apple" "Kiwi" "Kiwi"
[41] "Kiwi" "Orange" "Orange" "Banana" "Guava" "Apple" "Orange" "Mango"
[49] "Kiwi" "Mango" "Mango" "Orange" "Mango" "Orange" "Kiwi" "Guava"
[57] "Banana" "Kiwi" "Apple" "Banana"