> x1=c(1,2,3,4,5,6,7,8,9)//c()=产生一个向量
> x1
[1] 1 2 3 4 5 6 7 8 9
> mode(x1)
[1] "numeric"
> length(x1)
[1] 9
> rbind(x1,x1)//整合连个向量，形成一个矩阵
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
x1 1 2 3 4 5 6 7 8 9
x1 1 2 3 4 5 6 7 8 9
> cbind(x1,x1)
x1 x1
[1,] 1 1
[2,] 2 2
[3,] 3 3
[4,] 4 4
[5,] 5 5
[6,] 6 6
[7,] 7 7
[8,] 8 8
[9,] 9 9
> mean(x1)//求平均数
[1] 5
> sum(x1)//求和
[1] 45
> max(x2)//求最大最小值
[1] 100
> min(x1)
[1] 1
> var(x1)//求方差（variance）
[1] 7.5
> prod(x1)
[1] 362880
> prod(x2)
[1] 9.332622e+157
>
> sd(x2)//标准差（ standard deviation）
[1] 29.01149
一些语法
> 1:10
[1] 1 2 3 4 5 6 7 8 9 10
> 1:10-1
[1] 0 1 2 3 4 5 6 7 8 9
> 2:60*2+1
[1] 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
[19] 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
[37] 77 79 81 83 85 87 89 91 93 95 97 99 101 103 105 107 109 111
[55] 113 115 117 119 121
> 1:10*2
[1] 2 4 6 8 10 12 14 16 18 20
> 2:60*2+1
[1] 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
[19] 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
[37] 77 79 81 83 85 87 89 91 93 95 97 99 101 103 105 107 109 111
[55] 113 115 117 119 121
>
> 2:60*2+1
[1] 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
[19] 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
[37] 77 79 81 83 85 87 89 91 93 95 97 99 101 103 105 107 109 111
[55] 113 115 117 119 121
> 2:60*2+1
[1] 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
[21] 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83
[41] 85 87 89 91 93 95 97 99 101 103 105 107 109 111 113 115 117 119 121
> a=2:60*2+1
> a
[1] 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
[21] 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83
[41] 85 87 89 91 93 95 97 99 101 103 105 107 109 111 113 115 117 119 121
> a[5]
[1] 13
> a[-5]
[1] 5 7 9 11 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45
[21] 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85
[41] 87 89 91 93 95 97 99 101 103 105 107 109 111 113 115 117 119 121
> a[1:5]
[1] 5 7 9 11 13
> a[-(1:5)]
[1] 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
[21] 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93
[41] 95 97 99 101 103 105 107 109 111 113 115 117 119 121
> a[c(2,4,7)]
[1] 7 11 17
>
> a[3:8]
[1] 9 11 13 15 17 19
> a[a<20]
[1] 5 7 9 11 13 15 17 19
> a[a[3]]
[1] 21
> a[9]
[1] 21
> seq(5,20)//产生一个向量，可以指定
[1] 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
> seq(5,121,by=2)
[1] 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
[21] 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83
[41] 85 87 89 91 93 95 97 99 101 103 105 107 109 111 113 115 117 119 121
> seq(5,121,length=10)
[1] 5.00000 17.88889 30.77778 43.66667 56.55556 69.44444 82.33333 95.22222
[9] 108.11111 121.00000
> letters[1:30]
[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t"
[21] "u" "v" "w" "x" "y" "z" NA NA NA NA
> letters//内置对象，存着26个字幕
[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t"
[21] "u" "v" "w" "x" "y" "z"
> a=seq(2,40)
> which.max(a) //找位置，各种找位置
[1] 39
> which(a==2)
[1] 2
> which(a>5)
[1] 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
> a=1:20
> a
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
> rev(a)//翻转
[1] 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
> sort(a)//排序
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
> rev(sort(a))
[1] 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
> a1=c(1:12)
> a1=c(1:12)
> matrix(a1,nrow=4,ncol=3)//矩阵
[,1] [,2] [,3]
[1,] 1 5 9
[2,] 2 6 10
[3,] 3 7 11
[4,] 4 8 12
> matrix(a1,nrow=3,ncol=4)
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
> matrix(a1,nrow=4,ncol=3,byrow=T)
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 5 6
[3,] 7 8 9
[4,] 10 11 12
矩阵的转置，加减法
> a=b=matrix(a1,nrow=4,ncol=3,byrow=T)
> a
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 5 6
[3,] 7 8 9
[4,] 10 11 12
> b
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 5 6
[3,] 7 8 9
[4,] 10 11 12
> t(a)//转置
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
> a+b//加法
[,1] [,2] [,3]
[1,] 2 4 6
[2,] 8 10 12
[3,] 14 16 18
[4,] 20 22 24
> a-b//减法
[,1] [,2] [,3]
[1,] 0 0 0
[2,] 0 0 0
[3,] 0 0 0
[4,] 0 0 0
矩阵乘法
> a=matrix(1:12,nrow=3,ncol=4)
> b=matrix(1:12,nrow=4,ncol=3)
> a
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
> b
[,1] [,2] [,3]
[1,] 1 5 9
[2,] 2 6 10
[3,] 3 7 11
[4,] 4 8 12
> a%*%b
[,1] [,2] [,3]
[1,] 70 158 246
[2,] 80 184 288
[3,] 90 210 330
方阵的对角线
> a=matrix(1:16,nrow=4)
> a
[,1] [,2] [,3] [,4]
[1,] 1 5 9 13
[2,] 2 6 10 14
[3,] 3 7 11 15
[4,] 4 8 12 16
> diag(a)//结果是一个向量
[1] 1 6 11 16
> diag(diag(a))//产生以向量为对角线的矩阵
[,1] [,2] [,3] [,4]
[1,] 1 0 0 0
[2,] 0 6 0 0
[3,] 0 0 11 0
[4,] 0 0 0 16
> diag(4)//产生四阶单位矩阵
[,1] [,2] [,3] [,4]
[1,] 1 0 0 0
[2,] 0 1 0 0
[3,] 0 0 1 0
[4,] 0 0 0 1
> diag(seq(1,5))//产生以向量为对角线的矩阵
[,1] [,2] [,3] [,4] [,5]
[1,] 1 0 0 0 0
[2,] 0 2 0 0 0
[3,] 0 0 3 0 0
[4,] 0 0 0 4 0
[5,] 0 0 0 0 5
> rnorm(16)//以正态分布产生16个随机数 [1] 0.79027687 1.14167897 1.27162428 -1.13071815 -1.46295346 -0.33647679 [7] -0.20166697 0.02592894 0.20498691 1.51331875 1.35167580 1.40470721 [13] -0.16802030 -0.35107031 -0.51437608 -0.09406821 > a=matrix(rnorm(16),4,4) > a [,1] [,2] [,3] [,4] [1,] -1.4205777 0.3643621 0.82097989 1.03121963 [2,] 0.1486225 -0.7520685 0.68004193 -0.03371108 [3,] -1.4458179 -0.8287518 1.48177576 0.09116119 [4,] -1.3000649 -0.1764955 0.02366358 -0.06364255
> solve(a)//求逆矩阵（这个果真是不好求啊，电脑明显顿了一下）
[,1] [,2] [,3] [,4]
[1,] -0.02472174 0.2219122 -0.07808618 -0.6299696
[2,] -0.31118268 -2.6935027 1.43350614 -1.5621103
[3,] -0.27601213 -1.4377140 1.51227778 -1.5445831
[4,] 1.26536035 2.4019986 -1.81803407 0.9137964
> a
[,1] [,2] [,3] [,4]
[1,] -1.4205777 0.3643621 0.82097989 1.03121963
[2,] 0.1486225 -0.7520685 0.68004193 -0.03371108
[3,] -1.4458179 -0.8287518 1.48177576 0.09116119
[4,] -1.3000649 -0.1764955 0.02366358 -0.06364255
> b=c(1:4)
> b
[1] 1 2 3 4
> solve(a,b)//解线性方程组，a*x=b的值
[1] -2.335034 -7.646111 -4.792939 4.270441
求矩阵的特征值和特征向量（考研的童鞋慢慢的回忆啊）
> a=diag(4)+1
> a
[,1] [,2] [,3] [,4]
[1,] 2 1 1 1
[2,] 1 2 1 1
[3,] 1 1 2 1
[4,] 1 1 1 2
> a.e=eigen(a,symmetric=T)
> a.e
$values
[1] 5 1 1 1
$vectors
[,1] [,2] [,3] [,4]
[1,] -0.5 0.8660254 0.0000000 0.0000000
[2,] -0.5 -0.2886751 -0.5773503 -0.5773503
[3,] -0.5 -0.2886751 -0.2113249 0.7886751
[4,] -0.5 -0.2886751 0.7886751 -0.2113249
> a.e$vectors%*%diag(a.e$values)%*%t(a.e$vectors)（没错，就是那个公式）
[,1] [,2] [,3] [,4]
[1,] 2 1 1 1
[2,] 1 2 1 1
[3,] 1 1 2 1
[4,] 1 1 1 2
>
上边是向量和矩阵两种数据类型，下边是数组类型
> x=c(1:6)
> is.vector(x)
[1] TRUE
> is.array(x)
[1] FALSE
> is.matrix(x)
[1] FALSE
> dim(x)=c(2,3)
> is.vector(x)
[1] FALSE
> is.array(x)
[1] TRUE
> is.matrix(x)//从这看以得知，矩阵就是2维的数组
[1] TRUE
> x
[,1] [,2] [,3]
[1,] 1 3 5
[2,] 2 4 6
数据框：矩阵形式，但列可以是不同类型。
每列是一个变量（所以只能取到一列也就是一个变量的值），每行是一个观测值（样本）。
> x1=seq(6,10)
> x2=seq(19,23)
> x1
[1] 6 7 8 9 10
> x2
[1] 19 20 21 22 23
> x3=data.frame(x1,x2)//处理x1，x2，产生一个数据框
> x3
x1 x2
1 6 19
2 7 20
3 8 21
4 9 22
5 10 23
> x3[1]
x1
1 6
2 7
3 8
4 9
5 10
> x3[2]
x2
1 19
2 20
3 21
4 22
5 23
> x=data.frame("重量"=x1,"运费"=x2)
> x
重量 运费
1 6 19
2 7 20
3 8 21
4 9 22
5 10 23plot(x)//以上边的数据框x变量中的数值，画散点图。有两列，两个变量，画出来就是两个坐标轴，2维的。
For循环
> for(i in 1:59) {a[i]=i*2+3} > a [1] 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 [21] 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 [41] 85 87 89 91 93 95 97 99 101 103 105 107 109 111 113 115 117 119 121 >while循环
> a[1]=5 > i=1 > while(a[i]<121){i=i+1;a[i]=a[i-1]+2} > a [1] 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 [21] 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 [41] 85 87 89 91 93 95 97 99 101 103 105 107 109 111 113 115 117 119 121 >产生各种分布的向量> x1=round(runif(100,min=80,max=100))
> x1
[1] 98 87 85 92 86 90 96 91 90 95 94 93 99 84 93 81 81 94 92 81
[21] 84 82 85 88 81 94 97 99 81 89 85 100 90 82 87 87 83 96 83 92
[41] 94 97 88 94 88 92 82 93 92 81 85 86 87 98 84 97 91 95 86 81
[61] 85 83 81 94 90 81 89 96 92 86 96 89 100 81 97 82 87 96 91 86
[81] 92 97 96 99 86 99 82 89 96 94 86 98 91 99 95 98 100 92 87 85
> x1=round(rnorm(100,mean=80,sd=7))
> x1
[1] 99 89 72 92 72 88 68 78 80 84 76 74 74 90 78 86 74 76 80 74 58 91 69 90 72 88 78
[28] 90 79 76 82 77 80 85 77 74 70 89 85 91 67 82 64 85 75 82 82 88 85 84 86 71 69 87
[55] 90 70 87 61 74 76 76 78 79 89 79 73 85 82 78 77 82 75 78 82 84 94 69 92 72 73 93
[82] 76 90 77 76 82 87 82 81 82 75 78 77 88 68 74 78 82 74 90
> x1[which(x1>100)]=100
> x1
[1] 99 89 72 92 72 88 68 78 80 84 76 74 74 90 78 86 74 76 80 74 58 91 69 90 72 88 78
[28] 90 79 76 82 77 80 85 77 74 70 89 85 91 67 82 64 85 75 82 82 88 85 84 86 71 69 87
[55] 90 70 87 61 74 76 76 78 79 89 79 73 85 82 78 77 82 75 78 82 84 94 69 92 72 73 93
[82] 76 90 77 76 82 87 82 81 82 75 78 77 88 68 74 78 82 74 90
生成数据框并写入文件
> a1=round(rnorm(100,mean=90,sd=10))
> a1
[1] 90 89 94 108 72 97 88 99 98 89 74 104 81 84 77 72 83 94 100 95
[21] 100 94 87 90 87 92 81 97 78 98 100 101 90 82 94 92 83 80 103 91
[41] 83 84 90 92 97 99 108 68 72 84 78 93 98 91 85 117 103 71 87 100
[61] 96 88 93 89 90 130 90 94 75 85 87 105 94 75 88 96 104 88 86 102
[81] 109 83 87 95 114 91 88 94 82 90 104 80 83 79 87 95 99 92 78 87
> a2=round(rnorm(100,mean=90,sd=50))
> a2
[1] 94 14 -23 80 119 149 83 105 91 189 192 67 173 34 69 65 207 144 69 115
[21] 145 95 49 103 29 59 103 126 66 137 112 104 84 167 137 78 123 2 63 60
[41] 127 62 69 149 35 52 136 84 23 48 110 68 58 151 59 123 -20 157 161 48
[61] 20 138 118 44 54 197 67 175 180 28 31 23 25 94 61 80 144 58 85 79
[81] 67 117 26 -7 82 138 130 80 40 59 157 126 93 -60 48 123 37 75 18 230
> a3=round(rnorm(100,mean=90,sd=50))
> a3=round(rnorm(100,mean=90,sd=2))
> a3
[1] 89 91 88 89 90 89 91 88 92 87 90 89 92 89 88 92 89 92 88 88 88 90 89 95 91 87 91
[28] 94 90 90 94 93 91 89 91 92 93 88 89 87 91 88 90 91 94 90 89 88 93 92 88 91 88 90
[55] 90 93 92 91 89 89 85 94 90 87 89 88 86 93 94 87 91 88 88 89 90 91 93 90 90 92 88
[82] 89 91 89 91 89 90 93 92 91 90 89 90 89 91 90 86 92 92 93
> a4=round(rnorm(100,mean=90,sd=60))
> a4
[1] 87 13 125 109 179 29 40 27 152 187 68 5 120 105 123 186 148 167 110 115
[21] 114 8 119 64 29 107 120 6 123 206 141 124 96 66 -19 192 33 163 195 156
[41] 167 116 72 69 45 146 98 54 127 102 83 68 43 129 26 83 138 53 92 218
[61] 245 98 132 36 93 46 44 -21 15 87 143 6 112 143 79 145 41 69 99 0
[81] 196 15 96 120 -4 126 104 63 156 62 -58 37 104 136 71 213 59 152 8 102
> a=data.frame(a1,a2,a3,a4)//向量生成数据框
> write.table(x,file="d:\\mark.txt",col.names=F,row.names=F,sep=" ") //写入文件
a是一个数据框
> colMeans(a) a1 a2 a3 a4 90.80 88.58 90.10 94.37 > colMeans(a)[c("a1","a2","a3")] a1 a2 a3 90.80 88.58 90.10 >强大的apply函数，第一个参数是一个数据框，第二个参数，1为对行处理，2为对列处理，第三个参数为action，值可为max,min,mean,sum等
> apply(x,1,mean)
[1] 12.5 13.5 14.5 15.5 16.5
> apply(x,1,max)
[1] 19 20 21 22 23