如何找到R数据帧或矩阵的列的均值？
如果R数据框中的所有列都是数字，则有必要找到每个列的均值。此计算将帮助我们查看每个列的均值的差异如何，但要确保它们之间存在显着差异，我们将需要进行假设检验。要找到数据框或矩阵的列均值，我们可以使用colMeans函数。
示例
请看以下数据帧-
set.seed(9)x1<-rnorm(20,0.2)
x2<-rnorm(20,0.5)
x3<-rnorm(20,0.8)
x4<-rnorm(20,1.5)
x5<-rpois(20,2)
x6<-rpois(20,5)
df<-data.frame(x1,x2,x3,x4,x5,x6)
df
输出结果
x1 x2 x3 x4 x5 x61 -0.56679604 2.2569929 -0.008456344 1.7812222 2 4
2 -0.61645834 0.6822521 -1.219381694 0.2972914 4 5
3 0.05846481 0.2331113 0.061275928 1.9651637 1 3
4 -0.07760503 1.4264216 1.182886561 1.8520164 4 5
5 0.63630690 -0.1933319 2.530863668 0.9101438 2 3
6 -0.98687252 3.1819901 0.596918049 0.6464000 4 10
7 1.39198691 0.7225245 -0.196397348 1.2532679 4 7
8 0.18180966 -0.2066724 -0.506536295 3.0393386 3 5
9 -0.04808460 0.9172132 -0.197831604 2.0460777 0 4
10 -0.16293689 0.8695568 0.234971274 3.0649619 1 3
11 1.47757055 -0.4137643 2.552057836 1.7496702 2 4
12 -0.26889715 0.1830853 0.252228648 -0.4624186 1 4
13 0.27105410 1.5490592 -0.058525708 0.6909398 3 3
14 -0.06603845 0.6681118 0.849294533 1.0013149 2 5
15 2.04525720 0.5314402 0.599955518 1.8051218 1 7
16 -0.63944966 -0.5103305 -0.303954449 1.2107928 0 4
17 0.12255194 0.8827515 1.040588038 2.9577142 0 2
18 -2.41770553 -0.3196965 1.181113616 2.3737555 5 5
19 1.08788403 0.8617111 3.030458950 0.5470440 3 6
20 -0.50749145 0.5933714 1.999202392 1.4683245 1 6
colMeans(df)x1 x2 x3 x4 x5 x6
0.04572752 0.69578987 0.68103658 1.50990712 2.15000000 4.75000000
示例
如果矩阵是方形矩阵，则求矩阵的列均值-
Matrix<-matrix(1:100,nrow=10)Matrix
输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10][1,] 1 11 21 31 41 51 61 71 81 91
[2,] 2 12 22 32 42 52 62 72 82 92
[3,] 3 13 23 33 43 53 63 73 83 93
[4,] 4 14 24 34 44 54 64 74 84 94
[5,] 5 15 25 35 45 55 65 75 85 95
[6,] 6 16 26 36 46 56 66 76 86 96
[7,] 7 17 27 37 47 57 67 77 87 97
[8,] 8 18 28 38 48 58 68 78 88 98
[9,] 9 19 29 39 49 59 69 79 89 99
[10,] 10 20 30 40 50 60 70 80 90 100
colMeans(Matrix)
[1] 5.5 15.5 25.5 35.5 45.5 55.5 65.5 75.5 85.5 95.5
示例
如果矩阵不是正方形矩阵，则求矩阵的列均值-
Matrix_new<-matrix(1:100,ncol=20)Matrix_new
输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14][1,] 1 6 11 16 21 26 31 36 41 46 51 56 61 66
[2,] 2 7 12 17 22 27 32 37 42 47 52 57 62 67
[3,] 3 8 13 18 23 28 33 38 43 48 53 58 63 68
[4,] 4 9 14 19 24 29 34 39 44 49 54 59 64 69
[5,] 5 10 15 20 25 30 35 40 45 50 55 60 65 70
[,15] [,16] [,17] [,18] [,19] [,20]
[1,] 71 76 81 86 91 96
[2,] 72 77 82 87 92 97
[3,] 73 78 83 88 93 98
[4,] 74 79 84 89 94 99
[5,] 75 80 85 90 95 100
colMeans(Matrix_new)
[1] 3 8 13 18 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 98
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