如何标准化R数据框中的列？
标准化是指以某种方式转换R数据帧的向量或列，以使其平均值变为0且标准差变为1，即应将其转换为标准正态分布。在R中，可以借助缩放功能轻松完成此操作。查看以下示例，了解其操作方法。
示例
请看以下数据帧：
> set.seed(3665)> x1<-rnorm(20,1,0.35)
> x2<-rnorm(20,50,1.25)
> x3<-rnorm(20,125,10.27)
> x4<-rpois(20,5)
> x5<-runif(20,1,5)
> x6<-rexp(20,1.35)
> df<-data.frame(x1,x2,x3,x4,x5,x6)
> df
输出结果
x1 x2 x3 x4 x5 x61 1.3958185 49.39843 128.5224 3 4.183664 2.33406246
2 1.0467979 48.90103 120.5796 7 3.526731 0.02043217
3 0.9190516 50.74664 110.4765 6 2.145181 0.04268455
4 1.1196425 47.83063 126.3711 9 4.276084 0.87234197
5 1.0033896 51.31879 144.2594 5 3.308073 0.28540083
6 0.7571435 49.92559 109.9660 5 2.349070 0.09613835
7 0.8266129 48.93754 135.5895 3 2.479160 0.15018153
8 1.2786206 50.27384 122.8543 4 4.343062 1.26431542
9 0.8661156 50.36976 122.9482 7 3.517678 0.24045191
10 0.9237285 48.55069 121.6440 4 1.619902 0.72327013
11 0.8191029 49.27937 111.8696 3 4.760655 0.97199973
12 1.2619135 50.91131 129.0021 4 3.355301 1.42184615
13 1.5297983 49.38604 133.4756 1 2.977833 0.50042231
14 0.7858227 47.92899 142.0669 3 3.262058 0.37260602
15 0.5626517 51.22160 107.5586 2 3.194546 0.21176125
16 1.2106700 51.65911 132.4945 3 1.088987 0.78318970
17 1.5351378 48.60769 116.1427 7 3.423079 0.65904040
18 0.4134951 52.09415 125.1567 5 4.309763 0.03658430
19 1.0490230 49.87242 125.6695 6 2.255468 1.32173240
20 0.9521718 50.96409 131.8025 1 4.312514 0.26955446
标准化数据框df的列：
示例
> scale(df$x1)
输出结果
[,1][1,] 1.29357316
[2,] 0.11471258
[3,] -0.31676647
[4,] 0.36075419
[5,] -0.03190435
[6,] -0.86363126
[7,] -0.62898965
[8,] 0.89772243
[9,] -0.49556454
[10,] -0.30096951
[11,] -0.65435564
[12,] 0.84129223
[13,] 1.74610668
[14,] -0.76676371
[15,] -1.52055135
[16,] 0.66821113
[17,] 1.76414130
[18,] -2.02434637
[19,] 0.12222816
[20,] -0.20489902
attr(,"scaled:center")
[1] 1.012835
attr(,"scaled:scale")
[1] 0.2960661
示例
> scale(df$x2)
输出结果
[,1][1,] -0.41047444
[2,] -0.81044801
[3,] 0.67365908
[4,] -1.67118308
[5,] 1.13374554
[6,] 0.01343048
[7,] -0.78108246
[8,] 0.29346832
[9,] 0.37059777
[10,] -1.09216479
[11,] -0.50620790
[12,] 0.80607839
[13,] -0.42043493
[14,] -1.59208513
[15,] 1.05558665
[16,] 1.40740638
[17,] -1.04632509
[18,] 1.75723305
[19,] -0.02932018
[20,] 0.84852032
attr(,"scaled:center")
[1] 49.90889
attr(,"scaled:scale")
[1] 1.243585
示例
> scale(df$x3)
输出结果
[,1][1,] 0.34992259
[2,] -0.42214208
[3,] -1.40419396
[4,] 0.14081369
[5,] 1.87961161
[6,] -1.45381494
[7,] 1.03687396
[8,] -0.20103605
[9,] -0.19190685
[10,] -0.31867565
[11,] -1.26878469
[12,] 0.39654942
[13,] 0.83139424
[14,] 1.66649197
[15,] -1.68782930
[16,] 0.73602324
[17,] -0.85342655
[18,] 0.02276361
[19,] 0.07260960
[20,] 0.66875614
attr(,"scaled:center")
[1] 124.9225
attr(,"scaled:scale")
[1] 10.28772
示例
> scale(df$x4)
输出结果
[,1][1,] -0.6550055
[2,] 1.2164389
[3,] 0.7485778
[4,] 2.1521611
[5,] 0.2807167
[6,] 0.2807167
[7,] -0.6550055
[8,] -0.1871444
[9,] 1.2164389
[10,] -0.1871444
[11,] -0.6550055
[12,] -0.1871444
[13,] -1.5907277
[14,] -0.6550055
[15,] -1.1228666
[16,] -0.6550055
[17,] 1.2164389
[18,] 0.2807167
[19,] 0.7485778
[20,] -1.5907277
attr(,"scaled:center")
[1] 4.4
attr(,"scaled:scale")
[1] 2.137387
示例
> scale(df$x5)
输出结果
[,1][1,] 0.95270619
[2,] 0.29336256
[3,] -1.09325500
[4,] 1.04546450
[5,] 0.07390313
[6,] -0.88861840
[7,] -0.75805159
[8,] 1.11268817
[9,] 0.28427648
[10,] -1.62046133
[11,] 1.53181336
[12,] 0.12130380
[13,] -0.25754850
[14,] 0.02771942
[15,] -0.04004115
[16,] -2.15332455
[17,] 0.18933035
[18,] 1.07926704
[19,] -0.98256346
[20,] 1.08202896
attr(,"scaled:center")
[1] 3.23444
attr(,"scaled:scale")
[1] 0.9963449
示例
> scale(df$x6)
输出结果
[,1][1,] 2.8372424
[2,] -1.0124395
[3,] -0.9754135
[4,] 0.4050652
[5,] -0.5715544
[6,] -0.8864709
[7,] -0.7965477
[8,] 1.0572753
[9,] -0.6463456
[10,] 0.1570224
[11,] 0.5708871
[12,] 1.3193929
[13,] -0.2137772
[14,] -0.4264525
[15,] -0.6940844
[16,] 0.2567235
[17,] 0.0501497
[18,] -0.9855638
[19,] 1.1528122
[20,] -0.5979214
attr(,"scaled:center")
[1] 0.6289008
attr(,"scaled:scale")
[1] 0.6009926
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