采样R数据帧后如何更改行索引？
当我们从R数据帧中抽取随机样本时，样本行的行号与原始数据帧中一样，显然是由于随机化而发生的。但这可能会在进行分析时造成混乱，尤其是在需要使用行的情况下，因此，我们可以将行的索引数从1转换为所选样本中的行数。
示例
请看以下数据帧-
> set.seed(111)> x1<-rnorm(20,1.5)
> x2<-rnorm(20,2.5)
> x3<-rnorm(20,3)
> df1<-data.frame(x1,x2,x3)
> df1
输出结果
x1 x2 x31 1.735220712 2.8616625 1.824274
2 1.169264128 2.8469644 1.878784
3 1.188376176 2.6897365 1.638096
4 -0.802345658 2.3404232 3.481125
5 1.329123955 2.8265492 3.741972
6 1.640278225 3.0982542 3.027825
7 0.002573344 0.6584657 3.331380
8 0.489811581 5.2180556 3.644114
9 0.551524395 2.6912444 5.485662
10 1.006037783 1.1987039 4.959982
11 1.326325872 -0.6132173 3.191663
12 1.093401220 1.5586426 4.552544
13 3.345636264 3.9002588 3.914242
14 1.894054110 0.8795300 3.358625
15 2.297528501 0.2340040 3.175096
16 -0.066665360 3.6629936 2.152732
17 1.414148991 2.3838450 3.978232
18 1.140860519 2.8342560 4.805868
19 0.306391033 1.8791419 3.122915
20 1.864186737 1.1901551 2.870228
从df1创建大小为5的样本-
> df1_sample<-df1[sample(nrow(df1),5),]> df1_sample
输出结果
x1 x2 x318 1.140861 2.834256 4.805868
6 1.640278 3.098254 3.027825
13 3.345636 3.900259 3.914242
5 1.329124 2.826549 3.741972
15 2.297529 0.234004 3.175096
重命名样本中的行的索引数-
> rownames(df1_sample)<-1:nrow(df1_sample)> df1_sample
输出结果
x1 x2 x31 1.140861 2.834256 4.805868
2 1.640278 3.098254 3.027825
3 3.345636 3.900259 3.914242
4 1.329124 2.826549 3.741972
5 2.297529 0.234004 3.175096
让我们看另一个例子-
示例
> y1<-runif(20,2,5)> y2<-runif(20,3,5)
> y3<-runif(20,5,10)
> y4<-runif(20,5,12)
> df2<-data.frame(y1,y2,y3,y4)
> df2
输出结果
y1 y2 y3 y41 2.881213 4.894022 7.797367 6.487594
2 3.052896 3.223898 7.527572 6.695535
3 2.237543 4.127740 9.864026 8.754048
4 4.475907 4.696651 5.403004 6.239423
5 2.792642 4.023536 7.786222 8.992823
6 2.791539 4.333093 9.480036 6.087904
7 2.271143 3.053019 5.539486 8.320935
8 3.382534 3.212921 7.246406 10.091843
9 4.074728 4.390884 6.544056 10.924127
10 4.546881 3.546689 6.164413 11.710035
11 2.738344 4.489939 9.140333 8.211822
12 3.952763 4.490791 5.564392 7.542578
13 4.040586 3.333465 9.420011 11.554599
14 2.313604 4.959709 8.628101 11.193405
15 2.335957 4.189517 9.601667 9.694433
16 2.646964 4.376438 5.614787 10.929413
17 2.390349 3.343716 9.755718 11.017555
18 3.999001 3.083366 8.348515 8.370818
19 3.463324 3.379700 5.425484 7.219430
20 3.059911 4.522844 7.905784 11.420429
> df2_sample<-df2[sample(nrow(df2),7),]> df2_sample
输出结果
y1 y2 y3 y420 3.059911 4.522844 7.905784 11.420429
3 2.237543 4.127740 9.864026 8.754048
10 4.546881 3.546689 6.164413 11.710035
12 3.952763 4.490791 5.564392 7.542578
15 2.335957 4.189517 9.601667 9.694433
18 3.999001 3.083366 8.348515 8.370818
5 2.792642 4.023536 7.786222 8.992823
> rownames(df2_sample)<-1:nrow(df2_sample)> df2_sample
输出结果
y1 y2 y3 y41 3.059911 4.522844 7.905784 11.420429
2 2.237543 4.127740 9.864026 8.754048
3 4.546881 3.546689 6.164413 11.710035
4 3.952763 4.490791 5.564392 7.542578
5 2.335957 4.189517 9.601667 9.694433
6 3.999001 3.083366 8.348515 8.370818
7 2.792642 4.023536 7.786222 8.992823
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