如何根据因子列的组别选取 R 数据框的顶部行?
r programmingserver side programmingprogramming更新于 2025/6/27 17:07:17
我们使用 head 函数来查看 R 数据框中的一些顶部值，但它显示的是整个数据框的顶部值，而不考虑因子列的组别。因此，如果某个特定组中的值数量较多，那么单独使用 head 函数似乎毫无用处，我们必须使用某种方法提取每个组的顶部值。这可以通过使用带有单方括号的 by 函数和 head 函数来实现。
示例
data(iris) str(iris) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ... Top5_Based_on_Species<-by(iris,iris["Species"],head,n=5) Top5_Based_on_Species Species: setosa
输出
Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa ------------------------------------------------------------ Species: versicolor Sepal.Length Sepal.Width Petal.Length Petal.Width Species 51 7.0 3.2 4.7 1.4 versicolor 52 6.4 3.2 4.5 1.5 versicolor 53 6.9 3.1 4.9 1.5 versicolor 54 5.5 2.3 4.0 1.3 versicolor 55 6.5 2.8 4.6 1.5 versicolor ------------------------------------------------------------ Species: virginica Sepal.Length Sepal.Width Petal.Length Petal.Width Species 101 6.3 3.3 6.0 2.5 virginica 102 5.8 2.7 5.1 1.9 virginica 103 7.1 3.0 5.9 2.1 virginica 104 6.3 2.9 5.6 1.8 virginica 105 6.5 3.0 5.8 2.2 virginica
示例
data(ToothGrowth) str(ToothGrowth) 'data.frame': 60 obs. of 3 variables: $ len : num 4.2 11.5 7.3 5.8 6.4 10 11.2 11.2 5.2 7 ... $ supp: Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ... $ dose: num 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ... Top10_Based_on_Supp<-by(ToothGrowth,ToothGrowth["supp"],head,n=10) Top10_Based_on_Supp supp: OJ
输出
len supp dose 31 15.2 OJ 0.5 32 21.5 OJ 0.5 33 17.6 OJ 0.5 34 9.7 OJ 0.5 35 14.5 OJ 0.5 36 10.0 OJ 0.5 37 8.2 OJ 0.5 38 9.4 OJ 0.5 39 16.5 OJ 0.5 40 9.7 OJ 0.5 ------------------------------------------------------------ supp: VC len supp dose 1 4.2 VC 0.5 2 11.5 VC 0.5 3 7.3 VC 0.5 4 5.8 VC 0.5 5 6.4 VC 0.5 6 10.0 VC 0.5 7 11.2 VC 0.5 8 11.2 VC 0.5 9 5.2 VC 0.5 10 7.0 VC 0.5
示例
data(CO2) str(CO2) Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame': 84 obs. of 5 variables: $ Plant : Ord.factor w/ 12 levels "Qn1"<"Qn2"<"Qn3"<..: 1 1 1 1 1 1 1 2 2 2 ... $ Type : Factor w/ 2 levels "Quebec","Mississippi": 1 1 1 1 1 1 1 1 1 1 ... $ Treatment: Factor w/ 2 levels "nonchilled","chilled": 1 1 1 1 1 1 1 1 1 1 ... $ conc : num 95 175 250 350 500 675 1000 95 175 250 ... $ uptake : num 16 30.4 34.8 37.2 35.3 39.2 39.7 13.6 27.3 37.1 ... - attr(*, "formula")=Class 'formula' language uptake ~ conc | Plant .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> - attr(*, "outer")=Class 'formula' language ~Treatment * Type .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> - attr(*, "labels")=List of 2 ..$ x: chr "Ambient carbon dioxide concentration" ..$ y: chr "CO2 uptake rate" - attr(*, "units")=List of 2 ..$ x: chr "(uL/L)" ..$ y: chr "(umol/m^2 s)" Top5_Based_on_Treatment<-by(CO2,CO2["Treatment"],head,n=5) Top5_Based_on_Treatment
输出
Treatment: nonchilled Plant Type Treatment conc uptake 1 Qn1 Quebec nonchilled 95 16.0 2 Qn1 Quebec nonchilled 175 30.4 3 Qn1 Quebec nonchilled 250 34.8 4 Qn1 Quebec nonchilled 350 37.2 5 Qn1 Quebec nonchilled 500 35.3 ------------------------------------------------------------ Treatment: chilled Plant Type Treatment conc uptake 22 Qc1 Quebec chilled 95 14.2 23 Qc1 Quebec chilled 175 24.1 24 Qc1 Quebec chilled 250 30.3 25 Qc1 Quebec chilled 350 34.6 26 Qc1 Quebec chilled 500 32.5
