如何根据字符串匹配删除 R 数据框的行?
r programmingserver side programmingprogramming更新于 2025/4/17 1:52:17
通常，我们需要对数据框进行子集化，有时这种子集化是基于字符串的。如果我们有一个字符列或因子列，那么我们可能会将其值作为字符串，我们可以通过删除包含值或部分值的行来对整个数据框进行子集化，例如，我们可以删除 Species 列中包含 set 或 setosa 字的所有行。
示例
考虑下面的数据框 −
Character<-c("Andy","Amy","Carolina","Stone","Sam","Carriph","Selcan","Toni","Andrew","Samuel","Samreen","Erturul","Engjin","Engeline","Andreas","Sofia","Yannis","Salvador","Bahattin","Samsa","Orgopolos","Dragos")
ID<-1:22
df<-data.frame(ID,Character)
df
输出
ID Character 1 1 Andy 2 2 Amy 3 3 Carolina 4 4 Stone 5 5 Sam 6 6 Carriph 7 7 Selcan 8 8 Toni 9 9 Andrew 10 10 Samuel 11 11 Samreen 12 12 Erturul 13 13 Engjin 14 14 Engeline 15 15 Andreas 16 16 Sofia 17 17 Yannis 18 18 Salvador 19 19 Bahattin 20 20 Samsa 21 21 Orgopolos 22 22 Dragos
示例
df[!grepl("An",df$Character),]
输出
ID Character 2 2 Amy 3 3 Carolina 4 4 Stone 5 5 Sam 6 6 Carriph 7 7 Selcan 8 8 Toni 10 10 Samuel 11 11 Samreen 12 12 Erturul 13 13 Engjin 14 14 Engeline 16 16 Sofia 17 17 Yannis 18 18 Salvador 19 19 Bahattin 20 20 Samsa 21 21 Orgopolos 22 22 Dragos
示例
df[!grepl("os",df$Character),]
输出
ID Character 1 1 Andy 2 2 Amy 3 3 Carolina 4 4 Stone 5 5 Sam 6 6 Carriph 7 7 Selcan 8 8 Toni 9 9 Andrew 10 10 Samuel 11 11 Samreen 12 12 Erturul 13 13 Engjin 14 14 Engeline 15 15 Andreas 16 16 Sofia 17 17 Yannis 18 18 Salvador 19 19 Bahattin 20 20 Samsa
示例
df[!grepl("Sam",df$Character),]
输出
ID Character 1 1 Andy 2 2 Amy 3 3 Carolina 4 4 Stone 6 6 Carriph 7 7 Selcan 8 8 Toni 9 9 Andrew 12 12 Erturul 13 13 Engjin 14 14 Engeline 15 15 Andreas 16 16 Sofia 17 17 Yannis 18 18 Salvador 19 19 Bahattin 21 21 Orgopolos 22 22 Dragos
示例
df[!grepl("on",df$Character),]
输出
ID Character 1 1 Andy 2 2 Amy 3 3 Carolina 5 5 Sam 6 6 Carriph 7 7 Selcan 9 9 Andrew 10 10 Samuel 11 11 Samreen 12 12 Erturul 13 13 Engjin 14 14 Engeline 15 15 Andreas 16 16 Sofia 17 17 Yannis 18 18 Salvador 19 19 Bahattin 20 20 Samsa 21 21 Orgopolos 22 22 Dragos
示例
df[!grepl("ra",df$Character),]
输出
ID Character 1 1 Andy 2 2 Amy 3 3 Carolina 4 4 Stone 5 5 Sam 6 6 Carriph 7 7 Selcan 8 8 Toni 9 9 Andrew 10 10 Samuel 11 11 Samreen 12 12 Erturul 13 13 Engjin 14 14 Engeline 15 15 Andreas 16 16 Sofia 17 17 Yannis 18 18 Salvador 19 19 Bahattin 20 20 Samsa 21 21 Orgopolos
让我们看一个使用鸢尾花数据的示例 −
示例
head(iris)
输出
Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa
示例
iris[!grepl("set",iris$Species),]
输出
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 56 5.7 2.8 4.5 1.3 versicolor 57 6.3 3.3 4.7 1.6 versicolor 58 4.9 2.4 3.3 1.0 versicolor 59 6.6 2.9 4.6 1.3 versicolor 60 5.2 2.7 3.9 1.4 versicolor 61 5.0 2.0 3.5 1.0 versicolor 62 5.9 3.0 4.2 1.5 versicolor 63 6.0 2.2 4.0 1.0 versicolor 64 6.1 2.9 4.7 1.4 versicolor 65 5.6 2.9 3.6 1.3 versicolor 66 6.7 3.1 4.4 1.4 versicolor 67 5.6 3.0 4.5 1.5 versicolor 68 5.8 2.7 4.1 1.0 versicolor 69 6.2 2.2 4.5 1.5 versicolor 70 5.6 2.5 3.9 1.1 versicolor 71 5.9 3.2 4.8 1.8 versicolor 72 6.1 2.8 4.0 1.3 versicolor 73 6.3 2.5 4.9 1.5 versicolor 74 6.1 2.8 4.7 1.2 versicolor 75 6.4 2.9 4.3 1.3 versicolor 76 6.6 3.0 4.4 1.4 versicolor 77 6.8 2.8 4.8 1.4 versicolor 78 6.7 3.0 5.0 1.7 versicolor 79 6.0 2.9 4.5 1.5 versicolor 80 5.7 2.6 3.5 1.0 versicolor 81 5.5 2.4 3.8 1.1 versicolor 82 5.5 2.4 3.7 1.0 versicolor 83 5.8 2.7 3.9 1.2 versicolor 84 6.0 2.7 5.1 1.6 versicolor 85 5.4 3.0 4.5 1.5 versicolor 86 6.0 3.4 4.5 1.6 versicolor 87 6.7 3.1 4.7 1.5 versicolor 88 6.3 2.3 4.4 1.3 versicolor 89 5.6 3.0 4.1 1.3 versicolor 90 5.5 2.5 4.0 1.3 versicolor 91 5.5 2.6 4.4 1.2 versicolor 92 6.1 3.0 4.6 1.4 versicolor 93 5.8 2.6 4.0 1.2 versicolor 94 5.0 2.3 3.3 1.0 versicolor 95 5.6 2.7 4.2 1.3 versicolor 96 5.7 3.0 4.2 1.2 versicolor 97 5.7 2.9 4.2 1.3 versicolor 98 6.2 2.9 4.3 1.3 versicolor 99 5.1 2.5 3.0 1.1 versicolor 100 5.7 2.8 4.1 1.3 versicolor 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 106 7.6 3.0 6.6 2.1 virginica 107 4.9 2.5 4.5 1.7 virginica 108 7.3 2.9 6.3 1.8 virginica 109 6.7 2.5 5.8 1.8 virginica 110 7.2 3.6 6.1 2.5 virginica 111 6.5 3.2 5.1 2.0 virginica 112 6.4 2.7 5.3 1.9 virginica 113 6.8 3.0 5.5 2.1 virginica 114 5.7 2.5 5.0 2.0 virginica 115 5.8 2.8 5.1 2.4 virginica 116 6.4 3.2 5.3 2.3 virginica 117 6.5 3.0 5.5 1.8 virginica 118 7.7 3.8 6.7 2.2 virginica 119 7.7 2.6 6.9 2.3 virginica 120 6.0 2.2 5.0 1.5 virginica 121 6.9 3.2 5.7 2.3 virginica 122 5.6 2.8 4.9 2.0 virginica 123 7.7 2.8 6.7 2.0 virginica 124 6.3 2.7 4.9 1.8 virginica 125 6.7 3.3 5.7 2.1 virginica 126 7.2 3.2 6.0 1.8 virginica 127 6.2 2.8 4.8 1.8 virginica 128 6.1 3.0 4.9 1.8 virginica 129 6.4 2.8 5.6 2.1 virginica 130 7.2 3.0 5.8 1.6 virginica 131 7.4 2.8 6.1 1.9 virginica 132 7.9 3.8 6.4 2.0 virginica 133 6.4 2.8 5.6 2.2 virginica 134 6.3 2.8 5.1 1.5 virginica 135 6.1 2.6 5.6 1.4 virginica 136 7.7 3.0 6.1 2.3 virginica 137 6.3 3.4 5.6 2.4 virginica 138 6.4 3.1 5.5 1.8 virginica 139 6.0 3.0 4.8 1.8 virginica 140 6.9 3.1 5.4 2.1 virginica 141 6.7 3.1 5.6 2.4 virginica 142 6.9 3.1 5.1 2.3 virginica 143 5.8 2.7 5.1 1.9 virginica 144 6.8 3.2 5.9 2.3 virginica 145 6.7 3.3 5.7 2.5 virginica 146 6.7 3.0 5.2 2.3 virginica 147 6.3 2.5 5.0 1.9 virginica 148 6.5 3.0 5.2 2.0 virginica 149 6.2 3.4 5.4 2.3 virginica 150 5.9 3.0 5.1 1.8 virginica
