How to delete rows of an R data frame based on string match?

Often, we need to subset our data frame and sometimes this subsetting is based on strings. If we have a character column or a factor column then we might be having its values as a string and we can subset the whole data frame by deleting rows that contain a value or part of a value, for example, we can get rid of all rows that contain set or setosa word in Species column.
Consider the below data frame −
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
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
df[!grepl("An",df$Character),]
ID Character
2 2 Amy
3 3 Carolina
4 4 Stone
5 5 Sam
6 6 Carriph
7 7 Selcan
8 8 Toni
10 10 Samuel
11 11 Samreen
12 12 Erturul
13 13 Engjin
14 14 Engeline
16 16 Sofia
17 17 Yannis
18 18 Salvador
19 19 Bahattin
20 20 Samsa
21 21 Orgopolos
22 22 Dragos
df[!grepl("os",df$Character),]
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
df[!grepl("Sam",df$Character),]
ID Character
1 1 Andy
2 2 Amy
3 3 Carolina
4 4 Stone
6 6 Carriph
7 7 Selcan
8 8 Toni
9 9 Andrew
12 12 Erturul
13 13 Engjin
14 14 Engeline
15 15 Andreas
16 16 Sofia
17 17 Yannis
18 18 Salvador
19 19 Bahattin
21 21 Orgopolos
22 22 Dragos
df[!grepl("on",df$Character),]
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
df[!grepl("ra",df$Character),]
ID Character
1 1 Andy
2 2 Amy
3 3 Carolina
4 4 Stone
5 5 Sam
6 6 Carriph
7 7 Selcan
8 8 Toni
9 9 Andrew
10 10 Samuel
11 11 Samreen
12 12 Erturul
13 13 Engjin
14 14 Engeline
15 15 Andreas
16 16 Sofia
17 17 Yannis
18 18 Salvador
19 19 Bahattin
20 20 Samsa
21 21 Orgopolos
Let’s have a look at an example using iris data −
head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
iris[!grepl("set",iris$Species),]
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