How to subset rows of an R data frame using grepl function?

The grepl function in R search for matches to argument pattern within each element of a character vector or column of an R data frame. If we want to subset rows of an R data frame using grepl then subsetting with single-square brackets and grepl can be used by accessing the column that contains character values.

Example1

Consider the below data frame:

Live Demo

> x1<-sample(c("A","B","C"),20,replace=TRUE) > y1<-rnorm(20,1,0.24) > z1<-rpois(20,2) > df1<-data.frame(x1,y1,z1) > df1

Output

 x1 y1 z1 1 A 0.8833979 5 2 B 0.5400075 1 3 C 0.6923827 3 4 B 1.5069186 2 5 B 0.8190962 2 6 B 0.8296171 1 7 B 1.2793876 4 8 B 1.1401782 2 9 C 1.5187263 0 10 C 0.6187501 2 11 B 1.3837516 0 12 C 0.8790544 0 13 A 0.7818624 3 14 B 0.8659361 2 15 B 0.9503166 2 16 A 0.8711020 2 17 B 1.0646814 2 18 A 1.2973144 1 19 C 0.9172171 2 20 B 0.7062629 3

Subsetting df1 by excluding A in x1:

Example

> df1[!grepl("A",df1$x1),]

Output

 x1 y1 z1 2 B 0.5400075 1 3 C 0.6923827 3 4 B 1.5069186 2 5 B 0.8190962 2 6 B 0.8296171 1 7 B 1.2793876 4 8 B 1.1401782 2 9 C 1.5187263 0 10 C 0.6187501 2 11 B 1.3837516 0 12 C 0.8790544 0 14 B 0.8659361 2 15 B 0.9503166 2 17 B 1.0646814 2 19 C 0.9172171 2 20 B 0.7062629 3

Example2

Live Demo

> x2<-sample(c("India","China","France"),20,replace=TRUE) > y2<-rexp(20,0.335) > df2<-data.frame(x2,y2) > df2

Output

 x2 y2 1 India 2.91693551 2 India 5.86599500 3 China 3.41872121 4 India 6.82404548 5 France 4.26003369 6 China 6.31902445 7 China 2.67848516 8 France 3.20830803 9 India 0.01151151 10 India 2.04166415 11 China 1.72607765 12 China 2.31852068 13 India 1.59578792 14 France 1.06253867 15 China 1.44092496 16 China 2.89259111 17 China 0.16299576 18 France 3.37298728 19 India 0.94687404 20 France 1.26557174

Subsetting df2 by excluding France in x2:

Example

> df2[!grepl("France",df2$x2),]

Output

 x2 y2 1 India 2.91693551 2 India 5.86599500 3 China 3.41872121 4 India 6.82404548 6 China 6.31902445 7 China 2.67848516 9 India 0.01151151 10 India 2.04166415 11 China 1.72607765 12 China 2.31852068 13 India 1.59578792 15 China 1.44092496 16 China 2.89259111 17 China 0.16299576 19 India 0.94687404