How to create a column with the serial number of values in character column of an R data frame?

A group column in an R data frame have duplicate values and we might want to create a column with the serial number based on the values such as first value of the first group gets 1, the same value gets 2 when occurred second time in the same column and so on. This can be done by using ave function as shown in the below examples.

Example

Consider the below data frame −

 Live Demo

S.No<-1:20
Group<-sample(LETTERS[1:4],20,replace=TRUE)
df1<-data.frame(S.No,Group)
df1

Output

S.No Group
1  1 B
2  2 A
3  3 C
4  4 C
5  5 B
6  6 C
7  7 C
8  8 C
9  9 D
10 10 D
11 11 C
12 12 C
13 13 A
14 14 B
15 15 A
16 16 D
17 17 C
18 18 B
19 19 A
20 20 C

Creating a column for serial numbers of group values in df1 −

Example

df1$GroupWise_SerialNo<-with(df1,ave(as.character(Group),Group,FUN=seq_along))
df1

Output

S.No Group GroupWise_SerialNo
1  1    B    1
2  2    A    1
3  3    C    1  
4  4    C    2
5  5    B    2
6  6    C    3
7  7    C    4
8  8    C    5
9  9    D    1
10 10    D    2
11 11    C    6
12 12    C    7
13 13    A    2
14 14    B    3
15 15    A    3
16 16    D    3
17 17    C    8
18 18    B    4
19 19    A    4
20 20    C    9

Example

 Live Demo

ID<-1:20
Sample<-sample(c("India","China","UK"),20,replace=TRUE)
df2<-data.frame(ID,Sample)
df2

Output

   ID Sample
1  1 India
2  2 China
3  3 China
4  4 UK
5  5 China
6  6 UK
7  7 India
8  8 India
9  9 UK
10 10 India
11 11 UK
12 12 India
13 13 UK
14 14 UK
15 15 UK
16 16 India
17 17 India
18 18 UK
19 19 UK
20 20 China

Creating a column for serial numbers of group values in df2 −

Example

df2$SampleWise_Numbering<-
with(df2,ave(as.character(Sample),Sample,FUN=seq_along))
df2

Output

   ID Sample SampleWise_Numbering
1  1 India    1
2  2 China    1
3  3 China    2
4  4 UK       1
5  5 China    3
6  6 UK       2
7  7 India    2
8  8 India    3
9  9 UK       3
10 10 India    4
11 11 UK       4
12 12 India    5
13 13 UK       5
14 14 UK       6
15 15 UK       7
16 16 India    6
17 17 India    7
18 18 UK       8
19 19 UK       9
20 20 China    4