# How to create a column of natural log in data frames stored in R list?

To create a column of natural log in data frames stored in R list, we can follow the below steps −

• First of all, create a list of data frames.

• Then, use lapply function to create a column of natural log in data frames stored in the list.

## Example

#### Create the list of data frames

Using data.frame function to create data frames and list function to create the list of those data frames −

df1<-data.frame(x=sample(1:20,25,replace=TRUE))
df2<-data.frame(x=sample(1:20,25,replace=TRUE))
List<-list(df1,df2)
List

## Output

On executing, the above script generates the below output(this output will vary on your system due to randomization) −

[[1]]
x
1   7
2   3
3  20
4  17
5   7
6   4
7  10
8  18
9  17
10 11
11  7
12  9
13  7
14  7
15 10
16 20
17  7
18 15
19  4
20 14
21 13
22 15
23  9
24  9
25 11
[[2]]
x
1   7
2   4
3   4
4  18
5  13
6  19
7  15
8  11
9  20
10 18
11 19
12 20
13 17
14 12
15  6
16 10
17  4
18 12
19  9
20 11
21 14
22  6
23 20
24 19
25 11

Create a column of natural log in data frames stored in the list

Using lapply function to create a column of natural log in data frames df1 and df2 stored in the list called List as shown below −

df1<-data.frame(x=sample(1:20,25,replace=TRUE))
df2<-data.frame(x=sample(1:20,25,replace=TRUE))
List<-list(df1,df2)
lapply(List,function(x) {
+ x$NaturalLog<-log(x$x)
+ return(x)
+ })

## Output

[[1]]
x NaturalLog
1   7 1.945910
2   3 1.098612
3  20 2.995732
4  17 2.833213
5   7 1.945910
6   4 1.386294
7  10 2.302585
8  18 2.890372
9  17 2.833213
10 11 2.397895
11  7 1.945910
12  9 2.197225
13  7 1.945910
14  7 1.945910
15 10 2.302585
16 20 2.995732
17  7 1.945910
18 15 2.708050
19  4 1.386294
20 14 2.639057
21 13 2.564949
22 15 2.708050
23  9 2.197225
24  9 2.197225
25 11 2.397895
[[2]]
x NaturalLog
1   7 1.945910
2   4 1.386294
3   4 1.386294
4  18 2.890372
5  13 2.564949
6  19 2.944439
7  15 2.708050
8  11 2.397895
9  20 2.995732
10 18 2.890372
11 19 2.944439
12 20 2.995732
13 17 2.833213
14 12 2.484907
15  6 1.791759
16 10 2.302585
17  4 1.386294
18 12 2.484907
19  9 2.197225
20 11 2.397895
21 14 2.639057
22  6 1.791759
23 20 2.995732
24 19 2.944439
25 11 2.397895