Create Column of log(1+x) in Data Frames Stored in R List

To create a column of log(1+x) 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 log(1+x) 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(1:50,25))
df2<-data.frame(x=sample(1:50,25))
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   1
2  49
3  41
4  29
5  39
6  25
7   5
8  35
9  33
10 18
11 13
12 11
13 44
14 27
15 46
16  4
17 17
18 34
19 42
20  6
21 50
22 28
23  2
24 43
25 32
[[2]]
   x
1  19
2  18
3  21
4  38
5  46
6  29
7  22
8  35
9  16
10  7
11 23
12 28
13 15
14 11
15 42
16  2
17 43
18 31
19 34
20  1
21 47
22 14
23 24
24 12
25  3

Create a column of log(1+x) in data frames stored in the list

Using lapply function to create a column of log(1+x) in data frames df1 and df2 stored in the list called List as shown below −

df1<-data.frame(x=sample(1:50,25))
df2<-data.frame(x=sample(1:50,25))
List<-list(df1,df2)
lapply(List,function(x) {
+ x$NaturalLog1PlusX<-log1p(x$x)
+ return(x)
+ })

Output

     [[1]]
   x  NaturalLog1PlusX
1  1   0.6931472
2 49   3.9120230
3 41   3.7376696
4 29   3.4011974
5 39   3.6888795
6 25   3.2580965
7 5    1.7917595
8 35   3.5835189
9 33   3.5263605
10 18  2.9444390
11 13  2.6390573
12 11  2.4849066
13 44  3.8066625
14 27  3.3322045
15 46  3.8501476
16 4   1.6094379
17 17  2.8903718
18 34  3.5553481
19 42  3.7612001
20 6   1.9459101
21 50  3.9318256
22 28  3.3672958
23 2   1.0986123
24 43  3.7841896
25 32  3.4965076
 [[2]]
   x    NaturalLog1PlusX
1  19   2.9957323
2  18   2.9444390
3  21   3.0910425
4  38   3.6635616
5  46   3.8501476
6  29   3.4011974
7  22   3.1354942
8  35   3.5835189
9  16   2.8332133
10  7   2.0794415
11 23   3.1780538
12 28   3.3672958
13 15   2.7725887
14 11   2.4849066
15 42   3.7612001
16  2   1.0986123
17 43   3.7841896
18 31   3.4657359
19 34   3.5553481
20  1   0.6931472
21 47   3.8712010
22 14   2.7080502
23 24   3.2188758
24 12   2.5649494
25  3   1.3862944