How to standardize selected columns in data.table object in R?

To standardize selected columns in data.table object in R, we can follow the below steps −

  • First of all, create a data.table object.

  • Then, use scale function and cbind function with subsetting to standardize selected columns.

Example

Create the data.table object

Let’s create a data.table object as shown below −

library(data.table)
var1<-sample(1:10,25,replace=TRUE)
var2<-sample(1:10,25,replace=TRUE)
var3<-sample(1:10,25,replace=TRUE)
DT<-data.table(var1,var2,var3)
DT

Output

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

    var1 var2 var3
1:   8   10    3
2:  10    7    2
3:   2    8    2
4:   1    8    5
5:   5    7    6
6:   2    7   10
7:   7    6    2
8:   7    3    8
9:   4    2    9
10:  6    2   10
11:  8    8    9
12:  8    9    7
13:  1    9   10
14:  7    8    1
15:  4    6    5
16:  9    6    1
17: 10    2    5
18: 10    5    6
19:  5    6    2
20:  4    6   10
21:  5    9   10
22:  7    6    4
23:  3    5    4
24:  7    2    6
25:  1    2    3
    var1 var2 var3

Standardize selected columns

Using scale function and cbind function with subsetting to standardize columns 2 and 3 in data.table object DT −

library(data.table)
var1<-sample(1:10,25,replace=TRUE)
var2<-sample(1:10,25,replace=TRUE)
var3<-sample(1:10,25,replace=TRUE)
DT<-data.table(var1,var2,var3)
DT[]<-cbind(DT[,1],scale(DT[,2:3]))
DT

Output

     var1    var2        var3
1:   8   1.60028763  -0.8187877
2:  10   0.41195523  -1.1337060
3:   2   0.80806603  -1.1337060
4:   1   0.80806603  -0.1889510
5:   5   0.41195523   0.1259673
6:   2   0.41195523   1.3856406
7:   7   0.01584443  -1.1337060
8:   7  -1.17248797   0.7558040
9:   4  -1.56859877   1.0707223
10:  6  -1.56859877   1.3856406
11:  8   0.80806603   1.0707223
12:  8   1.20417683   0.4408857
13:  1   1.20417683   1.3856406
14:  7   0.80806603  -1.4486243
15:  4   0.01584443  -0.1889510
16:  9   0.01584443  -1.4486243
17: 10  -1.56859877  -0.1889510
18: 10  -0.38026637   0.1259673
19:  5   0.01584443  -1.1337060
20:  4   0.01584443   1.3856406
21:  5   1.20417683   1.3856406
22:  7   0.01584443  -0.5038693
23:  3  -0.38026637  -0.5038693
24:  7  -1.56859877   0.1259673
25:  1  -1.56859877  -0.8187877
    var1    var2          var3

Updated on: 08-Nov-2021

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