Multiply Corresponding Values from Two Data Table Objects in R

To multiply corresponding values from two data.table objects in R, we can follow the below steps −

• First of all, create two data.table objects.

• Then, use mapply function to multiply corresponding values from those two data.table objects.

Example

Create the first data.table object

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

library(data.table)
x1<-sample(1:50,25)
x2<-sample(1:50,25)
x3<-sample(1:50,25)
DT1<-data.table(x1,x2,x3)
DT1

Output

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

   x1 x2 x3
1:  27 39 33
2:  44 24 36
3:  19 50 40
4:  38 20 42
5:  33 29 26
6:  16 42 41
7:  21  6 22
8:  42 47  5
9:   8 33 34
10:  6 13 24
11: 12  4 29
12:  7 11 48
13: 15 15 27
14:  9 40  9
15: 46 34 17
16: 26 14 21
17: 20 19  1
18: 22 27 13
19: 36 16 23
20: 28 26  6
21: 37 41 46
22: 24 46  2
23: 49 32 10
24: 17 35 19
25: 47 31  3
    x1 x2 x3

Create the first data.table object

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

y1<-sample(1:50,25)
y2<-sample(1:50,25)
y3<-sample(1:50,25)
DT2<-data.table(y1,y2,y3)
DT2

Output

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

    y1 y2 y3
1:  35 45 30
2:  28 50 10
3:  33 35  8
4:  48 15 26
5:  43 39 37
6:  25 48 32
7:  15 40 44
8:  38 25 12
9:  13 33 16
10:  1 37 14
11: 23 31 13
12: 44  4 46
13: 40 22 15
14: 47 23 31
15: 29 18 48
16: 49 26 17
17: 42 30  2
18: 32 38  4
19: 22 29 28
20: 24 16 41
21: 19 11 42
22: 36 34  9
23: 14  6  1
24: 18 14 23
25: 31 19 11
   y1 y2 y3

Multiply corresponding values from two data.table objects

library(data.table)
x1<-sample(1:50,25)
x2<-sample(1:50,25)
x3<-sample(1:50,25)
DT1<-data.table(x1,x2,x3)
y1<-sample(1:50,25)
y2<-sample(1:50,25)
y3<-sample(1:50,25)
DT2<-data.table(y1,y2,y3)
mapply(`*`,DT1,DT2)

Output

       1    x2    x3
[1,]  945  1755   990
[2,]  1232 1200   360
[3,]  627  1750   320
[4,]  1824  300  1092
[5,]  1419 1131   962
[6,]  400  2016  1312
[7,]  315   240   968
[8,]  1596 1175    60
[9,]  104  1089   544
[10,]  6    481   336
[11,] 276   124   377
[12,] 308    44  2208
[13,] 600   330   405
[14,] 423   920   279
[15,] 1334  612   816
[16,] 1274  364   357
[17,] 840   570     2
[18,] 704  1026    52
[19,] 792   464   644
[20,] 672   416   246
[21,] 703   451  1932
[22,] 864  1564    18
[23,] 686   192    10
[24,] 306   490   437
[25,] 1457  589    33