How to create a correlation matrix by a categorical column in data.table object in R?

To create a correlation matrix by a categorical column in data.table object in R, we can follow the below steps −

• First of all, create a data.table object.
• Then, find the correlation matrix by splitting the object based on categorical column.

Create the data.table object

Loading data.table package and creating a data.table object −

library(data.table) x<-sample(1:50,25) y<-sample(1:50,25) z<-sample(1:50,25) Group<-sample(LETTERS[1:4],25,replace=TRUE) DT<-data.table(x,y,z,Group) DT

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

    x   y z  Group 1:  15  7 3  C 2:  11  1 31 B 3:  2  16 15 A 4:  33 42 49 B 5:  27 39 19 A 6:  16 21 1  D 7:  18 22 37 A 8:  42 6  35 D 9:  30 40 38 D 10: 8  17 26 C 11: 34 10 41 B 12: 47 33 13 C 13: 7  5  8  A 14: 26 26 43 D 15: 3  41 24 D 16: 31 23 9  B 17: 40 27 32 B 18: 25 30 21 A 19: 5  8  47 D 20: 6  49 17 A 21: 46 3  34 C 22: 21 38 48 A 23: 48 50 4  A 24: 19 36 36 B 25: 39 4  50 C     x  y z Group

Create the correlation matrix by a categorical column

Using split function with lapply to create the correlation matrix for the data in DT by Group column −

library(data.table) x<-sample(1:50,25) y<-sample(1:50,25) z<-sample(1:50,25) Group<-sample(LETTERS[1:4],25,replace=TRUE) DT<-data.table(x,y,z,Group) lapply(split(DT[,1:3],DT$Group),cor)

Output

$A       x          y          z x 1.0000000 0.58114264 -0.10587701 y 0.5811426 1.00000000 0.03787179 z -0.1058770 0.03787179 1.00000000 $B       x          y          z x 1.00000000 0.3848310 0.06995891 y 0.38483099 1.0000000 0.26711858 z 0.06995891 0.2671186 1.00000000 $C       x          y       z x 1.0000000 0.1111837 0.3542485 y 0.1111837 1.0000000 -0.4556856 z 0.3542485 -0.4556856 1.0000000 $D       x          y          z x 1.0000000 -0.2276999 0.1791530 y -0.2276999 1.0000000 -0.1737857 z 0.1791530 -0.1737857 1.0000000