How to find the row variance of columns having same name in R matrix?

To find the row variance of columns having same name in R matrix, we can follow the below steps −

• First of all, create a matrix with some columns having same name.

• Then, use tapply along with colnames and var function to find the row variance of columns having same name.

Example

Create the matrix

Let’s create a matrix as shown below −

M<-matrix(rpois(100,2),ncol=4) colnames(M)<-c("x1","x1","x2","x2") M

Output

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

 x1 x1 x2 x2 [1,] 1 3 4 0 [2,] 1 0 2 4 [3,] 3 2 2 2 [4,] 2 1 1 0 [5,] 2 3 1 2 [6,] 0 1 3 2 [7,] 2 3 3 0 [8,] 5 2 3 1 [9,] 1 3 1 0 [10,] 1 0 2 2 [11,] 2 2 1 0 [12,] 4 2 0 0 [13,] 2 4 2 3 [14,] 0 2 2 1 [15,] 2 4 1 2 [16,] 2 1 1 2 [17,] 2 1 1 3 [18,] 0 0 1 3 [19,] 4 1 3 3 [20,] 1 3 2 0 [21,] 2 1 4 2 [22,] 1 3 3 2 [23,] 2 0 0 1 [24,] 2 1 2 1 [25,] 3 1 2 1

Find the row variance of columns having same name

Using tapply along with colnames and var function to find the row variance of columns having same name in matrix M −

M<-matrix(rpois(100,2),ncol=4) colnames(M)<-c("x1","x1","x2","x2") t(apply(M,1, function(x) tapply(x,colnames(M),var)))

Output

 x1 x2 [1,] 2.0 8.0 [2,] 0.5 2.0 [3,] 0.5 0.0 [4,] 0.5 0.5 [5,] 0.5 0.5 [6,] 0.5 0.5 [7,] 0.5 4.5 [8,] 4.5 2.0 [9,] 2.0 0.5 [10,] 0.5 0.0 [11,] 0.0 0.5 [12,] 2.0 0.0 [13,] 2.0 0.5 [14,] 2.0 0.5 [15,] 2.0 0.5 [16,] 0.5 0.5 [17,] 0.5 2.0 [18,] 0.0 2.0 [19,] 4.5 0.0 [20,] 2.0 2.0 [21,] 0.5 2.0 [22,] 2.0 0.5 [23,] 2.0 0.5 [24,] 0.5 0.5 [25,] 2.0 0.5