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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
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