Useful commands for working with matrices.
Multiple ways to quickly create helper matrices.
matrix(0, 3, 3) mat.or.vec(3,3)
[,1] [,2] [,3] [,1] [,2] [,3]
[1,] 0 0 0 [1,] 0 0 0
[2,] 0 0 0 [2,] 0 0 0
[3,] 0 0 0 [3,] 0 0 0
.row(c(3,3)) .col(c(3,3))
[,1] [,2] [,3] [,1] [,2] [,3]
[1,] 1 1 1 [1,] 1 2 3
[2,] 2 2 2 [2,] 1 2 3
[3,] 3 3 3 [3,] 1 2 3
rbind(1:3, 3:1, 1:3) cbind(1:3, 3:1, 1:3)
[,1] [,2] [,3] [,1] [,2] [,3]
[1,] 1 2 3 [1,] 1 3 1
[2,] 3 2 1 [2,] 2 2 2
[3,] 1 2 3 [3,] 3 1 3
diag(3) outer(1:3, 1:3)
[,1] [,2] [,3] [,1] [,2] [,3]
[1,] 1 0 0 [1,] 1 2 3
[2,] 0 1 0 [2,] 2 4 6
[3,] 0 0 1 [3,] 3 6 9
Matrix can contain various classes. Below is a matrix of data.frames.
mat <- matrix(list(iris, mtcars, USArrests, chickwts), ncol=2)
[,1] [,2]
[1,] data.frame,5 data.frame,4
[2,] data.frame,11 data.frame,2
Element selection in such matrices works based on list behaviour.
mat[[2,2]] mat[[2,2]]
<returns a list> <returns the data.frame>
Subtract column/row means from each column/row.
X - rowMeans(X)[row(X)] X - colMeans(X)[col(X)]
Since matrices are just vectors ordered column-by-column row-wise subtraction can be simplified. The example below will subtract all means from the first column, then repeat this for all the means in the second column, etc. As a result every row will have its corresponding mean subtracted.
X - rowMeans(X)
These methods are general and work with any operations, not just subtraction.
(X - rowMeans(X)) / matrixStats::rowSds(X) # scale each row
(X - colMeans(X)[col(X)]) / matrixStats::colSds(X)[col(X)] # scale each column
Order each row/column of a matrix separately.
matrix(X[order(row(X), X)], nrow=nrow(X), byrow=TRUE) # by row
matrix(X[order(col(X), X)], nrow=nrow(X)) # by column
Missing values are placed last.
na.last argument in the order() function can be used to control this behaviour.
matrix(X[order(row(X), X, na.last=FALSE)], nrow=nrow(X), byrow=TRUE)
matrix(X[order(col(X), X, na.last=FALSE)], nrow=nrow(X))
This method is a lot faster than using apply().