matricks package in 0.8.2 version has been released on CRAN! In this post I will present you, what are advantages of using matricks and how you can use it.

Creating matrices

The main function the package started with is m. It’s a smart shortcut for creating matrices, especially usefull if you want to define a matrix by enumerating all the elements row-by-row. Typically, if you want to create a matrix in R, you can do it using base function called matrix.

matrix(c(3 ,4, 7,
         5, 8, 0,
         9, 2, 1), nrow = 3, byrow = TRUE)

##      [,1] [,2] [,3]
## [1,]    3    4    7
## [2,]    5    8    0
## [3,]    9    2    1

Although it’s a very simple opeartion, the funtion call doesn’t look tidy. Alternaively, we can use tibble with its frame_matrix function, defining column names with formulae first.

library(tibble)
frame_matrix(~ c1, ~ c2, ~ c3,
                3,    4,    7,
                5,    8,    0,
                9,    2,    1)

##      c1 c2 c3
## [1,]  3  4  7
## [2,]  5  8  0
## [3,]  9  2  1

However, it’s still not a such powerfull tool as matricks::m function is. Let’s see an example.

library(matricks)
m(3 ,4, 7|
  5, 8, 0|
  9, 2, 1)

##      [,1] [,2] [,3]
## [1,]    3    4    7
## [2,]    5    8    0
## [3,]    9    2    1

As simple as that! We join following rows using | operator. m function is very flexible and offers you much more than before mentioned ones.

m(1:3 | 4, 6, 7 | 2, 1, 4)

##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    6    7
## [3,]    2    1    4

And here and example with bindig multiple matrices together:

mat1   <- diag(1, 3, 3)
mat2  <- antidiag(1, 3, 3) * 3
m(mat1, mat2|
  mat2, mat1)

##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]    1    0    0    0    0    3
## [2,]    0    1    0    0    3    0
## [3,]    0    0    1    3    0    0
## [4,]    0    0    3    1    0    0
## [5,]    0    3    0    0    1    0
## [6,]    3    0    0    0    0    1

By the way, antidiag function can be found in the matricks package too.

Setting & accessing values

These code

mat <- matrix(0, 3, 3)
mat[1, 2] <- 0.3
mat[2, 3] <- 7
mat[3, 1] <- 13
mat[2, 2] <- 0.5
mat

##      [,1] [,2] [,3]
## [1,]    0  0.3    0
## [2,]    0  0.5    7
## [3,]   13  0.0    0

can be replaced with:

mat <- matrix(0, 3, 3)
mat <- set_values(mat,
                  c(1, 2) ~ 0.3,
                  c(2, 3) ~ 7,
                  c(3, 1) ~ 13,
                  c(2, 2) ~ 0.5)
mat

##      [,1] [,2] [,3]
## [1,]    0  0.3    0
## [2,]    0  0.5    7
## [3,]   13  0.0    0

In some cases, traditional way we access a matrix element in R may be inconvenient. Consider situation shown below:

sample.matrix <- matrix(1, 3, 3)
matrix.indices <- list(c(1, 1), c(1, 2),
                       c(1, 3), c(2, 2),
                       c(3, 1), c(3, 3))

for (idx in matrix.indices) {
  sample.matrix[idx[1], idx[2]] <- sample.matrix[idx[1], idx[2]] + 2
}

sample.matrix

##      [,1] [,2] [,3]
## [1,]    3    3    3
## [2,]    1    3    1
## [3,]    3    1    3

It can be expressed conciser using matrix at function.

sample.matrix <- matrix(1, 3, 3)
matrix.indices <- list(c(1, 1), c(1, 2),
                       c(1, 3), c(2, 2),
                       c(3, 1), c(3, 3))

for (idx in matrix.indices) {
  at(sample.matrix, idx) <- at(sample.matrix, idx) + 2
}
sample.matrix

##      [,1] [,2] [,3]
## [1,]    3    3    3
## [2,]    1    3    1
## [3,]    3    1    3

Plotting matrix

matrix objects haven’t had good automatized plotting function until now. Let’s create and plot a sample matrix of random values.

rmat <- runifm(3, 3)
print(rmat)

##           [,1]      [,2]      [,3]
## [1,] 0.3248890 0.1024049 0.3295454
## [2,] 0.8077164 0.7267801 0.1116789
## [3,] 0.4406909 0.4703106 0.7047498

plot(rmat)

And here the same using a matrix of random boolean values (rboolm).

set.seed(7)
rmat <- rboolm(3, 3)
print(rmat)

##       [,1]  [,2]  [,3]
## [1,] FALSE  TRUE  TRUE
## [2,]  TRUE  TRUE FALSE
## [3,]  TRUE FALSE  TRUE

plot(rmat)

Operators

matricks contains a family of operators, which allows you to perform column-/row-wise operation (addition/subtraction/multiplication/division) between matrix and vector.

mat <- m(1, 2, 3|
         4, 5, 6|
         7, 8, 9)
mat

##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6
## [3,]    7    8    9

vec <- v(1:3)
vec

##      [,1]
## [1,]    1
## [2,]    2
## [3,]    3

If we try to do a column-wise multiplication, we ecounter a problem.

mat * vec

## Error in mat * vec: niezgodne tablice

We can bypass this error using %m% function. It does what we want!

mat %m% vec

##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    8   10   12
## [3,]   21   24   27

There are also several other operators available.

mat %d% vec

##          [,1]     [,2] [,3]
## [1,] 1.000000 2.000000    3
## [2,] 2.000000 2.500000    3
## [3,] 2.333333 2.666667    3

mat %+% vec

##      [,1] [,2] [,3]
## [1,]    2    3    4
## [2,]    6    7    8
## [3,]   10   11   12

mat %-% vec

##      [,1] [,2] [,3]
## [1,]    0    1    2
## [2,]    2    3    4
## [3,]    4    5    6

I encourage you to familiarize with matricks. Visit matrix documentation site and learn more!