# matricks 0.8.2 available on CRAN

**krzjoa**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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`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!

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**krzjoa**.

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