| Type: | Package |
| Title: | Useful Tricks for Matrix Manipulation |
| Version: | 0.8.2 |
| Description: | Provides functions, which make matrix creation conciser (such as the core package's function m() for rowwise matrix definition or runifm() for random value matrices). Allows to set multiple matrix values at once, by using list of formulae. Provides additional matrix operators and dedicated plotting function. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| BugReports: | https://github.com/krzjoa/matricks/issues |
| URL: | https://github.com/krzjoa/matricks, https://krzjoa.github.io/matricks/ |
| Suggests: | testthat (≥ 2.1.0), knitr, rmarkdown, covr |
| RoxygenNote: | 6.1.1 |
| LinkingTo: | Rcpp |
| Imports: | Rcpp, rlang, ggplot2, reshape2 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | yes |
| Packaged: | 2020-02-12 22:40:10 UTC; krzysztof |
| Author: | Krzysztof Joachimiak
 [aut, cre] |
| Maintainer: | Krzysztof Joachimiak <joachimiak.krzysztof@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2020-02-23 11:40:02 UTC |
Matrix antidiagonals
Description
Extract or replace the antidiagonal of a matrix, or construct a antidiagonal matrix.
Usage
antidiag(x = as.numeric(c(1)), nrow = NULL, ncol = NULL)
antidiag(x) <- value
Arguments
x |
matrix, vector or 1D array, or missing. |
nrow |
number of rows (optional; when x is not a matrix) |
ncol |
number of columns (optional; when x is not a matrix) |
value |
either a single value or a vector of length equal to that of the current antidiagonal. Should be of a mode which can be coerced to that of x. |
Examples
# Extracting antidiag
antidiag(diag(3))
# Creating antidiagonal matrix
antidiag(7, 3, 3)
antidiag(1:5, 3, 3)
# Assigning antidiagonal
mat <- matrix(0, 3, 3)
antidiag(mat) <- c(3, 4, 5)
mat
Set or get matrix value at index vector
Description
This function allows to access matrix values by passing indices as vector
Usage
at(mat, idx)
at(mat, idx) <- value
Arguments
mat |
matrix |
idx |
two-element integer vector |
value |
a value to be assign at index |
Value
'at' function: value from matrix at index idx
Examples
mat <- matrix(0, 3, 3)
idx <- c(1, 2)
# Typically, given matrix and row-column indices as two-element vector, we should do it like this:
mat[idx[1], idx[2]]
mat[idx[1], idx[2]] <- 8
# Using `at`, we can do it simplier!
at(mat, idx)
at(mat, idx) <- 7
mat
at(mat, idx)
Bind vector, single values and matrices
Description
This functions works very similar to well-known base 'cbind' or 'rbind' function. However, there is one big difference between these functions. If you pass a vector, each value will be get individually.
Usage
col_bind(...)
row_bind(...)
Arguments
... |
single values, vectors, matrices or data.frames |
Value
a matrix being a product of matrix/vector/values binding
Examples
# `col_bind` vs `cbind`
cbind(1,2,3,4,5)
col_bind(1,2,3,4,5)
cbind(1:5)
col_bind(1:5)
cbind(matrix(3, 3, 3), 0.33, 4:7)
col_bind(matrix(3, 3, 3), 0.33, 4:7)
# `row_bind` vs `rbind`
rbind(1,2,3,4,5)
row_bind(1,2,3,4,5)
rbind(1:5)
row_bind(1:5)
rbind(matrix(3, 3, 3), 0.33, 4:7)
row_bind(matrix(3, 3, 3), 0.33, 4:7)
Is idx possible in given matrix?
Description
Is idx possible in given matrix?
Usage
is_idx_possible(mat, idx)
Arguments
mat |
matrix |
idx |
two-element vector |
Examples
is_idx_possible(matrix(0, 3, 3), c(4, 5))
is_idx_possible(matrix(0, 3, 3), c(3, 2))
A shortcut to create matrix defining rows
Description
One of the main functionalities of the package. It is an alternative to standard way we define matrices in R.
Usage
m(...)
Arguments
... |
Single values, vectors, matrices and '|' as special symbol which breaks input on the rows. |
Value
matrix with defines elements
Examples
# Typically, we define matrices like this:
x <- matrix(c(1, 2, 3,
4, 5, 6,
7, 8, 9), nrow=3, byrow=TRUE)
x
# However, this way of ceating matices seems to be
# a little bit clunky. Using `matricks`, we can do
# it in more staightforward way dividing our input
# into rows by using special symbol `|`
x <- m(1, 2, 3|
4, 5, 6|
7, 8, 9)
x
# Moreover, we can pass to the `m` function
# whole sequences or even matrices.
x <- m(1:5 | 6:10 | 11:15 )
x
# We can combine multiple matrices into one
m(diag(3), diag(3) * 3|
diag(3) * 3, diag(3) )
Get available marix indices
Description
Get available marix indices
Usage
matrix_idx(mat, n.row = NULL, n.col = NULL, mask = NULL)
Arguments
mat |
matrix |
n.row |
number of rows; default: NULL |
n.col |
number of columns; default: NULL |
mask |
logical matrix; default: NULL |
Examples
T <- TRUE; F <- FALSE
mat <- matrix(0, 3, 3)
mask <- m(T, T, F | T, F, T | F, F, T)
# All poss
matrix_idx(mat)
matrix_idx(mat, mask = mask)
matrix_idx(mask = mask)
Get all indices in neighbourhood
Description
Get all indices in neighbourhood
Usage
neighbour_idx(mat, idx, mask = NULL, diagonal = TRUE,
include.idx = FALSE)
Arguments
mat |
matrix or data.frame |
idx |
two-element vector |
mask |
logical matrix; optional |
diagonal |
include diagonal neighbours |
include.idx |
include current index |
Examples
mat <- matrix(0, 3, 3)
neighbour_idx(mat, c(1, 2))
neighbour_idx(mat, c(1, 2), diagonal = FALSE)
neighbour_idx(mat, c(1, 2), diagonal = FALSE, include.idx = TRUE)
# With mask
mat <- matrix(0, 3, 4)
mask <- m(FALSE, FALSE, TRUE, TRUE |
FALSE, FALSE, FALSE, FALSE |
TRUE, TRUE, FALSE, TRUE)
neighbour_idx(mat, c(1, 2), mask = mask)
Create matrix of lists, where each one contains list of neighbour field coordinates
Description
Create matrix of lists, where each one contains list of neighbour field coordinates
Usage
neighbour_idx_matrix(mat, mask = NULL, diagonal = TRUE,
random.select = NULL)
Arguments
mat |
matrix |
mask |
logical matrix. Its dimensions must be identical with dimensions of mat |
diagonal |
logical. get diagonal neighbours |
random.select |
select one random neighbour |
Examples
T <- TRUE; F <- FALSE
mat <- matrix(0, 3, 3)
mask <- m(T, T, F | T, F, T | F, F, T)
nimat <- neighbour_idx_matrix(mat, mask, diagonal = TRUE)
neighbour_idx_matrix(mat, mask, diagonal = TRUE, random.select = 1)
Binary operations on matrices/vectors
Description
This operator allows to do elementwise operation of two algebraic object i.e. matrices/vectors. There is one required condition to perform such operation: at least one domension values from both objects must be the same
Usage
a %m% b
a %d% b
a %-% b
a %+% b
Arguments
a |
matrix/vector |
b |
matrix/vector |
Value
Matrix/vector
Examples
# Multiply
m(1, 2, 3 | 4, 5, 6 | 7, 8, 9) %m% v(5,4,3)
# Divide
m(1, 2, 3 | 4, 5, 6 | 7, 8, 9) %d% v(5,4,3)
# Add
m(1, 2, 3 | 4, 5, 6 | 7, 8, 9) %+% v(5,4,3)
# Subtract
m(1, 2, 3 | 4, 5, 6 | 7, 8, 9) %-% v(5,4,3)
Plot a matrix
Description
This function allows us to plot matrices easily
Usage
plot_matrix(x, ...)
## S3 method for class 'matrix'
plot(x, ...)
Arguments
x |
a matrix |
... |
for S3 generic API consistency; does nothing |
Value
a ggplot object
Examples
T <- TRUE; F <- FALSE
x1 <- m(T, T, T, F, T |
T, T, F, T, T |
F, T, T, T, F |
T, T, T, T, T |
F, F, T, T, T |
F, T, T, T, F)
plot_matrix(x1)
x2 <- m(T, T, T, F, T |
T, T, F, T, T )
plot(x2)
x3 <- m(runif(3) | runif(3) | runif(3))
plot(x3)
Create matrix of random choosen boolean values
Description
Create matrix of random choosen boolean values
Usage
rboolm(nrow, ncol, true.proba = 0.5)
Arguments
nrow |
number of rows |
ncol |
numer of columns |
true.proba |
probability of true values; default: 0.5 |
Value
a matrix
Examples
rboolm(3, 3)
rboolm(4, 5, true.proba = 0.3)
Repeat columns or rows
Description
Repeat matrix object respectively to its shape and orientation
Usage
crep(x, times)
rrep(x, times)
Arguments
x |
matrix |
times |
number of repetitions |
Details
crep = columnwise repetition
rrep = rowwise repetition
Value
matrix
Examples
# Columnwise repetition
crep(v(1:3), 4)
crep(t(v(1:5)), 4)
# Rowwise repetition
rrep(v(1:3), 4)
rrep(t(v(1:5)), 4)
Create matrix of random values with dimensions copied from an existing matrix
Description
Create matrix of random values with dimensions copied from an existing matrix
Usage
runif_same_dims(mat, min = 0, max = 1)
Arguments
mat |
matrix |
min |
lower limit of the distribution. Must be finite. |
max |
upper limit of the distribution. Must be finite. |
Value
a matrix
Examples
mat <- matrix(0, 3, 3)
runif_same_dims(mat)
Create matrix of random values drawn from uniform distribution
Description
Create matrix of random values drawn from uniform distribution
Usage
runifm(nrow, ncol, min = 0, max = 1)
Arguments
nrow |
number of rows |
ncol |
numer of columns |
min |
lower limit of the distribution. Must be finite. |
max |
upper limit of the distribution. Must be finite. |
Value
a matrix
Examples
runifm(3, 3)
runifm(4, 5, min = -1, max = 3)
Return a sequence of pairs (value, index vector)
Description
Facilitates iterating over matrix, returning a sequence of pairs, where the first element is a value at index (x, y) and the second one is the index (x, y)
Usage
seq_matrix(mat)
Arguments
mat |
matrix |
Value
list of two-element list (single value, two-element vector)
Examples
mat <- matrix(1:9, 3, 3)
seq_matrix(mat)
Set multiple values useing one function call
Description
This functions allows to set multiple elements of a matrix instead of using annoying step-by-step assignment by mat[1,2] <- 2 mat[2,3] <- 0.5 etc.
Usage
set_values(mat, ...)
sv(mat, ...)
Arguments
mat |
a matrix object |
... |
formulae; left hand values should be two-element interger vectors and right-hand: a single-value numeric |
Value
matrix
Examples
mat <- matrix(0, 4, 5)
set_values(mat, c(1,1) ~ 5, c(3, 4) ~ 0.3)
A shortcut to create a vertical vector
Description
This function provides convenient shortcut to create a vertical (column) vector.
Usage
v(...)
Arguments
... |
arbitrary number of values |
Value
matrix with dims n_elements x 1
Examples
# Enumerating all the values with commas
v(1, 2, 3)
# Passing whole sequence as an argument
v(1:5)
Create new matrix copying dimensions from the existing one
Description
Create new matrix copying dimensions from the existing one
Usage
with_same_dims(mat, data)
Arguments
mat |
a matrix with desired dimensions |
data |
sigle numeric value or numeric vector |
Value
a matrix
Examples
x <- matrix(7, 3, 6)
x
with_same_dims(x, 0)
with_same_dims(x, c(1, 2))