README.md
In Leonardo-Bo/rgrids: Manage grids and lattices
rgrids 
Overview
rgrids is an R package that allows you to work consistently with grids and matrices.
rgrids facilitates density analysis in 1D, 2D and 3D. It is also possible to deal with block matrices and perform operations on lists of matrices. Further developments will concern operations on first neighbors and in general on matrix elements.
rgrids is mainly developed in R, but uses the C++ language to increase performance and use less memory.
Installation and update
rgrids hasn't been released on CRAN yet, so you can install it directly from this repository
devtools::install_github("Leonardo-Bo/rgrids")
If you have already installed the package, you can check for a new version and update it
devtools::update_packages("rgrids")
Overview of functions
The purpose of rgrids is to collect a not too large number of functions that work in a coherent way to solve some recurring problems on grids and matrices. Below is a list of the functions currently available
-
the functions that operate mainly on the grids are:
makeGrid1d(): creates a 1D grid object, i.e. divides a plane into strips or a straight linemakeGrid2d(): creates a 2D grid object, i.e. divides a plane into rectanglesmakeGrid3d(): creates a 2D grid object, i.e. divides a space into parallelepipedsgetBoxplot(): for a 1D grid it associates all y values within a strip to the same xgetCell(): associates each point of a 1D, 2D or 3D array to its respective strip, rectangle or parallelepipedgetCounts(): generates a dataframe with three columns: the coordinates of each cell of the grid and the number of elements that fall inside each cell
-
the functions that operate mainly on block matrices are:
blockmax(): given a matrix and the size of a block, it returns a new matrix containing the max of each blockblockmin(): given a matrix and the size of a block, it returns a new matrix containing the min of each blockblockmean(): given a matrix and the size of a block, it returns a new matrix containing the mean of each blockblocklist(): given a matrix and the size of a block, it returns a list containing all submatrices made by blocksdblocklist(): given a matrix and the size of a block, it returns a list all diagonal submatrices made by blocksmeanMatrix(): given a list of matrix, it returns a single matrix in which element (i, j) is the average of all corresponding elements (i, j) of all matrices in the listsumMatrix(): given a list of matrix, it returns a single matrix in which element (i, j) is the sum of all corresponding elements (i, j) of all matrices in the list dotMatrix(): given a list of matrix, it returns a single matrix in which element (i, j) is the product of all corresponding elements (i, j) of all matrices in the list
-
the functions that operate on rows and columns of a matrix are:
colMax(): given a matrix, it returns the max of each column colMin(): given a matrix, it returns the min of each columnrowMax(): given a matrix, it returns the max of each rowrowMin(): given a matrix, it returns the min of each row
-
the functions that connect matrices and dataframes are:
pileMatrix(): it transforms a matrix into a dataframe with three columns: row and column coordinates of each matrix element and value of each respective matrix element; it allows different selection options, for example the whole matrix or only the upper triangular matrix, ...getTriang(): given the number of elements of a square matrix represented as a three-column dataframe (obtained for example from pileMatrix()), it filters only the desired elements, i.e. the elements of the upper or lower triangular matrix with or without diagonal in the main or mirrored direction.
-
other functions:
tableToLatex(): given a numeric matrix or a data.frame, it returns a basic LaTeX table write with table and tabular packages; rownames and colnames are highlighted with bold.
Some simple uses
2d grids: density analysis
-
Generate random points on a plane
```r
library(rgrids)
df_points <- data.frame(
x = c(rnorm(n = 50000, mean = -2), rnorm(n = 50000, mean = 2)),
y = c(rnorm(n = 50000, mean = 1), rnorm(n = 50000, mean = -1))
)
```
-
Define a grid that contains all the points generated
r
the_grid <- makeGrid2d(
xmin = floor(min(df_points$x)), ymin = floor(min(df_points$y)),
xmax = ceiling(max(df_points$x)), ymax = ceiling(max(df_points$y)),
xcell = 50, ycell = 50, by = "v"
)
class : Grid2d
dimensions : xcell = 50, ycell = 50, ncell = 2500
range : xmin = -7, xmax = 7
ymin = -6, ymax = 6
by : v, count starts from xmin, ymin (bottom-left)
and y increase faster
-
Match each point with a grid element
r
grid_index <- getCell(the_grid, df_points)
df_points$grid_index <- grid_index
-
Count the points within each grid element
r
df_grid <- getCounts(the_grid, grid_index)
Operations on submatrixes and matrix lists
Given a matrix
mat1 <- matrix(1:64, nrow = 8, byrow = TRUE)
-
Calculate the average on 2x2 blocks
r
blockmean(mat1, 2)
```
[,1] [,2] [,3] [,4]
[1,] 5.5 7.5 9.5 11.5
[2,] 21.5 23.5 25.5 27.5
[3,] 37.5 39.5 41.5 43.5
[4,] 53.5 55.5 57.5 59.5
```
-
Get a list of 4x4 matrices
r
blocklist(mat1, 4)
```
$1
[,1] [,2] [,3] [,4]
[1,] 1 2 3 4
[2,] 9 10 11 12
[3,] 17 18 19 20
[4,] 25 26 27 28
$2
[,1] [,2] [,3] [,4]
[1,] 5 6 7 8
[2,] 13 14 15 16
[3,] 21 22 23 24
[4,] 29 30 31 32
$3
[,1] [,2] [,3] [,4]
[1,] 33 34 35 36
[2,] 41 42 43 44
[3,] 49 50 51 52
[4,] 57 58 59 60
$4
[,1] [,2] [,3] [,4]
[1,] 37 38 39 40
[2,] 45 46 47 48
[3,] 53 54 55 56
[4,] 61 62 63 64
```
-
from a list of matrices obtain the sum of each element of the matrix
```r
first create a list of matrices starting from mat1 and using blocklist
list_matrix <- blocklist(mat1, 4)
sumMatrix(list_matrix)
[,1] [,2] [,3] [,4]
[1,] 76 80 84 88
[2,] 108 112 116 120
[3,] 140 144 148 152
[4,] 172 176 180 184
```
Operations on rows and columns of a matrix
Given a matrix
mat2 <- matrix(1:100, ncol = 10)
mat1
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
rowMax(mat1)
## value index
## 1 91 10
## 2 92 10
## 3 93 10
## 4 94 10
## 5 95 10
## 6 96 10
## 7 97 10
## 8 98 10
## 9 99 10
## 10 100 10
rowMin(mat1)
## value index
## 1 1 1
## 2 2 1
## 3 3 1
## 4 4 1
## 5 5 1
## 6 6 1
## 7 7 1
## 8 8 1
## 9 9 1
## 10 10 1
colMax(mat1)
## value index
## 1 10 10
## 2 20 10
## 3 30 10
## 4 40 10
## 5 50 10
## 6 60 10
## 7 70 10
## 8 80 10
## 9 90 10
## 10 100 10
colMin(mat1)
## value index
## 1 1 1
## 2 11 1
## 3 21 1
## 4 31 1
## 5 41 1
## 6 51 1
## 7 61 1
## 8 71 1
## 9 81 1
## 10 91 1
A performance comparison with apply using the bench package
mat3 <- matrix(runif(500000), ncol = 500)
rowMax2 <- function(mat) {
df_rowMax <- data.frame(value = apply(mat, 1, max), index = apply(mat, 1, which.max))
return(df_rowMax)
}
colMax2 <- function(mat) {
df_colMax <- data.frame(value = apply(mat, 2, max), index = apply(mat, 2, which.max))
return(df_colMax)
}
bench::mark(colMax(mat3), colMax2(mat3))
## # A tibble: 2 x 6
## expression min median `itr/sec` mem_alloc `gc/sec`
## <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
## 1 colMax(mat3) 821.88µs 887.2µs 1065. 22.3KB 2.02
## 2 colMax2(mat3) 9.15ms 10.7ms 87.1 19.2MB 56.8
bench::mark(rowMax(mat3), rowMax2(mat3))
## # A tibble: 2 x 6
## expression min median `itr/sec` mem_alloc `gc/sec`
## <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
## 1 rowMax(mat2) 1.07ms 1.15ms 776. 41.8KB 0
## 2 rowMax2(mat2) 11.75ms 13.36ms 71.8 19.3MB 92.3
To do
- implement first neighbors analysis and matrix elements
- further tests before publishing on the CRAN
Leonardo-Bo/rgrids documentation built on July 27, 2024, 2:19 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
rgrids
Overview
rgrids is an R package that allows you to work consistently with grids and matrices.
rgrids facilitates density analysis in 1D, 2D and 3D. It is also possible to deal with block matrices and perform operations on lists of matrices. Further developments will concern operations on first neighbors and in general on matrix elements.
rgrids is mainly developed in R, but uses the C++ language to increase performance and use less memory.
Installation and update
rgrids hasn't been released on CRAN yet, so you can install it directly from this repository
devtools::install_github("Leonardo-Bo/rgrids")
If you have already installed the package, you can check for a new version and update it
devtools::update_packages("rgrids")
Overview of functions
The purpose of rgrids is to collect a not too large number of functions that work in a coherent way to solve some recurring problems on grids and matrices. Below is a list of the functions currently available
-
the functions that operate mainly on the grids are:
makeGrid1d(): creates a 1D grid object, i.e. divides a plane into strips or a straight linemakeGrid2d(): creates a 2D grid object, i.e. divides a plane into rectanglesmakeGrid3d(): creates a 2D grid object, i.e. divides a space into parallelepipedsgetBoxplot(): for a 1D grid it associates all y values within a strip to the same xgetCell(): associates each point of a 1D, 2D or 3D array to its respective strip, rectangle or parallelepipedgetCounts(): generates a dataframe with three columns: the coordinates of each cell of the grid and the number of elements that fall inside each cell
-
the functions that operate mainly on block matrices are:
blockmax(): given a matrix and the size of a block, it returns a new matrix containing themaxof each blockblockmin(): given a matrix and the size of a block, it returns a new matrix containing theminof each blockblockmean(): given a matrix and the size of a block, it returns a new matrix containing themeanof each blockblocklist(): given a matrix and the size of a block, it returns a list containing all submatrices made by blocksdblocklist(): given a matrix and the size of a block, it returns a list all diagonal submatrices made by blocksmeanMatrix(): given a list of matrix, it returns a single matrix in which element (i, j) is the average of all corresponding elements (i, j) of all matrices in the listsumMatrix(): given a list of matrix, it returns a single matrix in which element (i, j) is the sum of all corresponding elements (i, j) of all matrices in the listdotMatrix(): given a list of matrix, it returns a single matrix in which element (i, j) is the product of all corresponding elements (i, j) of all matrices in the list
-
the functions that operate on rows and columns of a matrix are:
colMax(): given a matrix, it returns themaxof each columncolMin(): given a matrix, it returns theminof each columnrowMax(): given a matrix, it returns themaxof each rowrowMin(): given a matrix, it returns theminof each row
-
the functions that connect matrices and dataframes are:
pileMatrix(): it transforms a matrix into a dataframe with three columns: row and column coordinates of each matrix element and value of each respective matrix element; it allows different selection options, for example the whole matrix or only the upper triangular matrix, ...getTriang(): given the number of elements of a square matrix represented as a three-column dataframe (obtained for example frompileMatrix()), it filters only the desired elements, i.e. the elements of the upper or lower triangular matrix with or without diagonal in the main or mirrored direction.
-
other functions:
tableToLatex(): given a numeric matrix or a data.frame, it returns a basic LaTeX table write with table and tabular packages; rownames and colnames are highlighted with bold.
Some simple uses
2d grids: density analysis
-
Generate random points on a plane
```r library(rgrids)
df_points <- data.frame( x = c(rnorm(n = 50000, mean = -2), rnorm(n = 50000, mean = 2)), y = c(rnorm(n = 50000, mean = 1), rnorm(n = 50000, mean = -1)) ) ```
-
Define a grid that contains all the points generated
r the_grid <- makeGrid2d( xmin = floor(min(df_points$x)), ymin = floor(min(df_points$y)), xmax = ceiling(max(df_points$x)), ymax = ceiling(max(df_points$y)), xcell = 50, ycell = 50, by = "v" )class : Grid2d dimensions : xcell = 50, ycell = 50, ncell = 2500 range : xmin = -7, xmax = 7 ymin = -6, ymax = 6 by : v, count starts from xmin, ymin (bottom-left) and y increase faster -
Match each point with a grid element
r grid_index <- getCell(the_grid, df_points) df_points$grid_index <- grid_index -
Count the points within each grid element
r df_grid <- getCounts(the_grid, grid_index)
Operations on submatrixes and matrix lists
Given a matrix
mat1 <- matrix(1:64, nrow = 8, byrow = TRUE)
-
Calculate the average on 2x2 blocks
r blockmean(mat1, 2)```[,1] [,2] [,3] [,4]
[1,] 5.5 7.5 9.5 11.5
[2,] 21.5 23.5 25.5 27.5
[3,] 37.5 39.5 41.5 43.5
[4,] 53.5 55.5 57.5 59.5
```
-
Get a list of 4x4 matrices
r blocklist(mat1, 4)```$
1[,1] [,2] [,3] [,4]
[1,] 1 2 3 4
[2,] 9 10 11 12
[3,] 17 18 19 20
[4,] 25 26 27 28
$
2[,1] [,2] [,3] [,4]
[1,] 5 6 7 8
[2,] 13 14 15 16
[3,] 21 22 23 24
[4,] 29 30 31 32
$
3[,1] [,2] [,3] [,4]
[1,] 33 34 35 36
[2,] 41 42 43 44
[3,] 49 50 51 52
[4,] 57 58 59 60
$
4[,1] [,2] [,3] [,4]
[1,] 37 38 39 40
[2,] 45 46 47 48
[3,] 53 54 55 56
[4,] 61 62 63 64
```
-
from a list of matrices obtain the sum of each element of the matrix
```r
first create a list of matrices starting from
mat1and usingblocklistlist_matrix <- blocklist(mat1, 4)
sumMatrix(list_matrix)
[,1] [,2] [,3] [,4]
[1,] 76 80 84 88
[2,] 108 112 116 120
[3,] 140 144 148 152
[4,] 172 176 180 184
```
Operations on rows and columns of a matrix
Given a matrix
mat2 <- matrix(1:100, ncol = 10)
mat1
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
rowMax(mat1)
## value index
## 1 91 10
## 2 92 10
## 3 93 10
## 4 94 10
## 5 95 10
## 6 96 10
## 7 97 10
## 8 98 10
## 9 99 10
## 10 100 10
rowMin(mat1)
## value index
## 1 1 1
## 2 2 1
## 3 3 1
## 4 4 1
## 5 5 1
## 6 6 1
## 7 7 1
## 8 8 1
## 9 9 1
## 10 10 1
colMax(mat1)
## value index
## 1 10 10
## 2 20 10
## 3 30 10
## 4 40 10
## 5 50 10
## 6 60 10
## 7 70 10
## 8 80 10
## 9 90 10
## 10 100 10
colMin(mat1)
## value index
## 1 1 1
## 2 11 1
## 3 21 1
## 4 31 1
## 5 41 1
## 6 51 1
## 7 61 1
## 8 71 1
## 9 81 1
## 10 91 1
A performance comparison with apply using the bench package
mat3 <- matrix(runif(500000), ncol = 500)
rowMax2 <- function(mat) {
df_rowMax <- data.frame(value = apply(mat, 1, max), index = apply(mat, 1, which.max))
return(df_rowMax)
}
colMax2 <- function(mat) {
df_colMax <- data.frame(value = apply(mat, 2, max), index = apply(mat, 2, which.max))
return(df_colMax)
}
bench::mark(colMax(mat3), colMax2(mat3))
## # A tibble: 2 x 6
## expression min median `itr/sec` mem_alloc `gc/sec`
## <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
## 1 colMax(mat3) 821.88µs 887.2µs 1065. 22.3KB 2.02
## 2 colMax2(mat3) 9.15ms 10.7ms 87.1 19.2MB 56.8
bench::mark(rowMax(mat3), rowMax2(mat3))
## # A tibble: 2 x 6
## expression min median `itr/sec` mem_alloc `gc/sec`
## <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
## 1 rowMax(mat2) 1.07ms 1.15ms 776. 41.8KB 0
## 2 rowMax2(mat2) 11.75ms 13.36ms 71.8 19.3MB 92.3
To do
- implement first neighbors analysis and matrix elements
- further tests before publishing on the CRAN
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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