cluster_matrix: Reorder a Matrix Based on Hierarchical Clustering
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Description
Usage
Arguments
Value
See Also
Examples
View source: R/cluster_matrix.R
Description
Reorder matrix rows, columns, or both via hierarchical clustering.
Usage
1
cluster_matrix(x, dim = "both", method = "ward.D2", ...)
Arguments
x
A matrix.
dim
The dimension to reorder (cluster); must be set to "row", "col",
or "both".
method
The agglomeration method to be used (see
hclust).
...
ignored.
Value
Returns a reordered matrix.
See Also
Examples
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cluster_matrix(mtcars)
cluster_matrix(mtcars, dim = 'row')
cluster_matrix(mtcars, dim = 'col')
## Not run:
if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse, viridis, gridExtra)
## plot heatmap w/o clustering
wo <- mtcars %>%
cor() %>%
tidy_matrix('car', 'var') %>%
ggplot(aes(var, car, fill = value)) +
geom_tile() +
scale_fill_viridis(name = expression(r[xy])) +
theme(
axis.text.y = element_text(size = 8) ,
axis.text.x = element_text(
size = 8,
hjust = 1,
vjust = 1,
angle = 45
),
legend.position = 'bottom',
legend.key.height = grid::unit(.1, 'cm'),
legend.key.width = grid::unit(.5, 'cm')
) +
labs(subtitle = "With Out Clustering")
## plot heatmap w clustering
w <- mtcars %>%
cor() %>%
cluster_matrix() %>%
tidy_matrix('car', 'var') %>%
mutate(
var = factor(var, levels = unique(var)),
car = factor(car, levels = unique(car))
) %>%
group_by(var) %>%
ggplot(aes(var, car, fill = value)) +
geom_tile() +
scale_fill_viridis(name = expression(r[xy])) +
theme(
axis.text.y = element_text(size = 8) ,
axis.text.x = element_text(
size = 8,
hjust = 1,
vjust = 1,
angle = 45
),
legend.position = 'bottom',
legend.key.height = grid::unit(.1, 'cm'),
legend.key.width = grid::unit(.5, 'cm')
) +
labs(subtitle = "With Clustering")
gridExtra::grid.arrange(wo, w, ncol = 2)
## End(Not run)
Example output
mpg qsec vs am cyl drat gear wt carb disp hp
Honda Civic 30.4 18.52 1 1 4 4.93 4 1.615 2 75.7 52
Toyota Corolla 33.9 19.90 1 1 4 4.22 4 1.835 1 71.1 65
Fiat 128 32.4 19.47 1 1 4 4.08 4 2.200 1 78.7 66
Fiat X1-9 27.3 18.90 1 1 4 4.08 4 1.935 1 79.0 66
Merc 240D 24.4 20.00 1 0 4 3.69 4 3.190 2 146.7 62
Lotus Europa 30.4 16.90 1 1 4 3.77 5 1.513 2 95.1 113
Merc 230 22.8 22.90 1 0 4 3.92 4 3.150 2 140.8 95
Volvo 142E 21.4 18.60 1 1 4 4.11 4 2.780 2 121.0 109
Datsun 710 22.8 18.61 1 1 4 3.85 4 2.320 1 108.0 93
Toyota Corona 21.5 20.01 1 0 4 3.70 3 2.465 1 120.1 97
Porsche 914-2 26.0 16.70 0 1 4 4.43 5 2.140 2 120.3 91
Ferrari Dino 19.7 15.50 0 1 6 3.62 5 2.770 6 145.0 175
Mazda RX4 21.0 16.46 0 1 6 3.90 4 2.620 4 160.0 110
Mazda RX4 Wag 21.0 17.02 0 1 6 3.90 4 2.875 4 160.0 110
Merc 280 19.2 18.30 1 0 6 3.92 4 3.440 4 167.6 123
Merc 280C 17.8 18.90 1 0 6 3.92 4 3.440 4 167.6 123
Hornet 4 Drive 21.4 19.44 1 0 6 3.08 3 3.215 1 258.0 110
Valiant 18.1 20.22 1 0 6 2.76 3 3.460 1 225.0 105
Merc 450SLC 15.2 18.00 0 0 8 3.07 3 3.780 3 275.8 180
Merc 450SE 16.4 17.40 0 0 8 3.07 3 4.070 3 275.8 180
Merc 450SL 17.3 17.60 0 0 8 3.07 3 3.730 3 275.8 180
Dodge Challenger 15.5 16.87 0 0 8 2.76 3 3.520 2 318.0 150
AMC Javelin 15.2 17.30 0 0 8 3.15 3 3.435 2 304.0 150
Maserati Bora 15.0 14.60 0 1 8 3.54 5 3.570 8 301.0 335
Ford Pantera L 15.8 14.50 0 1 8 4.22 5 3.170 4 351.0 264
Duster 360 14.3 15.84 0 0 8 3.21 3 3.570 4 360.0 245
Camaro Z28 13.3 15.41 0 0 8 3.73 3 3.840 4 350.0 245
Chrysler Imperial 14.7 17.42 0 0 8 3.23 3 5.345 4 440.0 230
Cadillac Fleetwood 10.4 17.98 0 0 8 2.93 3 5.250 4 472.0 205
Lincoln Continental 10.4 17.82 0 0 8 3.00 3 5.424 4 460.0 215
Hornet Sportabout 18.7 17.02 0 0 8 3.15 3 3.440 2 360.0 175
Pontiac Firebird 19.2 17.05 0 0 8 3.08 3 3.845 2 400.0 175
mpg cyl disp hp drat wt qsec vs am gear carb
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
mpg qsec vs am cyl drat gear wt carb disp hp
Mazda RX4 21.0 16.46 0 1 6 3.90 4 2.620 4 160.0 110
Mazda RX4 Wag 21.0 17.02 0 1 6 3.90 4 2.875 4 160.0 110
Datsun 710 22.8 18.61 1 1 4 3.85 4 2.320 1 108.0 93
Hornet 4 Drive 21.4 19.44 1 0 6 3.08 3 3.215 1 258.0 110
Hornet Sportabout 18.7 17.02 0 0 8 3.15 3 3.440 2 360.0 175
Valiant 18.1 20.22 1 0 6 2.76 3 3.460 1 225.0 105
Duster 360 14.3 15.84 0 0 8 3.21 3 3.570 4 360.0 245
Merc 240D 24.4 20.00 1 0 4 3.69 4 3.190 2 146.7 62
Merc 230 22.8 22.90 1 0 4 3.92 4 3.150 2 140.8 95
Merc 280 19.2 18.30 1 0 6 3.92 4 3.440 4 167.6 123
Merc 280C 17.8 18.90 1 0 6 3.92 4 3.440 4 167.6 123
Merc 450SE 16.4 17.40 0 0 8 3.07 3 4.070 3 275.8 180
Merc 450SL 17.3 17.60 0 0 8 3.07 3 3.730 3 275.8 180
Merc 450SLC 15.2 18.00 0 0 8 3.07 3 3.780 3 275.8 180
Cadillac Fleetwood 10.4 17.98 0 0 8 2.93 3 5.250 4 472.0 205
Lincoln Continental 10.4 17.82 0 0 8 3.00 3 5.424 4 460.0 215
Chrysler Imperial 14.7 17.42 0 0 8 3.23 3 5.345 4 440.0 230
Fiat 128 32.4 19.47 1 1 4 4.08 4 2.200 1 78.7 66
Honda Civic 30.4 18.52 1 1 4 4.93 4 1.615 2 75.7 52
Toyota Corolla 33.9 19.90 1 1 4 4.22 4 1.835 1 71.1 65
Toyota Corona 21.5 20.01 1 0 4 3.70 3 2.465 1 120.1 97
Dodge Challenger 15.5 16.87 0 0 8 2.76 3 3.520 2 318.0 150
AMC Javelin 15.2 17.30 0 0 8 3.15 3 3.435 2 304.0 150
Camaro Z28 13.3 15.41 0 0 8 3.73 3 3.840 4 350.0 245
Pontiac Firebird 19.2 17.05 0 0 8 3.08 3 3.845 2 400.0 175
Fiat X1-9 27.3 18.90 1 1 4 4.08 4 1.935 1 79.0 66
Porsche 914-2 26.0 16.70 0 1 4 4.43 5 2.140 2 120.3 91
Lotus Europa 30.4 16.90 1 1 4 3.77 5 1.513 2 95.1 113
Ford Pantera L 15.8 14.50 0 1 8 4.22 5 3.170 4 351.0 264
Ferrari Dino 19.7 15.50 0 1 6 3.62 5 2.770 6 145.0 175
Maserati Bora 15.0 14.60 0 1 8 3.54 5 3.570 8 301.0 335
Volvo 142E 21.4 18.60 1 1 4 4.11 4 2.780 2 121.0 109
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Description Usage Arguments Value See Also Examples
View source: R/cluster_matrix.R
Description
Reorder matrix rows, columns, or both via hierarchical clustering.
Usage
1 | cluster_matrix(x, dim = "both", method = "ward.D2", ...)
|
Arguments
x |
A matrix. |
dim |
The dimension to reorder (cluster); must be set to "row", "col", or "both". |
method |
The agglomeration method to be used (see
|
... |
ignored. |
Value
Returns a reordered matrix.
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | cluster_matrix(mtcars)
cluster_matrix(mtcars, dim = 'row')
cluster_matrix(mtcars, dim = 'col')
## Not run:
if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse, viridis, gridExtra)
## plot heatmap w/o clustering
wo <- mtcars %>%
cor() %>%
tidy_matrix('car', 'var') %>%
ggplot(aes(var, car, fill = value)) +
geom_tile() +
scale_fill_viridis(name = expression(r[xy])) +
theme(
axis.text.y = element_text(size = 8) ,
axis.text.x = element_text(
size = 8,
hjust = 1,
vjust = 1,
angle = 45
),
legend.position = 'bottom',
legend.key.height = grid::unit(.1, 'cm'),
legend.key.width = grid::unit(.5, 'cm')
) +
labs(subtitle = "With Out Clustering")
## plot heatmap w clustering
w <- mtcars %>%
cor() %>%
cluster_matrix() %>%
tidy_matrix('car', 'var') %>%
mutate(
var = factor(var, levels = unique(var)),
car = factor(car, levels = unique(car))
) %>%
group_by(var) %>%
ggplot(aes(var, car, fill = value)) +
geom_tile() +
scale_fill_viridis(name = expression(r[xy])) +
theme(
axis.text.y = element_text(size = 8) ,
axis.text.x = element_text(
size = 8,
hjust = 1,
vjust = 1,
angle = 45
),
legend.position = 'bottom',
legend.key.height = grid::unit(.1, 'cm'),
legend.key.width = grid::unit(.5, 'cm')
) +
labs(subtitle = "With Clustering")
gridExtra::grid.arrange(wo, w, ncol = 2)
## End(Not run)
|
Example output
mpg qsec vs am cyl drat gear wt carb disp hp
Honda Civic 30.4 18.52 1 1 4 4.93 4 1.615 2 75.7 52
Toyota Corolla 33.9 19.90 1 1 4 4.22 4 1.835 1 71.1 65
Fiat 128 32.4 19.47 1 1 4 4.08 4 2.200 1 78.7 66
Fiat X1-9 27.3 18.90 1 1 4 4.08 4 1.935 1 79.0 66
Merc 240D 24.4 20.00 1 0 4 3.69 4 3.190 2 146.7 62
Lotus Europa 30.4 16.90 1 1 4 3.77 5 1.513 2 95.1 113
Merc 230 22.8 22.90 1 0 4 3.92 4 3.150 2 140.8 95
Volvo 142E 21.4 18.60 1 1 4 4.11 4 2.780 2 121.0 109
Datsun 710 22.8 18.61 1 1 4 3.85 4 2.320 1 108.0 93
Toyota Corona 21.5 20.01 1 0 4 3.70 3 2.465 1 120.1 97
Porsche 914-2 26.0 16.70 0 1 4 4.43 5 2.140 2 120.3 91
Ferrari Dino 19.7 15.50 0 1 6 3.62 5 2.770 6 145.0 175
Mazda RX4 21.0 16.46 0 1 6 3.90 4 2.620 4 160.0 110
Mazda RX4 Wag 21.0 17.02 0 1 6 3.90 4 2.875 4 160.0 110
Merc 280 19.2 18.30 1 0 6 3.92 4 3.440 4 167.6 123
Merc 280C 17.8 18.90 1 0 6 3.92 4 3.440 4 167.6 123
Hornet 4 Drive 21.4 19.44 1 0 6 3.08 3 3.215 1 258.0 110
Valiant 18.1 20.22 1 0 6 2.76 3 3.460 1 225.0 105
Merc 450SLC 15.2 18.00 0 0 8 3.07 3 3.780 3 275.8 180
Merc 450SE 16.4 17.40 0 0 8 3.07 3 4.070 3 275.8 180
Merc 450SL 17.3 17.60 0 0 8 3.07 3 3.730 3 275.8 180
Dodge Challenger 15.5 16.87 0 0 8 2.76 3 3.520 2 318.0 150
AMC Javelin 15.2 17.30 0 0 8 3.15 3 3.435 2 304.0 150
Maserati Bora 15.0 14.60 0 1 8 3.54 5 3.570 8 301.0 335
Ford Pantera L 15.8 14.50 0 1 8 4.22 5 3.170 4 351.0 264
Duster 360 14.3 15.84 0 0 8 3.21 3 3.570 4 360.0 245
Camaro Z28 13.3 15.41 0 0 8 3.73 3 3.840 4 350.0 245
Chrysler Imperial 14.7 17.42 0 0 8 3.23 3 5.345 4 440.0 230
Cadillac Fleetwood 10.4 17.98 0 0 8 2.93 3 5.250 4 472.0 205
Lincoln Continental 10.4 17.82 0 0 8 3.00 3 5.424 4 460.0 215
Hornet Sportabout 18.7 17.02 0 0 8 3.15 3 3.440 2 360.0 175
Pontiac Firebird 19.2 17.05 0 0 8 3.08 3 3.845 2 400.0 175
mpg cyl disp hp drat wt qsec vs am gear carb
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
mpg qsec vs am cyl drat gear wt carb disp hp
Mazda RX4 21.0 16.46 0 1 6 3.90 4 2.620 4 160.0 110
Mazda RX4 Wag 21.0 17.02 0 1 6 3.90 4 2.875 4 160.0 110
Datsun 710 22.8 18.61 1 1 4 3.85 4 2.320 1 108.0 93
Hornet 4 Drive 21.4 19.44 1 0 6 3.08 3 3.215 1 258.0 110
Hornet Sportabout 18.7 17.02 0 0 8 3.15 3 3.440 2 360.0 175
Valiant 18.1 20.22 1 0 6 2.76 3 3.460 1 225.0 105
Duster 360 14.3 15.84 0 0 8 3.21 3 3.570 4 360.0 245
Merc 240D 24.4 20.00 1 0 4 3.69 4 3.190 2 146.7 62
Merc 230 22.8 22.90 1 0 4 3.92 4 3.150 2 140.8 95
Merc 280 19.2 18.30 1 0 6 3.92 4 3.440 4 167.6 123
Merc 280C 17.8 18.90 1 0 6 3.92 4 3.440 4 167.6 123
Merc 450SE 16.4 17.40 0 0 8 3.07 3 4.070 3 275.8 180
Merc 450SL 17.3 17.60 0 0 8 3.07 3 3.730 3 275.8 180
Merc 450SLC 15.2 18.00 0 0 8 3.07 3 3.780 3 275.8 180
Cadillac Fleetwood 10.4 17.98 0 0 8 2.93 3 5.250 4 472.0 205
Lincoln Continental 10.4 17.82 0 0 8 3.00 3 5.424 4 460.0 215
Chrysler Imperial 14.7 17.42 0 0 8 3.23 3 5.345 4 440.0 230
Fiat 128 32.4 19.47 1 1 4 4.08 4 2.200 1 78.7 66
Honda Civic 30.4 18.52 1 1 4 4.93 4 1.615 2 75.7 52
Toyota Corolla 33.9 19.90 1 1 4 4.22 4 1.835 1 71.1 65
Toyota Corona 21.5 20.01 1 0 4 3.70 3 2.465 1 120.1 97
Dodge Challenger 15.5 16.87 0 0 8 2.76 3 3.520 2 318.0 150
AMC Javelin 15.2 17.30 0 0 8 3.15 3 3.435 2 304.0 150
Camaro Z28 13.3 15.41 0 0 8 3.73 3 3.840 4 350.0 245
Pontiac Firebird 19.2 17.05 0 0 8 3.08 3 3.845 2 400.0 175
Fiat X1-9 27.3 18.90 1 1 4 4.08 4 1.935 1 79.0 66
Porsche 914-2 26.0 16.70 0 1 4 4.43 5 2.140 2 120.3 91
Lotus Europa 30.4 16.90 1 1 4 3.77 5 1.513 2 95.1 113
Ford Pantera L 15.8 14.50 0 1 8 4.22 5 3.170 4 351.0 264
Ferrari Dino 19.7 15.50 0 1 6 3.62 5 2.770 6 145.0 175
Maserati Bora 15.0 14.60 0 1 8 3.54 5 3.570 8 301.0 335
Volvo 142E 21.4 18.60 1 1 4 4.11 4 2.780 2 121.0 109
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