README.md
In Cal-Poly-Advanced-R/lab-7-clustering-methods-shanealman: Basic clustering implemented by hand
clust431
The goal of clust431 is to preform hierarchical clustering and k-means
clustering.
Installation
You can install the released version of clust431 from
CRAN with:
install.packages("clust431")
Example K-Means
K-Means clustering example on a dataset of two variables using k_means
mydata <- select(iris, Petal.Length, Petal.Width)
k_means(mydata, 3, PCA = F)
#> $`Clustering vector`
#> [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [75] 1 1 1 3 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 1 3 3 3 3
#> [112] 3 3 3 3 3 3 3 3 1 3 3 3 1 3 3 1 1 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3
#> [149] 3 3
#>
#> $`Cluster Means`
#> Petal.Length Petal.Width
#> 1 4.292593 1.359259
#> 2 1.462000 0.246000
#> 3 5.626087 2.047826
#>
#> $`Total SSE`
#> [1] 31.41289
Comparison with kmeans function in the stats package
km1 <- kmeans(mydata, 3)
km1
#> K-means clustering with 3 clusters of sizes 48, 52, 50
#>
#> Cluster means:
#> Petal.Length Petal.Width
#> 1 5.595833 2.037500
#> 2 4.269231 1.342308
#> 3 1.462000 0.246000
#>
#> Clustering vector:
#> [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [38] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [75] 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 2 1 1 1 1
#> [112] 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1
#> [149] 1 1
#>
#> Within cluster sum of squares by cluster:
#> [1] 16.29167 13.05769 2.02200
#> (between_SS / total_SS = 94.3 %)
#>
#> Available components:
#>
#> [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
#> [6] "betweenss" "size" "iter" "ifault"
km1$tot.withinss
#> [1] 31.37136
K-Means clustering with high dimensions
k_means(mtcars, 3, PCA = F)
#> $`Clustering vector`
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> 2 2 2 3
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> 1 3 1 2
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> 2 2 2 3
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> 3 3 1 1
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> 1 2 2 2
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> 2 3 3 1
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> 1 2 2 2
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> 1 2 1 2
#>
#> $`Cluster Means`
#> mpg cyl disp hp drat wt qsec vs
#> 1 14.64444 8.000000 388.2222 232.1111 3.343333 4.161556 16.40444 0.0000000
#> 2 24.50000 4.625000 122.2938 96.8750 4.002500 2.518000 18.54312 0.7500000
#> 3 17.01429 7.428571 276.0571 150.7143 2.994286 3.601429 18.11857 0.2857143
#> am gear carb
#> 1 0.2222222 3.444444 4.000000
#> 2 0.6875000 4.125000 2.437500
#> 3 0.0000000 3.000000 2.142857
#>
#> $`Total SSE`
#> [1] 91343.41
Comparison with kmeans function in the stat package
km2 <- kmeans(mtcars, 3)
km2
#> K-means clustering with 3 clusters of sizes 7, 16, 9
#>
#> Cluster means:
#> mpg cyl disp hp drat wt qsec vs
#> 1 17.01429 7.428571 276.0571 150.7143 2.994286 3.601429 18.11857 0.2857143
#> 2 24.50000 4.625000 122.2937 96.8750 4.002500 2.518000 18.54312 0.7500000
#> 3 14.64444 8.000000 388.2222 232.1111 3.343333 4.161556 16.40444 0.0000000
#> am gear carb
#> 1 0.0000000 3.000000 2.142857
#> 2 0.6875000 4.125000 2.437500
#> 3 0.2222222 3.444444 4.000000
#>
#> Clustering vector:
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> 2 2 2 1
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> 3 1 3 2
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> 2 2 2 1
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> 1 1 3 3
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> 3 2 2 2
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> 2 1 1 3
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> 3 2 2 2
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> 3 2 3 2
#>
#> Within cluster sum of squares by cluster:
#> [1] 11846.09 32838.00 46659.32
#> (between_SS / total_SS = 85.3 %)
#>
#> Available components:
#>
#> [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
#> [6] "betweenss" "size" "iter" "ifault"
km2$tot.withinss
#> [1] 91343.41
Example of k_means using PCA
k_means(mtcars, 7, PCA = T)
#> $`Clustering vector`
#> [1] 2 2 6 1 4 1 3 6 2 2 2 5 5 5 3 3 3 6 6 6 6 1 1 3 4 6 6 6 3 2 7 2
#>
#> $`Cluster Means`
#> # A tibble: 7 x 2
#> Comp.1 Comp.2
#> <dbl> <dbl>
#> 1 -33.3 36.0
#> 2 82.4 -11.1
#> 3 -195. -2.55
#> 4 -147. 39.5
#> 5 -55.2 -10.4
#> 6 148. 4.43
#> 7 -145. -139.
#>
#> $`Total SSE`
#> [1] 41774.24
Example of k_means using PCA and the k-means++ algorithm for improved
initialization
k_means(mtcars, 7, PCA = T, plus.plus = T)
#> $`Clustering vector`
#> [1] 3 3 2 7 4 7 4 2 2 3 3 6 6 6 1 1 1 2 2 2 2 6 6 4 4 2 2 2 5 3 5 2
#>
#> $`Cluster Means`
#> # A tibble: 7 x 2
#> Comp.1 Comp.2
#> <dbl> <dbl>
#> 1 -235. 35.6
#> 2 141. 3.05
#> 3 71.6 -14.2
#> 4 -151. 2.54
#> 5 -152. -96.2
#> 6 -62.7 6.55
#> 7 7.35 40.0
#>
#> $`Total SSE`
#> [1] 29147.72
Example Hierarchical Clustering
Hierarchical clustering on a dataset
mydata <- select(mtcars, mpg, cyl) %>% head(6)
hier_clust(mydata)
#> [1] "Mazda RX4,Mazda RX4 Wag"
#> [2] "Mazda RX4,Mazda RX4 Wag,Hornet 4 Drive"
#> [3] "Valiant,Hornet Sportabout"
#> [4] "Mazda RX4,Mazda RX4 Wag,Hornet 4 Drive,Datsun 710"
#> [5] "Valiant,Hornet Sportabout,Mazda RX4,Mazda RX4 Wag,Hornet 4 Drive,Datsun 710"
Comparison using the hclust function in the stats package
mydata %>% dist() %>% hclust() %>% plot(sub = "")
Example of hierachical clustering using the manhattan distance metric
hier_clust(mydata, method = 'manhattan')
#> [1] "Mazda RX4,Mazda RX4 Wag"
#> [2] "Mazda RX4,Mazda RX4 Wag,Hornet 4 Drive"
#> [3] "Valiant,Hornet Sportabout"
#> [4] "Datsun 710,Mazda RX4,Mazda RX4 Wag,Hornet 4 Drive"
#> [5] "Valiant,Hornet Sportabout,Mazda RX4,Mazda RX4 Wag,Hornet 4 Drive,Datsun 710"
Example of hierachical clustering using a large dataset
hier_clust(mtcars)
#> [1] "Mazda RX4 Wag,Mazda RX4"
#> [2] "Merc 450SE,Merc 450SL"
#> [3] "Merc 280C,Merc 280"
#> [4] "Merc 450SLC,Merc 450SE,Merc 450SL"
#> [5] "Fiat X1-9,Fiat 128"
#> [6] "Toyota Corona,Porsche 914-2"
#> [7] "Duster 360,Camaro Z28"
#> [8] "Toyota Corolla,Fiat X1-9,Fiat 128"
#> [9] "Toyota Corona,Porsche 914-2,Datsun 710"
#> [10] "AMC Javelin,Dodge Challenger"
#> [11] "Honda Civic,Toyota Corolla,Fiat X1-9,Fiat 128"
#> [12] "Cadillac Fleetwood,Lincoln Continental"
#> [13] "Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280"
#> [14] "Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E"
#> [15] "Ford Pantera L,Duster 360,Camaro Z28"
#> [16] "Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230"
#> [17] "Valiant,Hornet 4 Drive"
#> [18] "Hornet Sportabout,Pontiac Firebird"
#> [19] "Chrysler Imperial,Cadillac Fleetwood,Lincoln Continental"
#> [20] "Lotus Europa,Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230"
#> [21] "AMC Javelin,Dodge Challenger,Merc 450SLC,Merc 450SE,Merc 450SL"
#> [22] "Merc 240D,Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280"
#> [23] "Lotus Europa,Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230,Merc 240D,Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280"
#> [24] "Ford Pantera L,Duster 360,Camaro Z28,Hornet Sportabout,Pontiac Firebird"
#> [25] "Valiant,Hornet 4 Drive,AMC Javelin,Dodge Challenger,Merc 450SLC,Merc 450SE,Merc 450SL"
#> [26] "Ferrari Dino,Lotus Europa,Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230,Merc 240D,Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280"
#> [27] "Chrysler Imperial,Cadillac Fleetwood,Lincoln Continental,Ford Pantera L,Duster 360,Camaro Z28,Hornet Sportabout,Pontiac Firebird"
#> [28] "Honda Civic,Toyota Corolla,Fiat X1-9,Fiat 128,Ferrari Dino,Lotus Europa,Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230,Merc 240D,Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280"
#> [29] "Maserati Bora,Chrysler Imperial,Cadillac Fleetwood,Lincoln Continental,Ford Pantera L,Duster 360,Camaro Z28,Hornet Sportabout,Pontiac Firebird"
#> [30] "Honda Civic,Toyota Corolla,Fiat X1-9,Fiat 128,Ferrari Dino,Lotus Europa,Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230,Merc 240D,Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280,Valiant,Hornet 4 Drive,AMC Javelin,Dodge Challenger,Merc 450SLC,Merc 450SE,Merc 450SL"
#> [31] "Maserati Bora,Chrysler Imperial,Cadillac Fleetwood,Lincoln Continental,Ford Pantera L,Duster 360,Camaro Z28,Hornet Sportabout,Pontiac Firebird,Honda Civic,Toyota Corolla,Fiat X1-9,Fiat 128,Ferrari Dino,Lotus Europa,Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230,Merc 240D,Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280,Valiant,Hornet 4 Drive,AMC Javelin,Dodge Challenger,Merc 450SLC,Merc 450SE,Merc 450SL"
Cal-Poly-Advanced-R/lab-7-clustering-methods-shanealman documentation built on May 23, 2020, 12:33 a.m.
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clust431
The goal of clust431 is to preform hierarchical clustering and k-means clustering.
Installation
You can install the released version of clust431 from CRAN with:
install.packages("clust431")
Example K-Means
K-Means clustering example on a dataset of two variables using k_means
mydata <- select(iris, Petal.Length, Petal.Width)
k_means(mydata, 3, PCA = F)
#> $`Clustering vector`
#> [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [75] 1 1 1 3 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 1 3 3 3 3
#> [112] 3 3 3 3 3 3 3 3 1 3 3 3 1 3 3 1 1 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3
#> [149] 3 3
#>
#> $`Cluster Means`
#> Petal.Length Petal.Width
#> 1 4.292593 1.359259
#> 2 1.462000 0.246000
#> 3 5.626087 2.047826
#>
#> $`Total SSE`
#> [1] 31.41289
Comparison with kmeans function in the stats package
km1 <- kmeans(mydata, 3)
km1
#> K-means clustering with 3 clusters of sizes 48, 52, 50
#>
#> Cluster means:
#> Petal.Length Petal.Width
#> 1 5.595833 2.037500
#> 2 4.269231 1.342308
#> 3 1.462000 0.246000
#>
#> Clustering vector:
#> [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [38] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [75] 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 2 1 1 1 1
#> [112] 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1
#> [149] 1 1
#>
#> Within cluster sum of squares by cluster:
#> [1] 16.29167 13.05769 2.02200
#> (between_SS / total_SS = 94.3 %)
#>
#> Available components:
#>
#> [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
#> [6] "betweenss" "size" "iter" "ifault"
km1$tot.withinss
#> [1] 31.37136
K-Means clustering with high dimensions
k_means(mtcars, 3, PCA = F)
#> $`Clustering vector`
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> 2 2 2 3
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> 1 3 1 2
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> 2 2 2 3
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> 3 3 1 1
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> 1 2 2 2
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> 2 3 3 1
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> 1 2 2 2
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> 1 2 1 2
#>
#> $`Cluster Means`
#> mpg cyl disp hp drat wt qsec vs
#> 1 14.64444 8.000000 388.2222 232.1111 3.343333 4.161556 16.40444 0.0000000
#> 2 24.50000 4.625000 122.2938 96.8750 4.002500 2.518000 18.54312 0.7500000
#> 3 17.01429 7.428571 276.0571 150.7143 2.994286 3.601429 18.11857 0.2857143
#> am gear carb
#> 1 0.2222222 3.444444 4.000000
#> 2 0.6875000 4.125000 2.437500
#> 3 0.0000000 3.000000 2.142857
#>
#> $`Total SSE`
#> [1] 91343.41
Comparison with kmeans function in the stat package
km2 <- kmeans(mtcars, 3)
km2
#> K-means clustering with 3 clusters of sizes 7, 16, 9
#>
#> Cluster means:
#> mpg cyl disp hp drat wt qsec vs
#> 1 17.01429 7.428571 276.0571 150.7143 2.994286 3.601429 18.11857 0.2857143
#> 2 24.50000 4.625000 122.2937 96.8750 4.002500 2.518000 18.54312 0.7500000
#> 3 14.64444 8.000000 388.2222 232.1111 3.343333 4.161556 16.40444 0.0000000
#> am gear carb
#> 1 0.0000000 3.000000 2.142857
#> 2 0.6875000 4.125000 2.437500
#> 3 0.2222222 3.444444 4.000000
#>
#> Clustering vector:
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> 2 2 2 1
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> 3 1 3 2
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> 2 2 2 1
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> 1 1 3 3
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> 3 2 2 2
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> 2 1 1 3
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> 3 2 2 2
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> 3 2 3 2
#>
#> Within cluster sum of squares by cluster:
#> [1] 11846.09 32838.00 46659.32
#> (between_SS / total_SS = 85.3 %)
#>
#> Available components:
#>
#> [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
#> [6] "betweenss" "size" "iter" "ifault"
km2$tot.withinss
#> [1] 91343.41
Example of k_means using PCA
k_means(mtcars, 7, PCA = T)
#> $`Clustering vector`
#> [1] 2 2 6 1 4 1 3 6 2 2 2 5 5 5 3 3 3 6 6 6 6 1 1 3 4 6 6 6 3 2 7 2
#>
#> $`Cluster Means`
#> # A tibble: 7 x 2
#> Comp.1 Comp.2
#> <dbl> <dbl>
#> 1 -33.3 36.0
#> 2 82.4 -11.1
#> 3 -195. -2.55
#> 4 -147. 39.5
#> 5 -55.2 -10.4
#> 6 148. 4.43
#> 7 -145. -139.
#>
#> $`Total SSE`
#> [1] 41774.24
Example of k_means using PCA and the k-means++ algorithm for improved initialization
k_means(mtcars, 7, PCA = T, plus.plus = T)
#> $`Clustering vector`
#> [1] 3 3 2 7 4 7 4 2 2 3 3 6 6 6 1 1 1 2 2 2 2 6 6 4 4 2 2 2 5 3 5 2
#>
#> $`Cluster Means`
#> # A tibble: 7 x 2
#> Comp.1 Comp.2
#> <dbl> <dbl>
#> 1 -235. 35.6
#> 2 141. 3.05
#> 3 71.6 -14.2
#> 4 -151. 2.54
#> 5 -152. -96.2
#> 6 -62.7 6.55
#> 7 7.35 40.0
#>
#> $`Total SSE`
#> [1] 29147.72
Example Hierarchical Clustering
Hierarchical clustering on a dataset
mydata <- select(mtcars, mpg, cyl) %>% head(6)
hier_clust(mydata)
#> [1] "Mazda RX4,Mazda RX4 Wag"
#> [2] "Mazda RX4,Mazda RX4 Wag,Hornet 4 Drive"
#> [3] "Valiant,Hornet Sportabout"
#> [4] "Mazda RX4,Mazda RX4 Wag,Hornet 4 Drive,Datsun 710"
#> [5] "Valiant,Hornet Sportabout,Mazda RX4,Mazda RX4 Wag,Hornet 4 Drive,Datsun 710"
Comparison using the hclust function in the stats package
mydata %>% dist() %>% hclust() %>% plot(sub = "")
Example of hierachical clustering using the manhattan distance metric
hier_clust(mydata, method = 'manhattan')
#> [1] "Mazda RX4,Mazda RX4 Wag"
#> [2] "Mazda RX4,Mazda RX4 Wag,Hornet 4 Drive"
#> [3] "Valiant,Hornet Sportabout"
#> [4] "Datsun 710,Mazda RX4,Mazda RX4 Wag,Hornet 4 Drive"
#> [5] "Valiant,Hornet Sportabout,Mazda RX4,Mazda RX4 Wag,Hornet 4 Drive,Datsun 710"
Example of hierachical clustering using a large dataset
hier_clust(mtcars)
#> [1] "Mazda RX4 Wag,Mazda RX4"
#> [2] "Merc 450SE,Merc 450SL"
#> [3] "Merc 280C,Merc 280"
#> [4] "Merc 450SLC,Merc 450SE,Merc 450SL"
#> [5] "Fiat X1-9,Fiat 128"
#> [6] "Toyota Corona,Porsche 914-2"
#> [7] "Duster 360,Camaro Z28"
#> [8] "Toyota Corolla,Fiat X1-9,Fiat 128"
#> [9] "Toyota Corona,Porsche 914-2,Datsun 710"
#> [10] "AMC Javelin,Dodge Challenger"
#> [11] "Honda Civic,Toyota Corolla,Fiat X1-9,Fiat 128"
#> [12] "Cadillac Fleetwood,Lincoln Continental"
#> [13] "Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280"
#> [14] "Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E"
#> [15] "Ford Pantera L,Duster 360,Camaro Z28"
#> [16] "Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230"
#> [17] "Valiant,Hornet 4 Drive"
#> [18] "Hornet Sportabout,Pontiac Firebird"
#> [19] "Chrysler Imperial,Cadillac Fleetwood,Lincoln Continental"
#> [20] "Lotus Europa,Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230"
#> [21] "AMC Javelin,Dodge Challenger,Merc 450SLC,Merc 450SE,Merc 450SL"
#> [22] "Merc 240D,Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280"
#> [23] "Lotus Europa,Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230,Merc 240D,Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280"
#> [24] "Ford Pantera L,Duster 360,Camaro Z28,Hornet Sportabout,Pontiac Firebird"
#> [25] "Valiant,Hornet 4 Drive,AMC Javelin,Dodge Challenger,Merc 450SLC,Merc 450SE,Merc 450SL"
#> [26] "Ferrari Dino,Lotus Europa,Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230,Merc 240D,Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280"
#> [27] "Chrysler Imperial,Cadillac Fleetwood,Lincoln Continental,Ford Pantera L,Duster 360,Camaro Z28,Hornet Sportabout,Pontiac Firebird"
#> [28] "Honda Civic,Toyota Corolla,Fiat X1-9,Fiat 128,Ferrari Dino,Lotus Europa,Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230,Merc 240D,Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280"
#> [29] "Maserati Bora,Chrysler Imperial,Cadillac Fleetwood,Lincoln Continental,Ford Pantera L,Duster 360,Camaro Z28,Hornet Sportabout,Pontiac Firebird"
#> [30] "Honda Civic,Toyota Corolla,Fiat X1-9,Fiat 128,Ferrari Dino,Lotus Europa,Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230,Merc 240D,Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280,Valiant,Hornet 4 Drive,AMC Javelin,Dodge Challenger,Merc 450SLC,Merc 450SE,Merc 450SL"
#> [31] "Maserati Bora,Chrysler Imperial,Cadillac Fleetwood,Lincoln Continental,Ford Pantera L,Duster 360,Camaro Z28,Hornet Sportabout,Pontiac Firebird,Honda Civic,Toyota Corolla,Fiat X1-9,Fiat 128,Ferrari Dino,Lotus Europa,Toyota Corona,Porsche 914-2,Datsun 710,Volvo 142E,Merc 230,Merc 240D,Mazda RX4 Wag,Mazda RX4,Merc 280C,Merc 280,Valiant,Hornet 4 Drive,AMC Javelin,Dodge Challenger,Merc 450SLC,Merc 450SE,Merc 450SL"
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