library(car)
## Loading required package: carData
mtcars
## mpg cyl disp hp drat wt qsec vs am gear carb
## 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
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 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
## 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
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 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
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 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
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 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
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
library(rgl)
## This build of rgl does not include OpenGL functions. Use
## rglwidget() to display results, e.g. via options(rgl.printRglwidget = TRUE).
data <- mtcars
plot3d(
x=data$`mpg`, y=data$`cyl`, z=data$`disp`,
type = 'p',
radius = 10,
xlab="mpg", ylab="cyl", zlab="disp")
rglwidget()
library(factoextra)
## Loading required package: ggplot2
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
data <- mtcars [, c("mpg", "cyl", "disp")]
# Compute k-means with k = 3
set.seed(123)
km.res <- kmeans(data, 3)
print(km.res)
## K-means clustering with 3 clusters of sizes 12, 4, 16
##
## Cluster means:
## mpg cyl disp
## 1 16.350 7.666667 304.5333
## 2 13.675 8.000000 443.0000
## 3 24.500 4.625000 122.2937
##
## Clustering vector:
## Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
## 3 3 3 1
## Hornet Sportabout Valiant Duster 360 Merc 240D
## 1 1 1 3
## Merc 230 Merc 280 Merc 280C Merc 450SE
## 3 3 3 1
## Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
## 1 1 2 2
## Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
## 2 3 3 3
## Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
## 3 1 1 1
## Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
## 2 3 3 3
## Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
## 1 3 1 3
##
## Within cluster sum of squares by cluster:
## [1] 21601.963 3041.028 18312.519
## (between_SS / total_SS = 91.0 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"
km.res$cluster
## Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
## 3 3 3 1
## Hornet Sportabout Valiant Duster 360 Merc 240D
## 1 1 1 3
## Merc 230 Merc 280 Merc 280C Merc 450SE
## 3 3 3 1
## Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
## 1 1 2 2
## Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
## 2 3 3 3
## Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
## 3 1 1 1
## Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
## 2 3 3 3
## Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
## 1 3 1 3
km.res$centers
## mpg cyl disp
## 1 16.350 7.666667 304.5333
## 2 13.675 8.000000 443.0000
## 3 24.500 4.625000 122.2937
my_colors <- c("red", "blue", "green")
data$color<- my_colors[km.res$cluster]
plot3d(
x=data$mpg, y=data$cyl, z=data$dis,
col = data$color,
type = 'p',
radius = 5,
xlab="mpg", ylab="cyl", zlab="disp")
rglwidget()