In-class Exercise 9: Multivariate Analysis

Author

Kristine Joy Paas

Published

June 15, 2024

Modified

June 15, 2024

1 Overview

2 Getting Started

pacman::p_load(scatterPlotMatrix, parallelPlot, cluster, factoextra, tidyverse)

3 Plotting Correlation Map

wine <- read_csv("data/wine_quality.csv")
ggplot(data = wine,
       aes(x = type)) +
  geom_bar()

whitewine <- wine %>%
  filter(type == "white") %>%
  select(c(1:11))
scatterPlotMatrix(whitewine,
                  corrPlotType = "Text",
                  distribType = 1,
                  width = 500,
                  height = 500,
                  rotateTitle = TRUE)
Distribution Representation:
Continuous Color Scale:
Categorical Color Scale:
Correlation Plot Type:
Correlation Color Scale:
Mouse mode:
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensityfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensityfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensityfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensity-0.02270.2890.0890.0231-0.04940.09110.265-0.1490.06430.0705-0.0970.08930.02710.09420.1140.09410.1210.150.08870.2990.4010.8390.1010.1990.2570.6160.2940.5346810121400.10.20.30.40.500.20.40.60.8101234500.511.501234020406000.010.020.030.040.050.0600.10.20.305101520253035010020030000.0050.010.0150.020.025010020030040000.0020.0040.0060.0080.010.9911.011.021.031.0402040608010012014046810121400.20.40.60.81
946810121400.511.5
1700.20.40.60.8100.511.5
184681012140204060
2500.20.40.60.810204060
2600.511.50204060
2746810121400.10.20.3
3300.20.40.60.8100.10.20.3
3400.511.500.10.20.3
35020406000.10.20.3
364681012140100200300
4100.20.40.60.810100200300
4200.511.50100200300
4302040600100200300
4400.10.20.30100200300
454681012140100200300400
4900.20.40.60.810100200300400
5000.511.50100200300400
5102040600100200300400
5200.10.20.30100200300400
5301002003000100200300400
544681012140.9911.011.021.031.04
5700.20.40.60.810.9911.011.021.031.04
5800.511.50.9911.011.021.031.04
5902040600.9911.011.021.031.04
6000.10.20.30.9911.011.021.031.04
6101002003000.9911.011.021.031.04
6201002003004000.9911.011.021.031.04
63

4 Plotting Ternary Chart

The 3 variables in ternary plot must add up to the total, i.e. they represent the same thing

5 Plotting Heatmap

Heatmaps can be very inefficient when there are a lot of observations. For large number of observations, better use paralell plot.

6 Plotting Parallel Plots

set.seed(123)

kmeans4 <- kmeans(whitewine, 4, nstart = 25)
print(kmeans4)
K-means clustering with 4 clusters of sizes 1719, 1444, 757, 978

Cluster means:
  fixed acidity volatile acidity citric acid residual sugar  chlorides
1      6.782403        0.2719372   0.3247469       5.348342 0.04324549
2      6.908172        0.2776939   0.3455402       7.780852 0.04919668
3      6.981506        0.2965786   0.3563540       9.705878 0.05227081
4      6.805112        0.2759356   0.3168814       3.607822 0.04012781
  free sulfur dioxide total sulfur dioxide   density       pH sulphates
1            30.11635             121.1963 0.9931958 3.195829 0.4847935
2            42.31129             160.3061 0.9951215 3.193996 0.4940651
3            52.83421             206.8164 0.9965522 3.176975 0.5179392
4            20.52761              83.1411 0.9919192 3.175256 0.4707566
    alcohol
1 10.833256
2 10.120392
3  9.611471
4 11.233930

Clustering vector:
   [1] 2 1 4 3 3 4 1 2 1 1 4 1 4 2 2 1 4 4 2 1 4 4 1 2 1 3 2 1 1 1 1 4 4 1 2 1 2
  [38] 1 2 2 2 2 2 2 2 2 3 3 2 2 2 1 4 1 1 3 3 2 4 1 1 2 2 4 1 1 1 2 4 1 3 3 3 4
  [75] 4 1 4 4 1 1 1 2 2 3 2 2 2 3 2 2 2 3 1 1 2 3 2 4 4 2 3 2 2 2 3 1 2 2 2 3 2
 [112] 3 3 2 2 4 1 4 3 3 4 1 1 1 2 2 1 3 2 2 4 3 3 3 3 2 1 2 4 4 4 2 1 4 4 1 2 4
 [149] 4 1 2 2 1 4 4 3 3 1 1 1 1 2 4 3 3 2 3 4 2 1 1 4 4 1 2 2 4 2 1 2 2 3 2 3 3
 [186] 3 2 1 1 3 3 2 1 1 3 3 3 3 3 3 3 3 3 1 1 2 1 1 4 2 4 1 1 1 1 2 2 2 2 2 2 2
 [223] 1 2 1 2 3 3 3 2 1 3 3 3 3 3 3 3 1 2 3 4 4 3 2 3 1 4 4 1 3 3 2 1 2 2 4 4 1
 [260] 4 1 2 4 3 2 2 2 2 2 2 2 2 2 1 3 2 2 4 4 1 1 1 3 3 3 2 3 3 3 3 3 2 3 2 2 2
 [297] 2 3 2 1 4 4 4 2 2 2 2 2 1 2 4 2 2 2 2 1 1 1 1 4 4 1 1 1 3 3 3 2 3 4 1 1 4
 [334] 1 4 4 1 2 1 2 2 1 1 2 2 1 4 2 1 2 2 1 1 1 3 3 3 2 2 2 2 4 1 3 4 1 2 2 2 4
 [371] 2 2 3 2 4 4 1 4 2 1 4 2 2 2 1 4 1 3 1 3 3 4 1 4 2 2 4 1 2 4 1 2 1 3 2 2 1
 [408] 1 1 4 2 2 4 4 2 2 4 3 4 1 1 3 3 3 2 3 3 3 4 3 3 4 3 2 1 4 3 3 3 1 4 1 1 3
 [445] 2 4 2 1 1 1 2 1 2 1 1 1 4 1 3 3 1 2 2 4 2 1 2 4 2 3 2 3 4 1 1 3 1 1 2 2 2
 [482] 1 1 2 3 1 1 4 2 2 4 4 2 2 1 2 3 2 2 3 3 2 3 3 2 2 1 2 2 2 2 2 1 4 1 2 2 2
 [519] 4 4 1 1 4 4 4 1 4 1 1 1 1 2 2 2 2 2 2 2 4 3 2 3 2 2 2 2 2 4 1 3 2 4 1 2 1
 [556] 4 2 1 2 2 2 1 2 1 2 4 4 2 2 2 3 1 2 2 1 3 3 2 1 1 3 1 2 4 1 4 2 1 1 1 1 1
 [593] 2 1 1 1 2 1 1 4 2 1 1 1 1 1 2 2 2 4 1 4 1 1 1 1 4 3 3 1 3 2 1 4 1 1 2 3 3
 [630] 4 2 2 1 3 2 1 1 2 3 3 1 3 3 2 2 2 2 2 3 3 3 3 3 1 2 1 4 1 3 3 4 1 2 4 2 1
 [667] 3 2 2 3 3 4 2 1 3 3 3 4 4 4 2 2 2 2 2 3 1 3 3 1 1 3 3 2 3 3 4 3 3 3 3 1 4
 [704] 1 1 4 3 2 1 4 2 1 1 3 3 2 3 2 2 1 2 2 1 4 1 1 1 4 2 2 1 3 4 2 3 1 2 3 2 1
 [741] 4 4 1 2 2 1 3 2 2 2 2 2 2 3 1 1 2 2 2 1 2 2 3 1 2 1 3 4 1 1 1 2 2 2 1 1 4
 [778] 3 2 2 4 3 2 2 3 1 1 1 1 1 1 4 2 4 2 2 3 2 1 4 2 3 3 2 1 2 3 3 3 3 3 1 2 2
 [815] 3 1 4 1 1 1 4 3 1 1 4 2 2 1 4 4 1 2 1 4 1 1 2 2 2 1 1 2 2 1 1 1 2 4 2 1 1
 [852] 2 1 2 1 1 2 2 2 2 1 3 2 1 2 1 1 2 2 4 2 2 1 4 4 1 1 1 1 1 1 1 1 1 3 1 2 4
 [889] 2 4 2 1 1 1 1 4 3 4 4 3 2 1 3 3 2 4 4 1 2 3 1 1 1 4 4 4 1 1 1 1 2 2 2 3 2
 [926] 4 4 2 2 1 4 3 3 3 3 3 4 1 3 3 3 3 4 1 1 1 3 2 4 4 1 1 4 1 1 1 1 4 4 2 2 1
 [963] 2 1 2 4 3 2 4 4 4 1 2 4 1 1 1 3 2 4 4 2 4 4 2 1 2 2 2 1 2 4 3 4 2 2 4 2 2
[1000] 2 4 3 3 1 2 4 2 4 3 2 1 2 4 3 2 1 1 1 2 3 1 1 3 2 2 1 2 4 1 3 2 3 3 3 3 2
[1037] 4 4 1 4 1 4 1 3 4 4 1 4 4 1 2 1 4 1 4 1 1 3 1 2 1 3 3 3 2 1 2 1 4 2 2 1 1
[1074] 3 2 1 2 1 3 3 1 2 2 3 1 2 1 1 3 1 3 2 2 1 3 4 2 1 1 1 2 1 2 1 2 3 1 4 4 2
[1111] 4 4 2 4 4 4 4 3 4 1 1 1 4 1 1 2 2 4 4 1 2 1 2 1 1 2 2 2 1 4 4 2 1 1 1 3 2
[1148] 2 1 3 3 3 4 4 2 2 1 1 3 1 2 1 2 3 4 1 4 1 4 2 1 1 1 1 3 2 3 2 2 1 1 1 1 1
[1185] 1 3 2 1 2 4 1 1 1 1 3 2 1 1 1 4 4 4 3 4 4 3 2 3 1 1 4 2 2 4 4 2 4 3 1 4 3
[1222] 1 1 2 1 4 1 1 1 4 3 1 4 1 2 3 4 1 1 2 2 2 2 1 1 3 2 4 4 3 2 2 2 2 2 1 2 3
[1259] 3 3 3 2 2 3 4 2 1 2 1 3 2 1 2 1 3 1 2 1 1 2 1 2 2 2 2 1 2 1 1 4 4 3 4 4 4
[1296] 3 1 1 2 2 2 2 3 2 3 1 1 1 1 4 2 1 2 2 2 2 3 2 4 3 1 1 2 2 2 1 2 2 4 1 1 1
[1333] 3 2 1 3 2 3 3 1 1 2 1 2 2 1 2 2 2 4 1 1 3 3 2 2 3 2 1 1 2 3 1 4 2 1 4 1 3
[1370] 3 1 2 2 2 1 1 1 1 1 1 2 4 4 4 1 1 1 4 1 3 1 4 4 4 1 4 1 3 3 4 3 3 1 1 4 1
[1407] 4 4 3 1 4 4 2 1 1 4 1 3 1 1 1 4 1 3 1 1 1 2 4 4 1 4 4 4 2 4 3 4 3 3 2 1 1
[1444] 1 2 1 4 2 2 2 2 1 2 2 3 2 4 1 2 1 1 2 2 1 2 2 2 4 4 1 2 2 4 1 4 2 2 4 2 1
[1481] 2 1 3 4 1 1 4 2 3 3 1 4 3 2 3 3 4 1 4 2 1 2 1 1 1 1 3 2 2 1 1 1 1 2 1 1 2
[1518] 2 4 2 2 1 2 2 2 2 2 3 2 2 2 2 3 1 2 1 4 2 1 1 2 4 1 4 4 2 2 2 1 1 2 1 2 2
[1555] 2 2 2 2 1 4 2 1 1 2 1 1 2 1 3 2 2 3 2 1 1 3 4 1 2 3 1 4 1 2 3 2 2 3 2 1 2
[1592] 2 1 4 1 3 1 3 1 4 1 3 4 4 1 1 1 1 3 3 1 4 4 1 1 2 3 1 3 1 4 1 2 1 1 2 3 1
[1629] 1 4 1 1 1 1 3 1 2 2 3 1 2 2 1 2 1 2 2 4 4 2 1 3 1 2 2 1 4 2 3 3 3 3 1 2 2
[1666] 1 4 1 4 1 2 4 2 2 3 3 4 2 2 1 3 3 3 3 3 3 2 3 3 1 2 3 3 3 2 1 3 3 3 2 1 3
[1703] 1 2 2 1 2 2 2 2 4 1 2 1 1 2 1 1 2 4 2 3 2 2 1 2 4 3 1 1 1 3 1 1 3 2 4 3 4
[1740] 4 2 2 2 2 1 3 4 2 4 1 2 2 3 2 4 2 3 3 4 3 3 1 4 1 3 3 3 2 2 1 2 2 2 2 4 1
[1777] 2 2 2 2 2 3 2 4 1 2 1 2 1 3 2 1 2 3 1 2 1 1 2 2 3 4 2 2 3 1 1 3 1 3 2 1 4
[1814] 1 4 2 1 1 4 1 2 1 4 3 2 4 2 3 3 2 2 2 2 2 2 3 1 1 3 1 2 1 3 1 4 2 2 2 3 3
[1851] 2 4 1 1 2 3 2 2 2 3 2 3 1 3 1 1 3 1 2 2 2 2 2 2 2 2 2 4 3 4 3 4 3 3 1 4 1
[1888] 2 3 1 3 3 2 2 2 3 2 2 4 1 2 1 2 1 3 2 1 2 4 1 2 4 1 2 1 4 2 4 2 3 2 1 1 4
[1925] 4 4 4 2 3 2 3 3 4 2 4 2 3 2 4 2 3 2 3 3 3 2 2 3 1 2 3 2 1 2 3 2 4 4 3 4 4
[1962] 1 4 3 2 2 1 3 2 2 1 2 1 1 1 3 3 2 1 3 3 3 3 3 3 2 2 2 3 1 1 3 4 2 2 2 2 2
[1999] 2 3 2 2 2 2 2 2 2 4 2 4 4 2 2 1 4 4 1 4 1 1 2 2 3 2 3 2 4 2 2 3 1 2 4 3 1
[2036] 4 2 2 1 4 3 1 1 1 1 4 1 2 2 2 1 2 2 4 4 2 2 2 3 2 3 4 1 1 2 1 1 2 1 1 2 1
[2073] 2 3 2 1 1 3 1 1 1 4 1 1 2 2 4 2 1 2 2 2 4 1 1 2 1 2 2 2 2 4 3 1 2 1 3 2 2
[2110] 3 2 2 2 4 3 2 4 1 1 1 2 1 2 2 1 2 2 3 2 1 1 2 2 2 1 3 1 3 4 4 1 1 2 4 2 2
[2147] 1 1 4 4 1 1 4 4 3 2 4 4 1 4 1 4 1 4 1 2 2 3 3 3 3 3 1 2 3 3 1 1 2 1 2 1 2
[2184] 2 1 4 4 1 4 1 1 2 2 1 4 1 4 4 3 3 2 2 3 2 2 2 1 1 1 1 2 1 1 1 1 2 4 1 2 1
[2221] 1 2 2 2 2 2 2 2 1 2 1 2 4 2 4 1 3 2 2 1 2 3 2 2 3 1 2 2 4 3 3 1 2 3 2 1 1
[2258] 1 3 1 3 1 4 2 2 2 2 2 2 2 1 2 1 4 1 2 3 4 3 2 4 4 3 3 3 3 2 2 3 4 1 2 3 4
[2295] 1 2 2 3 1 1 1 1 3 1 2 2 1 2 1 1 2 1 1 4 1 2 1 2 2 4 1 2 1 2 3 1 1 1 1 2 3
[2332] 2 3 1 3 1 3 2 2 4 2 2 4 2 4 3 2 4 1 2 3 3 1 4 4 1 1 4 3 1 1 4 2 2 3 1 3 3
[2369] 2 2 1 3 4 4 3 2 3 4 3 3 3 2 1 4 1 2 2 2 4 4 1 2 1 1 3 3 3 4 4 4 4 1 3 1 1
[2406] 3 4 1 3 1 3 3 3 1 3 1 3 3 4 3 1 3 3 1 3 1 2 3 2 3 3 2 3 3 3 1 2 2 2 1 2 2
[2443] 3 3 3 3 3 1 1 3 2 2 1 1 3 3 2 2 3 2 2 4 4 2 1 2 2 1 4 1 2 2 4 1 4 1 2 4 3
[2480] 1 1 3 3 3 3 3 1 1 1 2 1 3 2 1 1 2 4 1 1 2 1 3 1 1 2 3 3 1 2 1 3 3 4 1 2 4
[2517] 2 3 4 3 2 1 2 2 2 2 1 1 1 2 2 2 2 2 4 2 1 1 1 1 2 2 2 1 1 2 2 1 3 3 1 3 1
[2554] 2 2 2 2 2 2 1 4 1 4 1 2 3 4 1 3 1 1 4 4 2 2 3 3 3 1 1 2 2 2 2 2 2 2 4 2 2
[2591] 1 2 2 2 1 2 3 1 3 3 1 3 1 1 1 4 2 3 3 4 2 3 1 1 4 1 1 1 1 2 2 1 1 1 4 2 1
[2628] 1 3 3 1 1 3 2 3 4 2 3 1 4 4 2 4 2 2 1 4 1 2 2 2 2 4 2 3 3 3 1 2 4 2 2 1 4
[2665] 4 1 2 1 1 2 2 2 1 4 1 1 4 2 1 1 1 2 1 1 1 4 1 3 2 1 1 1 2 1 1 1 2 4 2 1 4
[2702] 1 1 1 3 3 3 1 3 3 3 2 2 3 3 2 3 2 4 2 4 2 1 1 2 2 4 1 3 4 3 2 1 4 2 3 1 4
[2739] 1 4 2 1 2 4 4 4 2 1 2 1 2 1 1 4 4 3 3 4 4 1 2 2 2 1 2 1 4 2 1 2 3 1 1 4 1
[2776] 1 1 1 4 1 1 2 3 3 3 2 4 2 2 2 3 3 3 1 2 4 1 2 1 1 3 3 4 4 4 1 2 2 3 2 4 1
[2813] 1 2 4 4 1 4 2 1 1 3 2 4 2 2 3 2 2 2 2 2 4 1 1 2 3 2 4 4 4 4 4 4 4 4 4 4 2
[2850] 3 2 4 2 1 4 2 1 4 2 1 2 4 4 4 1 1 1 1 1 1 1 4 2 4 1 4 2 2 1 4 4 4 1 4 1 4
[2887] 4 4 4 1 2 2 3 2 4 2 3 3 4 2 4 4 2 4 1 2 3 4 4 4 2 2 4 3 4 4 1 1 4 1 4 3 1
[2924] 1 1 3 4 2 2 1 2 4 3 1 4 4 4 3 1 1 1 1 2 1 1 2 1 1 3 1 1 4 1 1 4 1 4 4 4 4
[2961] 1 1 4 1 1 1 1 1 2 4 2 1 1 1 1 2 1 1 1 1 1 4 3 2 4 1 1 1 4 3 2 2 1 1 1 1 1
[2998] 2 1 1 1 1 2 4 1 2 3 2 2 3 3 1 4 1 4 4 1 2 1 4 4 4 1 4 1 2 1 2 1 1 1 1 4 3
[3035] 2 4 3 2 1 3 1 2 2 1 1 4 1 2 1 3 3 3 2 1 4 1 4 1 3 4 2 2 1 2 3 1 3 1 1 4 1
[3072] 4 2 1 1 4 1 3 4 1 4 2 4 4 4 4 4 3 4 4 4 3 2 1 4 4 4 1 1 1 1 4 1 1 1 2 2 2
[3109] 2 3 1 4 1 1 1 1 4 4 2 4 3 1 1 4 1 2 1 4 4 1 2 3 1 1 1 3 1 1 1 1 3 4 1 1 1
[3146] 1 1 2 1 2 4 1 3 4 1 1 1 1 1 1 4 1 1 1 3 1 1 1 4 1 2 4 1 1 1 2 4 2 4 1 4 1
[3183] 1 4 4 1 4 1 1 2 1 2 1 1 4 1 1 1 1 1 2 1 4 1 2 2 4 1 2 2 1 2 1 2 4 4 1 1 1
[3220] 4 4 4 1 2 2 4 1 3 3 1 2 1 4 4 1 2 2 2 1 4 1 1 1 4 4 1 1 2 2 2 1 2 1 1 3 3
[3257] 3 3 3 3 3 4 3 4 3 2 1 2 1 3 1 4 4 1 1 4 2 1 3 1 1 1 1 2 1 1 1 1 2 3 4 4 3
[3294] 4 1 3 3 3 1 1 4 4 4 4 2 4 2 3 1 4 1 2 4 4 3 4 4 1 1 2 1 4 1 4 1 1 2 4 1 1
[3331] 2 2 2 2 4 3 3 3 4 4 1 4 1 3 3 3 3 2 4 4 1 4 4 4 1 1 2 4 4 4 4 4 1 4 4 4 1
[3368] 1 2 1 1 1 2 1 2 1 2 3 2 3 1 1 1 2 2 1 2 3 4 4 1 1 4 1 3 3 1 3 3 4 1 1 1 1
[3405] 4 1 4 3 3 1 2 1 2 3 2 2 3 4 3 2 2 4 1 2 1 2 2 2 1 2 2 2 1 4 4 4 4 1 3 1 1
[3442] 1 4 1 3 1 2 4 1 1 1 1 1 4 1 4 2 2 1 2 1 3 1 1 2 1 1 3 4 2 3 1 1 4 3 2 4 2
[3479] 2 4 4 1 4 4 4 1 4 3 4 4 2 1 1 1 1 1 2 2 1 1 1 1 2 4 1 1 2 1 2 2 2 4 1 4 4
[3516] 4 2 1 1 1 3 2 2 3 1 1 1 2 4 1 2 2 4 2 2 2 4 1 1 4 4 1 2 2 2 3 2 3 1 2 1 1
[3553] 1 1 4 1 1 4 1 4 4 4 2 4 4 4 1 4 4 4 4 4 1 4 1 1 2 1 1 4 2 1 4 4 4 1 2 1 1
[3590] 1 1 2 2 2 1 1 1 2 2 3 4 1 1 1 4 2 2 4 2 2 2 4 1 2 2 4 3 1 1 1 2 1 4 1 4 3
[3627] 1 3 2 2 1 1 1 1 2 4 4 1 4 4 1 2 2 1 1 2 4 4 1 1 1 3 1 3 1 1 3 1 2 1 1 2 4
[3664] 2 2 1 2 1 4 1 2 4 4 4 1 2 4 2 1 1 3 1 1 3 1 3 2 4 3 1 2 1 1 1 1 2 1 3 4 2
[3701] 2 1 2 2 2 2 4 1 3 2 4 2 2 3 4 3 2 1 1 3 2 1 1 2 1 1 1 2 4 1 3 2 1 1 1 4 4
[3738] 1 1 2 2 2 2 2 2 2 1 3 1 2 2 1 2 2 1 1 1 2 2 1 1 4 4 4 1 3 3 3 1 3 1 1 1 1
[3775] 3 1 1 1 1 4 3 1 4 3 1 4 3 3 3 3 3 3 1 2 1 1 4 4 1 2 4 4 1 1 4 4 4 1 1 1 2
[3812] 2 1 2 2 1 1 1 1 1 1 2 3 3 1 4 1 4 1 4 1 1 1 1 2 1 1 1 2 1 4 3 1 1 4 2 1 2
[3849] 4 4 1 1 4 1 1 2 4 1 1 3 3 2 3 3 4 1 1 3 3 2 2 3 3 2 3 1 2 4 2 4 1 1 1 2 1
[3886] 4 2 4 1 1 4 1 1 4 1 4 2 2 1 1 4 4 4 4 1 4 4 4 1 1 2 1 4 1 1 1 2 3 1 1 1 2
[3923] 4 1 1 4 4 1 2 2 4 1 1 4 4 3 1 2 4 2 2 2 1 1 2 2 1 2 1 2 2 2 4 1 2 4 1 4 1
[3960] 1 2 2 1 1 2 4 1 3 3 1 2 1 4 3 3 2 1 1 3 3 2 2 2 1 1 1 1 3 1 1 3 1 4 1 1 1
[3997] 1 2 1 1 1 1 4 1 1 1 4 1 4 2 1 2 1 2 3 4 2 1 3 4 4 1 2 2 2 4 2 2 4 1 1 1 1
[4034] 1 1 2 2 2 1 1 3 2 1 1 1 1 2 1 1 4 1 4 2 1 2 4 1 1 1 4 4 4 1 1 4 1 2 2 2 1
[4071] 2 4 2 1 4 1 1 1 1 4 1 1 2 2 4 4 4 1 4 1 2 4 1 4 4 4 1 4 1 1 4 2 2 4 4 1 2
[4108] 2 1 2 3 4 4 4 1 4 2 2 1 2 1 2 2 4 4 2 2 3 3 4 1 3 3 1 4 1 1 3 4 2 2 2 1 1
[4145] 2 2 1 2 1 4 3 3 1 3 3 3 3 2 2 2 2 2 2 1 1 4 2 1 1 1 2 1 2 4 2 2 2 1 1 3 1
[4182] 4 2 4 4 3 4 1 1 1 4 1 4 4 4 4 4 2 2 4 4 4 1 2 1 4 2 1 4 4 1 3 2 4 3 3 3 1
[4219] 2 3 4 1 1 4 4 3 2 4 3 1 1 4 4 1 1 1 1 4 1 4 1 2 2 4 1 1 4 1 1 2 4 4 4 4 1
[4256] 1 1 1 1 1 2 1 2 2 1 2 1 1 2 3 2 3 1 1 1 1 1 2 4 1 1 1 1 4 4 4 4 1 4 1 1 3
[4293] 1 3 1 3 1 1 1 2 2 2 3 1 1 1 1 1 4 1 2 1 1 4 1 1 4 2 1 1 3 2 1 1 1 2 2 2 2
[4330] 2 2 2 2 2 2 2 2 2 2 4 2 1 2 1 1 1 1 2 2 3 4 1 2 2 2 1 2 3 2 3 2 1 1 1 1 2
[4367] 1 1 1 1 1 4 1 4 2 3 1 4 1 1 2 2 1 4 2 2 2 4 4 1 2 3 2 2 2 2 2 2 2 2 2 1 2
[4404] 3 3 3 1 4 3 1 2 4 1 4 1 1 2 1 1 1 1 1 1 1 1 1 1 3 2 2 2 4 4 3 2 2 4 2 1 1
[4441] 2 1 2 1 1 1 1 4 1 2 1 3 2 4 2 2 2 2 1 1 2 2 1 1 1 1 2 2 4 1 4 4 4 1 1 1 1
[4478] 2 2 1 1 2 2 1 1 4 4 4 1 1 1 4 4 2 4 3 4 2 1 4 2 2 2 1 1 2 1 4 2 4 2 1 1 4
[4515] 3 1 4 4 4 2 3 3 4 2 2 2 3 4 4 2 2 2 1 1 1 2 2 4 1 4 1 1 4 4 1 1 4 4 3 4 4
[4552] 1 1 1 1 4 1 3 1 1 1 4 1 1 1 2 2 2 1 1 4 4 4 4 1 1 4 4 4 1 1 1 2 2 1 2 1 1
[4589] 1 1 2 3 2 1 1 1 1 4 1 4 1 1 2 1 2 4 1 2 4 4 4 4 2 2 2 1 4 1 1 3 1 4 1 1 4
[4626] 2 3 4 4 4 1 1 3 3 1 1 2 1 2 3 1 1 4 3 1 2 4 1 3 4 4 1 3 4 2 2 2 2 1 4 4 2
[4663] 1 1 1 1 3 1 1 1 2 2 2 1 2 2 1 1 2 2 1 4 4 1 3 1 1 2 2 2 2 2 2 2 2 1 4 1 1
[4700] 2 2 2 2 4 3 1 1 1 1 2 1 1 1 4 1 4 4 1 1 4 4 4 1 2 4 1 4 1 1 4 1 2 2 1 4 4
[4737] 4 1 1 4 3 1 1 2 4 3 1 4 2 2 2 3 4 1 1 4 1 1 1 1 1 1 4 1 1 4 1 2 2 2 2 2 3
[4774] 4 1 1 1 1 1 4 1 2 1 1 2 4 1 1 2 1 1 1 1 2 2 2 2 4 1 1 1 2 1 1 4 4 1 1 4 2
[4811] 2 4 1 2 1 1 2 1 4 1 2 1 2 1 2 1 1 4 2 4 1 1 1 4 4 1 4 3 1 4 1 3 4 2 2 4 2
[4848] 1 2 2 2 2 1 4 4 2 2 1 2 1 1 4 4 4 2 4 1 4 1 4 1 4 2 1 1 4 1 4 4 2 2 1 2 2
[4885] 2 2 1 4 1 1 4 1 1 4 2 1 1 4

Within cluster sum of squares by cluster:
[1] 462118.7 579703.3 681403.3 357903.9
 (between_SS / total_SS =  80.0 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
[6] "betweenss"    "size"         "iter"         "ifault"      
fviz_cluster(kmeans4, data=whitewine)

whitewine <- whitewine %>%
  mutate(Cluster = kmeans4$cluster)
whitewine$Cluster <- as_factor(whitewine$Cluster)
whitewine %>%
  parallelPlot(refColumnDim = "Cluster",
               width = 300,
               height = 250,
               rotateTitle = TRUE)
Continuous Color Scale:
Categorical Color Scale:
Categories Representation:
Arrange Method in Category Boxes:
4567891011121314fixed acidity0.10.20.30.40.50.60.70.80.911.1volatile acidity00.20.40.60.811.21.41.6citric acid05101520253035404550556065residual sugar00.050.10.150.20.250.30.35chlorides020406080100120140160180200220240260280free sulfur dioxide050100150200250300350400total sulfur dioxide0.990.99511.0051.011.0151.021.0251.031.0351.04density2.72.82.933.13.23.33.43.53.63.73.8pH0.20.30.40.50.60.70.80.911.1sulphates88.599.51010.51111.51212.51313.514alcohol11.21.41.61.822.22.42.62.833.23.43.63.84Cluster