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)

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)