In-class Ex9

Author

Jayexx

Published

June 15, 2024

Modified

June 16, 2024

Overview

The key learning objective of this hands-on exercise is to plot functional and truthful choropleth maps using appropriate R packages.

Getting Started

The code chunk below installs and loads various required packages into R environment

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

Importing Data

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,
                  rotateTitle = TRUE,
                  width = 900,
                  height = 900
                  )

Compute gap stats

set.seed(1234)
gap_stat <- clusGap(whitewine,
                    FUN = kmeans,
                    nstart = 25,
                    K.max = 8,
                    B=50)
print(gap_stat, method = "firstmax")

Clustering Gap statistic ["clusGap"] from call:
clusGap(x = whitewine, FUNcluster = kmeans, K.max = 8, B = 50, nstart = 25)
B=50 simulated reference sets, k = 1..8; spaceH0="scaledPCA"
 --> Number of clusters (method 'firstmax'): 4
         logW   E.logW      gap      SE.sim
[1,] 11.10732 12.39508 1.287761 0.004179654
[2,] 10.66044 11.96489 1.304448 0.004430951
[3,] 10.45646 11.79647 1.340018 0.004159366
[4,] 10.31450 11.69596 1.381456 0.004007562
[5,] 10.23143 11.59684 1.365408 0.003900456
[6,] 10.15899 11.50135 1.342360 0.004266582
[7,] 10.09952 11.42700 1.327488 0.003842396
[8,] 10.06017 11.36623 1.306064 0.003989220

set.seed(123)
kmeans4 <- kmeans(whitewine, 4, nstart = 5)
print(kmeans4)

K-means clustering with 4 clusters of sizes 757, 978, 1444, 1719

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

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

Within cluster sum of squares by cluster:
[1] 681403.3 357903.9 579703.3 462118.7
 (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)