In Class Exercise 9

Author

Seng Jing Yi

Published

June 15, 2024

Modified

June 15, 2024

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

Import Data

wine <- read_csv("data/wine_quality.csv")

Quick overview of the type of wine in data set.

ggplot(data = wine, aes(x= type)) + geom_bar() + labs(
  title = "Breakdown of Wine by Type", 
  x = "Type of Wine", 
  y = "Frequency of Wine"
)

Only interested in white wine, hence filter to white wine.

Drop column 12: Quality of Wine to make the data more clean for clustering
Drop column 13 on type as already filtered down to white wine, all will be “white:

whitewine <- wine %>% 
  filter(type == "white") %>%
  select(c(1:11))

Scatter Plot Matrix (Interactive)

Plotting the scatter plot matrix.

As the plot is a widget itself, the width and height of the display is defined within the variable (500 pixel width and 500 pixel height).
distribType = 1: Density Plot, 2 = Histogram
corrPlotCS = Setting color for the scheme. (e.g “YlOrRd”)

# returning density plot
scatterPlotMatrix(whitewine, corrPlotType = "Text", 
                  distribType = 1, 
                  width = 500, 
                  height = 500)

# returning histogram
scatterPlotMatrix(whitewine, corrPlotType = "Text", 
                  distribType = 2, 
                  width = 500, 
                  height = 500)

Exposing control for the widget with controlWidgets = TRUE

scatterPlotMatrix(whitewine, corrPlotType = "Text", 
                  distribType = 2, 
                  width = 500, 
                  height = 500, 
                  controlWidgets = TRUE)

Clustering

Determining optimal number of clusters

#set.seed(1234)
#gap_stat <- clusGap

#fviz_gap_stat(gap_stat)

Deriving the 4 clusters

set.seed defined to ensure that it starts from the same position to return similar clusters per iteration

set.seed(123)
kmeans4 <- kmeans(whitewine, 4, nstart = 25)
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"

Visualisation technique to show the cluster group.

fviz_cluster(kmeans4, data = whitewine)

Take results from kmeans and pull out the cluster field and append back to the white wine data set.

whitewine <- whitewine %>% mutate(Cluster = kmeans4$cluster)

Factorize the cluster column (1,2,3,4,5), as the values returned from cluster is as integer (continuous), should convert into discrete categories.

whitewine$Cluster <- as_factor(whitewine$Cluster)

Plotting the parallel plot

Rotate labels for axis to minimise overlap.

#whitewine %>%