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.

  1. Drop column 12: Quality of Wine to make the data more clean for clustering
  2. 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)
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
# returning histogram
scatterPlotMatrix(whitewine, corrPlotType = "Text", 
                  distribType = 2, 
                  width = 500, 
                  height = 500)
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-0.02270.2890.0890.0231-0.04940.09110.265-0.1490.06430.0705-0.0970.08930.02710.09420.1140.09410.1210.150.08870.2990.4010.8390.1010.1990.2570.6160.2940.53468101214020040060080010001.2 E31.4 E300.20.40.60.81020040060080010001.2 E31.4 E300.511.5050010001.5 E32 E30204060050010001.5 E32 E300.10.20.3050010001.5 E32 E32.5 E30100200300050010001.5 E30100200300400020040060080010000.9911.011.021.031.04050010001.5 E346810121400.20.40.60.81
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

Exposing control for the widget with controlWidgets = TRUE

scatterPlotMatrix(whitewine, corrPlotType = "Text", 
                  distribType = 2, 
                  width = 500, 
                  height = 500, 
                  controlWidgets = 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-0.02270.2890.0890.0231-0.04940.09110.265-0.1490.06430.0705-0.0970.08930.02710.09420.1140.09410.1210.150.08870.2990.4010.8390.1010.1990.2570.6160.2940.53468101214020040060080010001.2 E31.4 E300.20.40.60.81020040060080010001.2 E31.4 E300.511.5050010001.5 E32 E30204060050010001.5 E32 E300.10.20.3050010001.5 E32 E32.5 E30100200300050010001.5 E30100200300400020040060080010000.9911.011.021.031.04050010001.5 E346810121400.20.40.60.81
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

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

  1. Rotate labels for axis to minimise overlap.
#whitewine %>%