K Means Clustering Project
Udemy Course: R for Data Science and Machine Learning
Objective: Perform K means clustering for wine data
library(ggplot2)
library(cluster)Load data
fileURL <- "http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality"
df1 <- read.csv(paste0(fileURL, "/winequality-red.csv"), header = TRUE, sep = ";")
df2 <- read.csv(paste0(fileURL, "/winequality-white.csv"), header = TRUE, sep = ";")Add wine color indicator
df1$wine.color <- 'red'
df2$wine.color <- 'white'
head(df1)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.4 0.70 0.00 1.9 0.076
## 2 7.8 0.88 0.00 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.70 0.00 1.9 0.076
## 6 7.4 0.66 0.00 1.8 0.075
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 11 34 0.9978 3.51 0.56 9.4
## 2 25 67 0.9968 3.20 0.68 9.8
## 3 15 54 0.9970 3.26 0.65 9.8
## 4 17 60 0.9980 3.16 0.58 9.8
## 5 11 34 0.9978 3.51 0.56 9.4
## 6 13 40 0.9978 3.51 0.56 9.4
## quality wine.color
## 1 5 red
## 2 5 red
## 3 5 red
## 4 6 red
## 5 5 red
## 6 5 redhead(df2)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.0 0.27 0.36 20.7 0.045
## 2 6.3 0.30 0.34 1.6 0.049
## 3 8.1 0.28 0.40 6.9 0.050
## 4 7.2 0.23 0.32 8.5 0.058
## 5 7.2 0.23 0.32 8.5 0.058
## 6 8.1 0.28 0.40 6.9 0.050
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 45 170 1.0010 3.00 0.45 8.8
## 2 14 132 0.9940 3.30 0.49 9.5
## 3 30 97 0.9951 3.26 0.44 10.1
## 4 47 186 0.9956 3.19 0.40 9.9
## 5 47 186 0.9956 3.19 0.40 9.9
## 6 30 97 0.9951 3.26 0.44 10.1
## quality wine.color
## 1 6 white
## 2 6 white
## 3 6 white
## 4 6 white
## 5 6 white
## 6 6 whiteCombine red and white datasets. Will remove labels later.
wine <- rbind(df1, df2)
str(wine)## 'data.frame': 6497 obs. of 13 variables:
## $ fixed.acidity : num 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
## $ volatile.acidity : num 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
## $ citric.acid : num 0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
## $ residual.sugar : num 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
## $ chlorides : num 0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
## $ free.sulfur.dioxide : num 11 25 15 17 11 13 15 15 9 17 ...
## $ total.sulfur.dioxide: num 34 67 54 60 34 40 59 21 18 102 ...
## $ density : num 0.998 0.997 0.997 0.998 0.998 ...
## $ pH : num 3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
## $ sulphates : num 0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
## $ alcohol : num 9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
## $ quality : int 5 5 5 6 5 5 5 7 7 5 ...
## $ wine.color : chr "red" "red" "red" "red" ...table(wine$wine.color)##
## red white
## 1599 4898Lots of histograms
ggplot(data = wine, aes(x = residual.sugar,
#set levels = c('white','red') to stack white on top of red
fill = factor(wine.color, levels = c('white','red')))) +
geom_histogram(bins= 50, color = 'black')+
scale_fill_manual(name = 'label', values=c('#FEF9E7','#800020'), label = c('red', 'white'),
#set breaks to arrange order of legend labels
breaks = c('red', 'white')) +
theme_bw() #turn background to light color
ggplot(data = wine, aes(x = citric.acid,
#set levels = c('white','red') to stack white on top of red
fill = factor(wine.color, levels = c('white','red')))) +
geom_histogram(bins= 50, color = 'black')+
scale_fill_manual(name = 'label', values=c('#FEF9E7','#800020'), label = c('red', 'white'),
#set breaks to arrange order of legend labels
breaks = c('red', 'white')) +
theme_bw() #turn background to light color
ggplot(data = wine, aes(x = alcohol,
#set levels = c('white','red') to stack white on top of red
fill = factor(wine.color, levels = c('white','red')))) +
geom_histogram(bins= 50, color = 'black')+
scale_fill_manual(name = 'label', values=c('#FEF9E7','#800020'), label = c('red', 'white'),
#set breaks to arrange order of legend labels
breaks = c('red', 'white')) +
theme_bw() #turn background to light color
More scatter plots
ggplot(data = wine, aes(x = citric.acid, y = residual.sugar)) +
geom_point(shape = 1, aes(color = wine.color)) +
scale_color_manual(name = 'label', values=c('#800020', '#FEF9E7')) +
theme_dark()
ggplot(data = wine, aes(x = volatile.acidity, y = residual.sugar)) +
geom_point(shape = 1, aes(color = wine.color)) +
scale_color_manual(name = 'label', values=c('#800020', '#FEF9E7')) +
theme_dark()
Kmeans clustering
clus.data <- wine[c(-13)]
head(clus.data)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.4 0.70 0.00 1.9 0.076
## 2 7.8 0.88 0.00 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.70 0.00 1.9 0.076
## 6 7.4 0.66 0.00 1.8 0.075
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 11 34 0.9978 3.51 0.56 9.4
## 2 25 67 0.9968 3.20 0.68 9.8
## 3 15 54 0.9970 3.26 0.65 9.8
## 4 17 60 0.9980 3.16 0.58 9.8
## 5 11 34 0.9978 3.51 0.56 9.4
## 6 13 40 0.9978 3.51 0.56 9.4
## quality
## 1 5
## 2 5
## 3 5
## 4 6
## 5 5
## 6 5set.seed(123)
wine.cluster <- kmeans(clus.data, centers = 2)
print(wine.cluster$centers)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.619044 0.4079451 0.2911080 3.082690 0.0656846
## 2 6.904698 0.2871364 0.3398094 7.259286 0.0486092
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates
## 1 18.43735 63.54832 0.9945680 3.255147 0.5718655
## 2 39.82503 155.90101 0.9947956 3.190308 0.5000354
## alcohol quality
## 1 10.79529 5.809204
## 2 10.25832 5.825436print(wine.cluster)## K-means clustering with 2 clusters of sizes 2825, 3672
##
## Cluster means:
## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.619044 0.4079451 0.2911080 3.082690 0.0656846
## 2 6.904698 0.2871364 0.3398094 7.259286 0.0486092
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates
## 1 18.43735 63.54832 0.9945680 3.255147 0.5718655
## 2 39.82503 155.90101 0.9947956 3.190308 0.5000354
## alcohol quality
## 1 10.79529 5.809204
## 2 10.25832 5.825436
##
## Clustering vector:
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [35] 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 2 1 1 1 2 1 1 1 1 1 1
## [69] 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 2 1 2 2 2 1 1 1 1 1 1 1 1 1
## [103] 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1
## [137] 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 2 2 1 1 1 1 1
## [171] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 2 1 1 2 1 1 1 1 1 2 1 1
## [205] 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [239] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [273] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [307] 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1
## [341] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [375] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1
## [409] 1 1 1 1 1 1 2 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [443] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1
## [477] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1
## [511] 1 1 1 1 1 2 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [545] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [579] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [613] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1
## [647] 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1
## [681] 1 1 1 1 2 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [715] 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1
## [749] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1
## [783] 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [817] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [851] 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [885] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1
## [919] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [953] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1
## [987] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1
## [1021] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1055] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1
## [1089] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1123] 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1157] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1
## [1191] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1225] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1259] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1293] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1327] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1361] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1
## [1395] 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1
## [1429] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1463] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1
## [1497] 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1531] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1
## [1565] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1599] 1 2 2 1 2 2 1 2 2 2 2 1 1 1 2 2 2 1 1 2 2 1 1 2 2 2 2 2 2 2 2 2 1 1
## [1633] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 1 2 2 2
## [1667] 2 1 1 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2
## [1701] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2
## [1735] 2 2 2 1 1 1 2 1 1 1 2 2 1 1 2 2 2 2 1 1 2 2 2 2 2 2 2 1 2 2 2 2 1 2
## [1769] 1 2 1 1 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1803] 2 2 2 2 2 1 2 1 2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1837] 2 2 2 2 1 1 2 2 2 2 1 1 2 2 2 2 1 2 2 1 1 2 1 1 2 1 2 2 2 2 2 2 2 2
## [1871] 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2
## [1905] 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 1 1 2 1 2 1 1 1 2 2
## [1939] 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 1 2 2 2
## [1973] 2 1 1 2 1 2 2 1 2 2 2 2 1 2 2 2 2 2 1 2 1 2 2 1 1 2 1 1 2 1 2 2 2 1
## [2007] 2 2 1 2 2 1 1 2 2 1 2 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 1 1 2 2 2 1 1
## [2041] 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 1 2 2 2 2 1 2 1
## [2075] 2 1 2 2 2 2 1 2 2 2 2 2 1 2 2 1 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [2109] 2 2 2 2 1 2 2 2 2 1 1 2 2 1 1 1 2 1 2 1 1 1 2 2 2 2 2 2 2 1 2 2 2 2
## [2143] 2 2 2 2 1 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 2 2 2
## [2177] 2 1 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 1 2
## [2211] 1 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [2245] 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1
## [2279] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 1 2 2 2 1 2 2 2
## [2313] 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2
## [2347] 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 1 2 2 2 1
## [2381] 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [2415] 2 1 2 2 2 1 2 1 2 1 2 2 2 1 1 2 2 1 1 2 1 2 2 2 2 2 2 2 1 2 2 2 1 2
## [2449] 1 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 1 2 2 2 1 1 2 2 1 2 1 2 2 2
## [2483] 2 2 1 2 1 2 1 2 2 2 2 2 1 2 1 1 2 2 2 2 2 2 1 1 2 2 2 2 2 2 1 1 1 2
## [2517] 2 1 1 2 2 2 2 2 1 1 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 1 2 2 1 2 2 1 1 2
## [2551] 2 1 2 2 2 2 1 1 2 2 1 2 2 2 1 2 2 1 1 1 1 2 1 2 1 2 2 2 1 1 2 1 1 2
## [2585] 2 2 2 2 2 2 1 2 1 2 2 1 2 2 2 1 2 2 2 2 1 2 1 2 2 1 2 1 2 2 2 2 1 2
## [2619] 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 1 1 2 1 1 2 1 1 1 1 1 2 2 2 1
## [2653] 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [2687] 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1 1 2 1 1 1 1 2 1 2 1
## [2721] 2 1 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 2
## [2755] 2 2 2 2 2 2 2 2 2 1 2 1 2 1 2 2 2 2 1 2 2 2 2 2 1 2 2 1 2 2 2 2 1 2
## [2789] 1 2 2 1 2 2 2 2 2 1 1 1 1 2 1 1 2 2 2 2 2 1 2 2 1 1 2 1 2 2 1 2 2 2
## [2823] 2 1 1 1 1 1 1 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 1 2
## [2857] 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1
## [2891] 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2
## [2925] 2 2 2 1 1 1 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1
## [2959] 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 2 1 1
## [2993] 1 2 1 2 2 2 1 2 2 2 2 1 2 1 1 2 2 1 1 2 2 2 1 2 2 2 1 2 1 1 2 2 2 2
## [3027] 2 1 1 2 1 1 1 2 1 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2
## [3061] 2 2 2 2 2 2 1 1 2 2 2 1 2 1 2 2 1 2 2 2 2 2 1 2 2 1 2 2 2 2 1 2 2 2
## [3095] 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2
## [3129] 2 2 2 2 1 2 2 1 2 1 1 2 1 1 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 1 2
## [3163] 2 1 2 2 1 2 2 2 2 2 2 1 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2
## [3197] 2 1 1 2 2 1 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 1 2
## [3231] 1 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2
## [3265] 2 1 2 1 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3299] 2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 1 2 2 2 1 2 2
## [3333] 2 2 2 1 2 1 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 1 2 2 2 1 2 2 2 1 1 2 2 2
## [3367] 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3401] 1 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 1 1 2 2 1 2 2 2 2 2 2 2 2 2
## [3435] 2 1 1 2 1 2 1 2 2 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2
## [3469] 2 2 2 2 2 2 2 2 1 2 1 2 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2
## [3503] 1 2 2 2 2 1 2 2 1 2 2 2 1 2 1 2 2 2 2 2 1 1 1 1 2 2 2 2 2 1 2 1 2 2
## [3537] 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1 1 2 1 2 2 2 2 2 2 2 2
## [3571] 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2
## [3605] 2 2 1 2 1 1 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 1 2 2 1
## [3639] 1 2 1 1 1 2 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2
## [3673] 2 2 2 2 2 2 2 2 1 2 1 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2
## [3707] 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2
## [3741] 2 2 1 2 2 1 2 1 1 1 2 1 1 2 2 1 1 1 1 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2
## [3775] 2 2 2 2 1 2 2 2 2 1 1 1 2 1 2 2 2 2 2 1 2 1 1 2 2 2 2 2 2 2 2 2 2 2
## [3809] 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2
## [3843] 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 2 1 1 1 2 2
## [3877] 1 2 2 1 1 2 2 2 2 2 2 2 1 1 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2
## [3911] 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 1
## [3945] 2 2 1 2 2 2 2 2 1 1 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 1 2 1 1 2 2 2 1 2
## [3979] 2 2 2 1 1 1 2 2 2 1 1 2 2 2 2 2 2 2 1 1 1 1 1 2 2 2 2 1 1 2 1 2 2 2
## [4013] 1 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [4047] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 1 2 2 2 1 2 1 2 2 1 2 2 2
## [4081] 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2
## [4115] 1 2 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 1 2 1 2 2 1 2 2 2 1 2 2 2 2 2
## [4149] 2 2 2 1 2 2 2 2 2 2 2 1 2 1 2 2 2 1 2 2 2 2 1 1 2 2 2 2 2 1 2 2 2 2
## [4183] 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 1 2 1 2 2
## [4217] 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 2 2 2 1 2 2 1 1 1 2 1 2 2 2 1 2 2 2 2
## [4251] 2 1 2 2 2 2 2 2 1 2 2 2 1 1 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2
## [4285] 1 2 2 2 2 2 1 2 2 1 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
## [4319] 2 1 2 1 2 2 2 1 2 2 1 2 2 2 1 2 2 2 1 2 1 2 2 2 1 1 1 2 2 2 2 2 2 2
## [4353] 1 1 2 2 1 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 1 1 1 2 2 2 2 2 2 1
## [4387] 2 2 2 2 2 2 1 2 1 1 2 2 2 2 2 1 1 1 2 2 2 2 2 1 2 1 2 1 1 2 1 2 2 2
## [4421] 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2 1 2 1 1
## [4455] 2 2 1 2 2 2 1 1 1 1 2 2 2 2 1 2 1 2 1 2 1 2 2 2 1 1 1 2 1 2 1 1 1 1
## [4489] 2 2 2 2 2 1 2 2 2 1 2 1 1 2 1 2 2 2 1 1 1 2 2 1 2 1 1 2 2 1 2 1 2 2
## [4523] 2 2 2 1 2 2 2 2 1 2 2 1 1 1 2 1 1 2 1 2 1 2 2 1 1 2 2 1 1 1 1 1 2 1
## [4557] 1 1 1 2 2 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 1 2 2
## [4591] 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 1 1 1 1 1 1 2 2 1 1 1 2 1 1
## [4625] 2 2 2 2 2 2 2 1 2 2 1 2 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 1 2 1 2 2
## [4659] 1 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 1 2 2 1 2 1 2 1 1 1 1 1 2 1 1 1 2 2
## [4693] 2 1 1 1 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 2 1 1 2 1 2 2 1 1 2 2 2
## [4727] 1 1 2 2 2 2 2 2 2 1 2 2 2 2 1 2 1 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 1
## [4761] 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 1 2 1 1 1 1 2 1 1 2 1 2 2 2 2 2 1 2 1
## [4795] 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1 2 2 2 1 1 2 1 1 1 1 1 2 2 2 1 2 2 2
## [4829] 2 2 1 1 1 2 2 2 2 2 1 2 2 2 1 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 1 2
## [4863] 1 2 2 1 2 2 2 2 1 1 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 2 1 2 2 2
## [4897] 2 2 1 1 1 1 1 2 1 2 2 2 1 2 2 1 1 2 1 1 1 1 2 2 1 1 1 2 2 2 1 2 2 2
## [4931] 2 2 2 1 2 2 2 1 1 2 1 1 2 2 2 2 2 1 1 2 1 1 1 2 2 2 1 1 1 1 1 2 1 1
## [4965] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 1 2 2 2 2 2 2
## [4999] 1 2 2 2 1 1 2 1 2 2 1 2 2 2 2 2 2 2 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2
## [5033] 1 1 1 1 2 2 2 1 2 1 1 2 2 2 1 2 2 1 1 2 1 1 1 2 2 2 2 1 2 2 1 2 2 2
## [5067] 2 1 2 2 1 2 1 2 2 1 2 2 1 1 2 1 1 1 2 1 2 1 1 2 1 2 2 2 1 2 2 2 2 2
## [5101] 2 2 1 2 2 2 2 2 2 2 1 2 1 1 1 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2
## [5135] 2 1 1 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 2 1 1 1 2 1 1 1 1 1 1
## [5169] 1 1 1 1 1 2 2 2 2 2 1 2 2 1 1 1 1 2 2 2 2 1 2 2 2 1 2 2 2 2 2 1 2 2
## [5203] 2 1 2 2 1 2 2 2 1 2 2 2 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 1 1
## [5237] 2 1 1 2 2 2 2 2 2 1 1 2 2 2 2 1 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 2
## [5271] 1 1 1 2 2 1 2 1 1 2 2 2 2 2 2 2 1 2 2 2 2 1 2 1 2 2 2 1 2 2 2 2 2 2
## [5305] 2 1 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 1 1 2 2
## [5339] 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2
## [5373] 1 2 1 2 1 1 1 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 2 2 1 1 2 1 1 1 1
## [5407] 1 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 2 1 2 1 2 2 2 2 2 2 1 1 2 2 1
## [5441] 2 2 1 1 2 1 2 1 1 1 2 1 2 2 2 1 1 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2
## [5475] 1 2 1 2 1 2 2 2 2 2 1 2 1 2 2 1 2 2 1 2 1 2 2 2 2 1 1 1 1 1 1 1 1 2
## [5509] 2 2 2 1 1 2 1 2 2 2 1 2 2 1 1 1 1 1 1 2 2 1 1 2 1 1 2 1 2 1 2 2 2 1
## [5543] 2 2 2 2 2 1 2 2 2 1 2 2 1 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2
## [5577] 2 2 2 2 2 2 1 2 1 1 2 2 1 2 1 1 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 1 2 2
## [5611] 2 2 2 2 1 2 2 2 1 1 1 2 2 2 1 2 2 1 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2
## [5645] 2 2 2 2 1 2 1 2 2 2 1 2 2 2 1 1 1 2 2 1 2 2 2 2 2 2 1 2 1 1 2 2 1 1
## [5679] 1 2 2 2 2 1 1 1 1 1 2 2 1 1 1 1 1 2 1 2 1 1 2 2 1 1 2 2 2 1 2 2 1 1
## [5713] 1 1 1 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2
## [5747] 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 1 2 1 2 2 2 2 2 2 2
## [5781] 1 2 1 1 2 1 2 2 1 1 2 1 1 1 1 1 2 2 1 1 1 2 2 2 1 2 2 1 1 2 2 2 1 2
## [5815] 2 2 2 2 2 1 2 2 1 1 2 2 1 2 2 2 1 1 2 2 2 2 1 2 1 2 2 2 1 2 2 1 2 2
## [5849] 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2
## [5883] 1 1 1 1 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 1 2 2 1
## [5917] 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 1 1 2 2 2 1
## [5951] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 1 2 2 2 1 2 2 2 2 2 1 2
## [5985] 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 1 2 2 2 2 2
## [6019] 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2
## [6053] 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 1 1 1 2 2 2 1 2 2 2 2 2 2 1 2 1 1
## [6087] 1 2 2 2 1 1 2 1 2 1 2 1 1 2 2 2 1 2 2 1 1 2 1 2 1 1 1 2 2 1 1 1 2 2
## [6121] 2 1 2 2 2 2 1 1 2 2 2 2 2 1 2 2 1 2 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1
## [6155] 1 2 2 2 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 1 2 1 1 1 2 2 2 2 2 1 2 2 1 2
## [6189] 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 1 1 2 1 1 1 1 2 2 2 2 1 2 2 2 2 1 2
## [6223] 2 1 2 2 1 1 1 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 2 1 1 2 2 1 2 2 2
## [6257] 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1 1 2 2 2 2 2 2 2 2
## [6291] 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 1 2 1 2 2 1 2 1 1 2 2 1 1 1 2 2 1
## [6325] 2 1 2 2 1 1 2 2 2 1 1 1 2 2 1 2 2 2 2 1 2 2 1 2 2 2 2 1 1 1 1 2 2 2
## [6359] 2 2 2 1 2 2 1 2 2 2 2 2 2 2 1 2 2 2 1 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2
## [6393] 2 2 2 2 1 2 2 2 2 2 2 1 1 2 1 1 2 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2
## [6427] 1 2 1 2 2 2 1 1 1 1 2 2 1 2 2 1 2 2 1 2 1 2 2 2 2 1 1 1 2 2 2 2 2 2
## [6461] 1 1 1 2 1 2 1 2 1 2 1 2 2 2 1 2 1 1 2 2 2 2 2 2 2 2 1 2 2 1 2 2 1 2
## [6495] 2 1 1
##
## Within cluster sum of squares by cluster:
## [1] 3296871 5297991
## (between_SS / total_SS = 62.6 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"##See cluster by scatter plot of resid sugar vs total sulfur
ggplot(data = clus.data, aes(x = total.sulfur.dioxide, y = residual.sugar)) +
geom_point(shape = 1, aes(color = as.factor(wine.cluster$cluster))) +
scale_color_manual(name = 'label', values=c('#800020', '#FEF9E7')
#,labels = c('red', 'white')
) +
theme_dark()
Evaluate clustering
table(wine$wine.color, wine.cluster$cluster)##
## 1 2
## red 1515 84
## white 1310 3588prop.table(table(wine$wine.color, wine.cluster$cluster), margin=2)##
## 1 2
## red 0.53628319 0.02287582
## white 0.46371681 0.97712418There is a lot more noise in cluster 1 than in cluster 2. It’s easier to classify into white wine.
Now check cluster plot
clusplot(clus.data, clus = wine.cluster$cluster, diss = FALSE,
main = 'cluster plot', lines = 0, labels = 5, shade = TRUE, color = TRUE)