-
Notifications
You must be signed in to change notification settings - Fork 0
/
TA Metkuan.R
165 lines (119 loc) · 3.48 KB
/
TA Metkuan.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
# Install Library
install.packages("data.table")
install.packages("factoextra")
install.packages("magrittr")
install.packages("dplyr")
install.packages("ggpubr")
install.packages("MASS")
# Library yang digunakan
library(data.table)
library(factoextra)
library(magrittr)
library(dplyr)
library(ggpubr)
library(MASS)
###############################################################################
# Input Data #
WHR <- fread("D:\\Fakhri\\Kuliah\\Jadwal dan Materi Kuliah\\Tahun Kedua\\Semester 4\\Hal Lain\\Metode Kuantitatif\\TA-Metkuan\\2019.csv") # Ganti file path nya
WHR_v2 <- WHR[,c(2:9)]
WHR_Fix <- data.frame(WHR_v2, row.names = 1)
###############################################################################
# PCA #
WHR.pca <- prcomp(WHR_Fix, center=TRUE, scale. = TRUE)
summary(WHR.pca)
pcnya <- predict(WHR.pca, newdata=WHR_Fix)
View(pcnya)
###############################################################################
# Biplot #
WHR.biplot <- biplot(WHR.pca, scale = 0)
###############################################################################
# Cluster Optimal
fviz_nbclust(WHR_v2[,2:8], hcut, method = "silhouette") # Cluster Optimal = 2
###############################################################################
# K-Means #
# K-Means
(kmeans.hasil.pca <- kmeans(WHR.pca$x,2))
plot(WHR.pca$x, col = kmeans.hasil.pca$cluster)
points(kmeans.hasil.pca$centers[,c("PC1", "PC2")], col = 1:3, pch = 8, cex = 2)
###############################################################################
# MDS #
# Compute MDS
# Metode 1
mds <- WHR_Fix %>%
dist() %>%
cmdscale() %>%
#isoMDS() %>%
#sammon() %>%
#.$points %>%
as_tibble()
colnames(mds) <- c("Dim.1", "Dim.2")
# Metode 2
mds <- WHR_Fix %>%
dist() %>%
#cmdscale() %>%
isoMDS() %>%
#sammon() %>%
.$points %>%
as_tibble()
colnames(mds) <- c("Dim.1", "Dim.2")
# Metode 3
mds <- WHR_Fix %>%
dist() %>%
#cmdscale() %>%
#isoMDS() %>%
sammon() %>%
.$points %>%
as_tibble()
colnames(mds) <- c("Dim.1", "Dim.2")
## PENTING: Lakukan untuk setiap metode ##
# Plot MDS
# K-means clustering
clust <- kmeans(mds,2)$cluster %>%
as.factor()
mds <- mds %>%
mutate(groups = clust)
# Plot and color by groups
ggscatter(mds, x = "Dim.1", y = "Dim.2",
label = rownames(WHR_Fix),
color = "groups",
palette = "jco",
size = 1,
ellipse = TRUE,
ellipse.type = "convex",
repel = TRUE)
###############################################################################
# Hierarchical Clustering #
# Jarak antar data
data_jarak = dist(WHR_v2[,2:8])
data_jarak
# Perbandingan korelasi antar metode hirarki
# Single #
hc = hclust(data_jarak,"single")
d2 = cophenetic(hc)
cor.sing = cor(data_jarak,d2)
cor.sing
# Average #
hc = hclust(data_jarak,"ave")
d2 = cophenetic(hc)
cor.ave = cor(data_jarak,d2)
cor.ave
# Complete #
hc = hclust(data_jarak,"complete")
d2 = cophenetic(hc)
cor.comp = cor(data_jarak,d2)
cor.comp
## PENTING: Menggunakan metode average karena nilainya lebih besar ##
# Analisis cluster dgn hirarki average
hirarki.ave = hclust(data_jarak, method = "ave")
hirarki.ave
# Dendogram
plot(hirarki.ave, labels = WHR_v2$`Country or region`)
# mengelompokkan data pada dendogram (k=2)
rect.hclust(hirarki.ave, k=2, border = 2:3)
# Anggota cluster
# Average #
hasil.cut = cutree(hirarki.ave,2)
table(hasil.cut)
rownames(WHR_v2)[hasil.cut==1]
rownames(WHR_v2)[hasil.cut==2]
###############################################################################