data <- read.table("/Users/admin/Desktop/data.txt", header=TRUE, dec=",", sep="\t", row.names=1)
#We do not include the Gender feature since it binary so that we do not complicate the calculations.
my_data <- data[,-8]
colnames(my_data)

sum_1 = summary(my_data$Prambanan)
mean_1 = mean(my_data$Prambanan)
quartiles_1<-quantile(my_data$Prambanan, probs = c(0.25,0.75))
first_quartile <- quartiles_1[1]
# or first_quartile = sum_1[2]
third_quartile <- quartiles_1[2]
# or third_quartile = sum_1[4]
median_1 = median(my_data$Prambanan)
#or median_1 = sum_1[3]
sd_1 = sd(my_data$Prambanan)


sum_2 = summary(my_data$Azul_Beach_Club)
mean_2 = mean(my_data$Azul_Beach_Club)
quartiles_2<-quantile(my_data$Azul_Beach_Club, probs = c(0.25,0.75))
first_quartile_2 <- quartiles_2[1]
third_quartile_2 <- quartiles_2[2]
median_2 = median(my_data$Azul_Beach_Club)
sd_2 = sd(my_data$Azul_Beach_Club)

boxplot(my_data)
#The most favourable place is Gili_Meno_Beach because median is the highest, 6.
#50% of people voted between 8 and 4.
#On the contorary, the least favourable places are Azul_Beach_Club, Prambanan and GiliTrawangan_FreeDive with 2.
# It is relatively obvious for the most of people in case of Prambanan since the scale is from ~1.5 to 4.
#The highest varience is in GiliTrawangan_FreeDive.
#However, for the other places, variance is the highest. Meaning that, there is not a significant agreement among people voted.
#On the other hand, the there is one person that voted "significantly high" that we can see as 10 and 9 in Bali_Safari and Prambanan respectively.
#Those are outliers.
x <- my_data$Prambanan
y <- my_data$Azul_Beach_Club
plot(x, y)


cor ( my_data, method="spearman" )
#I chosed Spearman because it  analyses not only linear but also monotone vector based on rankings.
library(corrgram)
corrgram(my_data)
corrgram(my_data, upper.panel=panel.conf, lower.panel=panel.pie)
#The bigger percent of the coloured pie is, the higher correlation they have.
corrgram(my_data, upper.panel=panel.conf, lower.panel=panel.ellipse)
#The confidence ellipse is more circular when two variables are uncorrelated.
#Correlation efficient is plus if it raises from left to right. Accordingly, if it is negative it descends from left to right.
#Logistic regression: Gender and Prombanan would not have correlation. p-value ≈ 0.682135 so p-value>5%
#Logistic regression: Gender and Azul_Beach_Club have correlation. p-value ≈ 0.005184 so p-value>5%


#We do not need to scale the data because all the entities are in the same scale.
#Latent components are ordered based on the importance/proportion of their variance.
#If we want a 2D graph, the most wise option would be to choose Comp1 and Comp2 with approximately 85% of Cumulative Proportion.
PCA_result <- princomp(my_data)
biplot(PCA_result)
summary(PCA_result)
PCA_result$loadings
#Here we can also realise that the first components have a relations with each of the features.


#we will be using "euclidean" becauase there is almost no outlier and we do not care how different is the distribution of the entities.
distance_matrix <- dist(my_data, method="euclidean")
map <- cmdscale(distance_matrix)
plot(map)
text(map, pos = 4, labels = row.names(my_data))
my_data[2,]
my_data[6,]
my_data[75,]
MDS_gof <- cmdscale(distance_matrix, eig=TRUE)$GOF
MDS_gof
#GOF ≈ 80%. We have almost 80 percent of information used for MDS.
#We see 3 different clusters because of polarisations, including 2 extremes.
#TIP: Look at middle points of clusters to deduce their identity.
#Maybe look at extreme of x and y to identify the osy.

library(plotly)
hc <- hclust(distance_matrix, method="average")
my_dendrogram <- as.dendrogram(hc)
plot(my_dendrogram, horiz=T)
png("/Users/admin/Desktop/my_hc.png", width=10, height=19, units="in", res=600)
plot(my_dendrogram, horiz=T)
dev.off()