Code Happiest Countries.Rmd

---
title: "Happiest Countries"
author: "Rory Quinlan"
output: github_document
---
### Set Up
```{r,warning=F,message=F}
library(tidyverse)
library(dplyr)
library(factoextra)
library(cluster)
library(gridExtra)

# Load data
happiness_0 = read_csv("happiness_2018.csv", show_col_types = FALSE)

happiness_1 = happiness_0 %>% 
 mutate(Corruption = as.numeric(`Perceptions of corruption`)) 

# Remove NA values
happiness_2 = happiness_1 %>% drop_na()
dim(happiness_1)


happiness_1 = happiness_2 %>% 
 select(-c(`Overall rank`, `Country or region`,`Perceptions of corruption`)) 
```

### Explore Data
```{r}
# Correlogram
pairs(happiness_1[,1:4], pch = 19)
```

### PCA

```{r}
# Create A PCA
pc.happiness = happiness_1 %>% 
 select(-Score) %>% 
 prcomp(scale=TRUE)
pc.happiness
```

```{r}
# Visualize top contributors
fviz_contrib(pc.happiness,choice="var", axes=1, top=5)
```

```{r,warning=F}
# Graph Explantory power of variables
PRVar<- pc.happiness$sdev^2
PVE<- PRVar[1:9]/sum(PRVar)

PC=1:9
data=data.frame(PC, PVE)
ggplot(data=data, aes(x=PC, y=PVE))+
 geom_line(color="navy")+
 geom_point(aes(x=3,y=0.1),cex=5,color="orange",alpha=0.3)+
 geom_point(color="red",cex=2)+
 labs(title="Proportion of Variance Explained", x="Principal Component",y="pve")+
 scale_x_continuous(breaks = 1:9)
```

```{r}
# View scatterplot of greatest contributors from PCA
PC12 <- pc.happiness$x %>% as_tibble() %>% select(1:2)
pc.happiness$x %>% as_tibble() %>% select(PC1, PC2) %>%
 bind_cols(happiness_1) %>% 
 ggplot(aes(x = `PC1`, y = `PC2`)) + geom_point()
```

```{r}
# Find optimal clusters
happiness_4 = scale(happiness_1[,-1])
fviz_nbclust(PC12, kmeans, method = "gap_stat")
```

```{r}
km_mod = kmeans(happiness_4, centers=3)
pam_mod = pam(happiness_4, 3)
```


```{r}
# Create variable for cluster number in df
PC12_cluster = happiness_1 %>% mutate(cluster=factor(pam_mod$cluster))
```

```{r}
# Compare kmean and pam
p1<-fviz_cluster(km_mod, data = happiness_4)

p2<- fviz_cluster(pam_mod, data = happiness_4)

grid.arrange(p1, p2, ncol=2)

```

```{r}
# Create clusters with country name
PC12_cluster <- PC12 %>% mutate(cluster = factor(pam_mod$cluster), countryOrRegion = factor(happiness_2$`Country or region`))


```

```{r}
# View cluster diparity
p1<-ggplot(PC12_cluster,aes(x=cluster,y=PC1,color=cluster)) + 
  geom_boxplot()+ labs(title="GDP per Capita by Cluster")

# Boxplot
p2<-ggplot(PC12_cluster,aes(x=cluster,y=PC2 ,color=cluster)) + 
  geom_boxplot()+ labs(title="Healthy Life Expectancy by Cluster")

grid.arrange(p1, p2, ncol = 2)
```

```{r}
# Filter and display the happiest countries
as.data.frame(PC12_cluster %>% filter(cluster == 1) %>% select(countryOrRegion))

```