---
title: "Viz Dash"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse);
library(heatmaply);
library(visdat); # for exploring missing data structure
library(ggplot2);
library(naniar);
library(dplyr);
library(tidyr);
library(hrbrthemes);
library(ggchicklet);
library(ggalt);
library(corrplot); #correlogram
library(plotly);
library(cowplot);
library(patchwork);
library(rworldmap);
library(RColorBrewer);


getwd()
setwd("C:/Users/farhat/OneDrive/Documents/R/farzana_assignment/dataviz_assignment")
#read data
df_2021 <- read.csv("data/world-happiness-report-2021.csv")
df_all <- read.csv("data/world-happiness-report.csv")

```
try1
=======================================================================
ROW
-----------------------------------------------------------------------

### Chart A

```{r, fig.height = 4, fig.width = 10} 
a1 <- vis_dat(df_2021)+ labs(x = "Datatypes for 2021 dataset")
a2 <- vis_dat(df_all)+ labs(x = "Datatypes for all dataset")
a1 + a2
```

```{r}

```
try2
=======================================================================
ROW
-----------------------------------------------------------------------

### Chart C

```{r}
# dimensions
dimensions <- c('ladder_score',
                'logged_GDP_per_capita',
                'social_support',
                'healthy_life_expectancy',
                'freedom_to_make_life_choices',
                'generosity',
                'perceptions_of_corruption')

# map country to regions
country_region_dict = df_2021 %>% 
  select(country = ï..country_name, region = regional_indicator) %>% unique()

df_2021_long <- df_2021 %>% 
  select(country = ï..country_name, all_of(dimensions)) %>%
  mutate(absence_of_corruption = 1- perceptions_of_corruption) %>%
  pivot_longer(cols = c(all_of(dimensions),'absence_of_corruption'),
               names_to = 'dimension', values_to = 'score') %>%
  filter(dimension != "perceptions_of_corruption")

df_2021_tranformed <- df_2021_long %>%
  group_by(dimension) %>%
  mutate(min_value = min(score),
         max_value = max(score)) %>%
  mutate(score_pct = (score-min_value)/(max_value-min_value)) %>%
  ungroup()

# getting top 10
df_2021_top10 <- df_2021_tranformed %>%
  filter(dimension == "ladder_score") %>%
  slice_max(score, n = 10) %>%
  mutate(cat = 'top_10', 
         country_rank = rank(-score),
         country_label = paste0(country, ' (', country_rank, ')'))
# getting bottom 10
df_2021_bottom10 <- df_2021_tranformed %>%
  filter(dimension == "ladder_score") %>%
  mutate(country_rank = rank(score),
         country_label = paste0(country, ' (', country_rank, ')')) %>%
  slice_min(score, n = 10) %>%
  mutate(cat = 'bottom_10')
a5 <- ggplot(df_2021_top10, aes(x = reorder(country_label, score))) + 
  geom_chicklet(aes(y = 10, fill = 4.9), width = 0.5, radius = grid::unit(5, "pt")) +
  geom_chicklet(aes(y = score, fill = score), width = 0.5, radius = grid::unit(5, "pt")) +
  geom_text(aes(y = score), label = round(df_2021_top10$score,2), nudge_y = 0.4, size = 3) + 
  scale_y_continuous(expand = c(0, 0.1), position = "right", limits = c(0, 10)) +
  scale_fill_gradient2(low = 'black', high = '#818aeb', mid = 'white', midpoint = 5) + 
  coord_flip() +
  labs(y="Best possible life = 10", x = '',
       title="Top 10 Happiest Countries in 2021",
       subtitle="Happiest countries in Europe",
       caption="Source: The World Happiness Report 2021") + 
  theme_ipsum(grid = '')  +
  theme(plot.title = element_text(size=15),
        plot.subtitle = element_text(size = 12),
        plot.caption = element_text(size = 10),
        axis.title.x = element_text(size= 10, color = '#555955'),
        axis.text.y = element_text(size = 10, color = 'black'),
        axis.text.x = element_blank(),
        legend.position = 'None')

a6 <- ggplot(df_2021_bottom10, aes(x = reorder(country_label, score))) + 
  geom_chicklet(aes(y = 10, fill = 4.9), width = 0.5, radius = grid::unit(5, "pt")) +
  geom_chicklet(aes(y = score, fill = score), width = 0.5, radius = grid::unit(5, "pt")) +
  geom_text(aes(y = score), label = round(df_2021_bottom10$score,2), nudge_y = 0.4, size = 3) + 
  scale_y_continuous(expand = c(0, 0.1), position = "right", limits = c(0, 10)) +
  scale_fill_gradient2(low = '#074040', high = '#4cc2c2', mid = 'white', midpoint = 5) + 
  coord_flip() +
  labs(y="Best possible life = 10", x = '',
       title="Top 10 Saddest Countries in 2021",
       subtitle="Countries struck by poverty and war",
       caption="Source: The World Happiness Report 2021") + 
  theme_ipsum(grid = '') +
  theme(plot.title = element_text(size=15),
        plot.subtitle = element_text(size = 12),
        plot.caption = element_text(size = 10),
        axis.title.x = element_text(size= 10, color = '#555955'),
        axis.text.y = element_text(size = 10, color = 'black'),
        axis.text.x = element_blank(),
        legend.position = 'None')

a5 + a6
```

### Chart D
This shows top correlated factors to happiness. 
```{r}
df_cor <- df_2021 %>% 
  select(corruption = perceptions_of_corruption,
         generosity = generosity,
         freedom = freedom_to_make_life_choices, 
         life_expectancy = healthy_life_expectancy, 
         social_support = social_support,
         GDP_per_capita = logged_GDP_per_capita, 
         happiness = ladder_score
  )

corr <- cor(df_cor)
plot_ly(colors = "RdBu") %>%
  add_heatmap(x = rownames(corr), y = colnames(corr), z = corr) %>%
  colorbar(limits = c(-1, 1))
```