---
title: "ANLY 512 - Data Explorationand Analysis Laboratory"
author: "Shirong Liu"
date: "`r Sys.Date()`"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    horizontal_layout: fill
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(xts)
library(ggplot2)
library(reshape)
library(reshape2)
```

```{r loading data, include=FALSE}
disaster <- read.csv("Climate_Disaster.csv")

disaster_sum <- data.frame(
  group = c("Drought", "Flooding", "Freeze", "Severe Storm", "Tropical Cyclone", "Wildfire", "Winter Storm"),
  value = c(sum(disaster$Drought.Count),
            sum(disaster$Flooding.Count),
            sum(disaster$Freeze.Count),
            sum(disaster$Severe.Storm.Count),
            sum(disaster$Tropical.Cyclone.Count),
            sum(disaster$Wildfire.Count),
            sum(disaster$Winter.Storm.Count)
            )
  )

disaster_avgCost <- data.frame(
  group = c("Drought", "Flooding", "Freeze", "Severe Storm", "Tropical Cyclone", "Wildfire", "Winter Storm"),
  value = c(mean(disaster$Drought.Cost),
            mean(disaster$Flooding.Cost),
            mean(disaster$Freeze.Cost),
            mean(disaster$Severe.Storm.Cost),
            mean(disaster$Tropical.Cyclone.Cost),
            mean(disaster$Wildfire.Cost),
            mean(disaster$Winter.Storm.Cost)
            )
  )
```

# Climate Disaster by Year
## Row {data-width=250}
```{r, fig.width=12, fig.height=4}
ggplot(disaster, aes(x = Year, y = All.Disasters.Count)) + 
  geom_bar(stat = "identity", fill = "seagreen4") + 
  labs(y = "Number of Climate Disaster", title = "Total Climate Disasters 1980-2022") +
  theme_bw()
```

## Row {data-width=250}
```{r, fig.width=12, fig.height=4}
ggplot(disaster, aes(x = Year, y = All.Disasters.Cost)) + 
  geom_bar(stat = "identity", fill = "seagreen4") + 
  labs(y = "Billions of Dollar", title = "Average Cost") +
  theme_bw()

```

# The Most Common and Most Costly
## Row {data-width=250 .tabset .tabset-fade}
Number of Climate Disasters by Type from 1980 to 2022

### Pie Chart
```{r, fig.width=15}
piechart1 <- ggplot(disaster_sum, aes(x="", y=value, fill=group)) + 
  geom_bar(width = 1, stat = "identity") + 
  coord_polar("y", start=0) +
  scale_fill_brewer(palette="Set3") + 
  theme_bw() + 
  theme(axis.title.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.ticks.y=element_blank(),
        legend.title=element_blank())
piechart1
```

### Bar Chart
```{r, fig.width=15}
bar1 <- ggplot(disaster_sum, aes(x = group, y = value)) + 
  geom_bar(stat = "identity", fill = "seagreen4") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + 
  labs(y = "Total Number of Climate Disaster")
bar1
```

## Row {data-width=250 .tabset .tabset-fade}
Average Cost Incurred by Different Types of Climate Disasters from 1980 to 2022

### Pie Chart
```{r, fig.width=15}
piechart2 <- ggplot(disaster_avgCost, aes(x="", y=value, fill=group)) +
  geom_bar(width = 1, stat = "identity") +
  coord_polar("y", start=0) +
  scale_fill_brewer(palette="Set3") +
  theme_bw() +
  theme(axis.title.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.ticks.y=element_blank(),
        legend.title=element_blank())
piechart2
```

### Bar Chart
```{r, fig.width=15}
bar2 <- ggplot(disaster_avgCost, aes(x = group, y = value)) + 
  geom_bar(stat = "identity", fill = "seagreen4") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + 
  labs(y = "Billions of Dollar")
bar2
```

# Severe Storms and Tropical Cyclones
Now that we have identified Severe Storm and Tropical Cyclone as the two most frequent and most costly climate disaster, let's take a closer look at them.

## Severe Storm {data-width=250 .tabset .tabset-fade}
### Frequency
```{r, fig.width=15}
ggplot(disaster, aes(x = Year, y = Severe.Storm.Count)) + 
  geom_bar(stat = "identity", fill = "coral1") + 
  labs(y = "Count of Severe Storms", title = "Severe Storms 1980-2022") +
  theme_bw()
```

### Cost
```{r, fig.width=15}
ggplot(disaster) + 
  geom_bar(aes(x = Year, y = Severe.Storm.Cost), stat = "identity", fill = "darkolivegreen3") +
  geom_line(aes(x = Year, y = Severe.Storm.Lower.75), size = 0.5, color="coral1", group = 1) + 
  geom_line(aes(x = Year, y = Severe.Storm.Upper.75), size = 0.5, color="coral1", group = 1) + 
  theme_bw() + 
  labs(y = "Billions of Dollars", title = "Cost of Severe Storm with Upper and Lower 75% Bounds")

```

## Tropical Cyclone {data-width=250 .tabset .tabset-fade}
### Frequency
```{r, fig.width=15}
ggplot(disaster, aes(x = Year, y = Tropical.Cyclone.Count)) + 
  geom_bar(stat = "identity", fill = "coral1") + 
  labs(y = "Count of Tropical Cyclones", title = "Tropical Cyclones 1980-2022") +
  theme_bw()
```

### Cost
```{r, fig.width=15}
ggplot(disaster) + 
  geom_bar(aes(x = Year, y = Tropical.Cyclone.Cost), stat = "identity", fill = "darkolivegreen3") +
  geom_line(aes(x = Year, y = Tropical.Cyclone.Lower.75), size = 0.5, color="coral1", group = 1) + 
  geom_line(aes(x = Year, y = Tropical.Cyclone.Upper.75), size = 0.5, color="coral1", group = 1) + 
  theme_bw() + 
  labs(y = "Billions of Dollars", title = "Cost of Tropical Cyclone with Upper and Lower 75% Bounds")

```