---
title: "Untitled"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(ggplot2)
library(dplyr)
library(plotly)
library(tidyr)
library(readr)
```

```{r}
setwd("E:/Masters course content/Semester 2/Data visualization/Assignment 3/CLimate change")
data1 <- read.csv("climate-change.csv")
data1 <- na.omit(data1)
```

tab
================================================================================

Column {data-width=500}
--------------------------------------------------------------------------------

### Chart A

```{r}
library("lubridate")
data1 <- data1 %>%
  mutate(Date = as.Date(Date, format = "%d/%m/%Y")) %>%
  filter(Date >= as.Date("1990-01-01") & Date <= as.Date("2023-12-31")) %>%
  group_by(Year = lubridate::year(Date)) %>%
  summarise(Mean_Temperature = mean(monthly_sea_surface_temperature_anomaly))
```

```{r}
# Create an interactive chart
temperature_chart <- ggplot(data1, aes(x = Year, y = Mean_Temperature)) +
  geom_line() +
  labs(title = "Global Warming: Monthly Temperature Anomaly", x = "Month", y = "Temperature Anomaly")

# Convert the ggplot chart to a plotly chart for interactivity
temperature_chart <- ggplotly(temperature_chart)
```

```{r}
temperature_chart
```

Column {data-width=500}
-----------------------------------------------------------------------

### Chart B

```{r include=FALSE}
data2 <- read.csv("co-emissions-per-capita.csv")
data2 <- na.omit(data2)

# Convert the 'Year' column to numeric (if it's not already)
data2$Year <- as.numeric(data2$Year)

# Filter the data for years between 1990 and 2023
filtered_data <- data2[data2$Year >= 1990 & data2$Year <= 2023, ]

names(filtered_data)[3] <- "Co2Emission"
```


```{r include=FALSE}
# Calculate the mean CO2 emissions for each country over the years 1990-2021
mean_emissions_by_country <- filtered_data %>%
  group_by(Entity) %>%
  summarise(Mean_Emissions = mean(Co2Emission))

top_10_emitters <- mean_emissions_by_country %>%
  arrange(desc(Mean_Emissions)) %>%
  slice_head(n = 10)

# View the result
top_10_emitters
```


```{r}
# Create an interactive line chart for the top 10 countries with a y-axis range of 0-100
line_chart <- filtered_data %>%
  plot_ly(
    x = ~Year,
    y = ~Co2Emission,
    color = ~Entity,
    type = "scatter",
    mode = "lines",
    line = list(width = 2)
  ) %>%
  layout(
    title = "Top 10 Countries - CO2 Emissions per Capita Over Time (1990-2021)",
    xaxis = list(title = "Year"),
    yaxis = list(title = "CO2 Emissions (per capita)", range = c(0, 100)),
    legend = list(orientation = "h")
  )

# Display the line chart
line_chart


```

```{r}
# List of countries to include
included_countries <- c(
  "Qatar", "Kuwait", "Curacao", "United Arab Emirates", "Bahrain",
  "Trinidad and Tobago", "Luxembourg", "United States", "Brunei", "Australia"
)

filtered_data <- filtered_data %>%
  filter(Entity == included_countries)

filtered_data
```

```{r}

# Filter the data to include only the specified countries
filtered_data <- filtered_data %>%
  filter(Entity %in% included_countries)

# Create an interactive line chart for the specified countries
line_chart <- filtered_data %>%
  plot_ly(
    x = ~Year,
    y = ~Co2Emission,
    color = ~Entity,
    type = "scatter",
    mode = "lines",
    line = list(width = 2)
  ) %>%
  layout(
    title = "Top 10 Countries - CO2 Emissions per Capita Over Time (1990-2021)",
    xaxis = list(title = "Year"),
    yaxis = list(title = "CO2 Emissions (per capita)"),
    legend = list(orientation = "h")
  )

# Display the line chart
line_chart
```

tab
================================================================================

Column {data-width=500}
-----------------------------------------------------------------------

### Chart C

```{r include=FALSE}
data3 <- read.csv("global.csv", encoding = "UTF-8")
data3 <- na.omit(data3)
```

```{r}
bar_chart <- data3 %>%
  plot_ly(
    x = ~Year,
    y = ~`Gas.production...TWh`,
    type = 'bar',
    name = 'GasProduction_TWh'
  ) %>%
  add_trace(
    x = ~Year,
    y = ~`Coal.production...TWh`,
    type = 'bar',
    name = 'CoalProduction_TWh'
  ) %>%
  add_trace(
    x = ~Year,
    y = ~`Primary.energy.consumption...TWh`,
    type = 'bar',
    name = 'PrimaryEnergyConsumption_TWh'
  ) %>%
  layout(
    title = "Changes in Energy Production and Consumption Over Time",
    xaxis = list(title = "Year"),
    yaxis = list(title = "Production/Consumption - TWh"),
    barmode = 'group'  # 'group' displays bars side by side, 'stack' would stack them
  )

# Display the interactive bar chart
bar_chart
```