server.R

library(plotly)
library(tidyverse)

# Read the data
co2_df <- read_csv("www/data/Countries.csv")
cities_df <- read_csv("www/data/CityAnnualTemps.csv")

# Function to get a list of countries from the data-set
get_countries <- function() {
  country_list <- co2_df %>%
    distinct(Country) %>%
    pull(Country)
  return(country_list)
}

# Function to get a list of years from the data-set
get_years <- function() {
  countries <- get_countries()
  
  df <- co2_df %>%
    filter(Country %in% countries)
  
  year_list <- unique(df$dt)
  
  # Initialize min and max value
  min_year <- max(year_list, na.rm = TRUE)
  max_year <- min(year_list, na.rm = TRUE)
  
  # Find the maximum among the minimum years
  min_year <- df %>%
    group_by(Country) %>%
    summarise(min_year_country = min(dt)) %>%
    pull(min_year_country) %>%
    max()
  
  # Find the minimum among the maximum years
  max_year <- df %>%
    group_by(Country) %>%
    summarise(max_year_country = max(dt)) %>%
    pull(max_year_country) %>%
    min()
  
  year_vec <- c(min_year, max_year)
  return(year_vec)
}


# Return a list of all continents.
get_contitents <- function() {
  continents <- c(
    "Africa", "Asia", "Europe", "Oceania", "North America", "South America"
    )
  return(continents)
}

# Create vectors for each continent
africa <- c(
  "Algeria", "Angola", "Benin", "Botswana", "Burkina Faso", "Burundi",
  "Cabo Verde", "Cameroon", "Central African Republic", "Chad", "Comoros",
  "Congo", "Côte d’Ivoire", "Djibouti", "Egypt", "Equatorial Guinea", "Eritrea",
  "Eswatini", "Ethiopia", "Gabon", "Gambia", "Ghana", "Guinea", "Guinea Bissau",
  "Kenya", "Lesotho", "Liberia", "Libya", "Madagascar", "Malawi", "Mali",
  "Mauritania", "Mauritius", "Morocco", "Mozambique", "Namibia", "Niger",
  "Nigeria", "Rwanda", "Sao Tome and Principe", "Senegal", "Seychelles",
  "Sierra Leone", "Somalia", "South Africa", "Sudan", "Tanzania",
  "Togo", "Tunisia", "Uganda", "Zambia", "Zimbabwe"
)

asia <- c(
  "Afghanistan", "Armenia", "Azerbaijan", "Bahrain", "Bangladesh", "Bhutan",
  "Brunei Darussalam", "Cambodia", "China", "China, Hong Kong",
  "North Korea", "South Korea", "India", "Indonesia", "Iran", "Iraq", "Israel",
  "Japan", "Jordan", "Kazakhstan", "Kuwait", "Kyrgyzstan", 
  "Laos", "Lebanon", "Malaysia", "Maldives", "Mongolia", "Myanmar", "Nepal",
  "Oman", "Pakistan", "Palestine", "Philippines", "Qatar", "Saudi Arabia", 
  "Singapore", "Sri Lanka", "Syria", "Taiwan", "Tajikistan", "Thailand", 
  "Timor Leste", "Turkey", "Turkmenistan", "United Arab Emirates", 
  "Uzbekistan", "Vietnam", "Yemen"
)

europe <- c(
  "Åland", "Albania", "Andorra", "Austria", "Belarus", "Belgium", 
  "Bosnia and Herzegovina", "Bulgaria", "Croatia", "Cyprus", "Czechia", 
  "Denmark", "Estonia", "Faroe Islands", "Finland", "France", "Germany", 
  "Greece", "Hungary", "Iceland", "Ireland", "Isle of Man",
  "Italy", "Jersey", "Kosovo", "Latvia", "Liechtenstein", "Lithuania", 
  "Luxembourg", "Malta", "Moldova", "Monaco", "Montenegro", "Netherlands", 
  "North Macedonia", "Norway", "Poland", "Portugal", "Romania", 
  "Russian Federation", "San Marino", "Serbia", "Slovakia", "Slovenia",
  "Spain", "Svalbard and Jan Mayen Islands", "Sweden", "Switzerland", "Ukraine",
  "United Kingdom"
)

north_america <- c(
  "Bermuda", "Canada", "Greenland", "Saint Pierre and Miquelon", 
  "United States", "Mexico"
)

oceania <- c(
  "American Samoa", "Australia", "Christmas Island", "Fiji", "French Polynesia",
  "Guam", "Kiribati", "Federated States Of Micronesia", "New Caledonia",
  "New Zealand", "Niue", "Northern Mariana Islands",
  "Palau", "Papua New Guinea", "Samoa", "Solomon Islands", "Tonga",
  "Virgin Islands"
)

south_america <- c(
  "Argentina", "Bolivia", "Brazil", "Chile", "Colombia", "Ecuador",
  "Guyana", "Paraguay", "Peru", "Suriname", "Uruguay", "Venezuela"
)

# Function to create a table of co2 vs temperature increases
temp_vs_co2_increase <- function(continents, min_year, max_year) {
  # Add continents column to DF
  selected_df <- co2_df %>%
    mutate(Continent = ifelse(
      Country %in% asia, "Asia",
      ifelse(
        Country %in% africa, "Africa",
        ifelse(
          Country %in% europe, "Europe",
          ifelse(
            Country %in% north_america, "North America",
            ifelse(
              Country %in% oceania, "Oceania",
              ifelse(
                Country %in% south_america, "South America", NA
              )
            )
          )
        )
      )
    ))
  selected_df <- selected_df %>%
    filter(Continent %in% continents) %>%
    filter(dt %in% as.character(c(min_year: max_year))) %>%
    group_by(Country) %>%
    mutate(co2 = sum(co2, na.rm = TRUE)) %>%
    filter(dt %in% as.character(c(min_year, max_year))) %>%
    mutate(avg_temp_change = ifelse(
      !is.na(AverageTemperature),
      diff(AverageTemperature),
      NA
    )) %>%
    mutate(max_temp_change = ifelse(
      !is.na(MaxAverageTemperature),
      diff(MaxAverageTemperature),
      NA
    )) %>%
    mutate(min_temp_change = ifelse(
      !is.na(MinAverageTemperature),
      diff(MinAverageTemperature),
      NA
    )) %>%
    filter(dt == as.character(max_year)) %>%
    select(dt, Country, Continent,
           avg_temp_change, 
           max_temp_change, 
           min_temp_change,
           co2)
  
  return(selected_df)
}

server <- function(input, output){
  
  # Code for creating a bar chart of C02 by continent
  # Dynamically set choices based on the list of countries
  observe({
    updateSelectInput(
      inputId = "country_name",
      choices = get_contitents(),
      selected = get_contitents()
    )
  })
  
  # Create a reactive that holds the selected countries
  selected_countries_reactive <- reactive({
    input$country_name
  })
  
  
  # For debugging.
  output$selected_countries <- renderPrint({
    selected_countries <- selected_countries_reactive()
    if (length(selected_countries) > 0) {
      paste(selected_countries, collapse = ", ")
    }
  })
  
  # Code for creating a map of C02 by year
  # Dynamically set slider choices based on available years
  observe({
    updateSliderInput(
      inputId = "selected_year",
      label = "Select Year",
      min = 1856,
      max = 2013,
      value = c(1856, 2013),
      step = 1
    )
  })
  
  
  # Create a reactive that holds the selected countries
  selected_year_reactive <- reactive({
    input$country_year
  })
  
  # For debugging.
  output$selected_years <- renderPrint({
    selected_year <- selected_year_reactive()
    if (!is.null(selected_year)) {
      selected_year
    }
  })
  
  output$co2Table <- renderTable({
    countries <- selected_countries_reactive()
    years <- selected_year_reactive()
    min_year <- years[1]
    max_year <- years[2]
    selected_df <- temp_vs_co2_increase(countries, min_year, max_year) %>%
      arrange(desc(co2)) %>%
      reframe(
        `Year` = as.character(dt),
        Country,
        `Average Temperature Change` = avg_temp_change,
        `Maximum Temperature Change` = max_temp_change,
        `Minimum Temperature Change` = min_temp_change,
        `Total CO2 Emissions` = co2
      )
    return(selected_df)
  })
  
  output$co2_plotly <- renderPlotly({
    countries <- selected_countries_reactive()
    years <- selected_year_reactive()
    min_year <- years[1]
    max_year <- years[2]
    selected_df <- temp_vs_co2_increase(countries, min_year, max_year) %>%
      filter(!(Country %in% get_contitents()))
    
    selected_df$countryID <- factor(
      selected_df$Continent, levels = unique(selected_df$Continent)
    )
    
    co2_plotl <- ggplot(selected_df) +
      geom_col(aes(
        y = co2,
        x = countryID,
        fill = avg_temp_change,
        text = paste0("Region: ", Country, "<br>",
                     "Year: ", min_year, "-", max_year, "<br>",
                     "Average Temperature Change: ", avg_temp_change, "<br>",
                     "Maximum Temperature Change: ", max_temp_change, "<br>",
                     "Minimum Temperature Change: ", min_temp_change, "<br>",
                     "Absoluate CO2 Emissions: ", co2)
      )) +
      labs(title = paste("Climate Change from ", min_year,
                         "to", max_year,"by Continent"),
           fill = "Average <br>Temperature <br>Change (°C)",
           x = "Continent", y = "Total CO2 Emissions") +
      theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
      scale_fill_gradient(low = "darkkhaki", high = "darkgreen")
    co2_plotly <- ggplotly(co2_plotl, tooltip = "text")
    return(co2_plotly)
  })
  
  # # Render the second plot.
  # output$co2_plotly2 <- renderPlotly({
  #   years <- selected_year_reactive()
  #   countries <- get_countries()
  #   min_year <- years[1]
  #   max_year <- years[2]
  #   selected_df <- co2_df %>%
  #     filter(dt %in% as.character(c(min_year: max_year))) %>%
  #     filter(Country %in% countries)
  #   
  #   co2_plot2 <- ggplot(selected_df) +
  #     geom_point(aes(
  #       y = co2_growth_abs,
  #       x = population,
  #       fill = abs_co2,
  #       text = paste0("Region: ", Country, "<br>",
  #                     "Year: ", year, "<br>",
  #                     "CO2 Growth: ", co2_growth_abs, "<br>",
  #                     "Absoluate CO2 Emissions: ", abs_co2)
  #     )) +
  #     labs(title = "Annual CO2 Emissions by Region",
  #          fill = "Absolute CO2 Emmisions",
  #          x = "Population", y = "Absolute Annual CO2 Growth") # +
  #     # scale_x_continuous(limits = c(1840, 2020), breaks = seq(1840, 2020, 20))
  #   co2_plotly2 <- ggplotly(co2_plot2, tooltip = "text")
  #   return(co2_plotly2)
  # })
  # 
}