library(shiny)
library(ggplot2)
library(dplyr)
library(reshape2)

setwd("/Users/janekom/Desktop/uni/THESIS/emissions_data")

file_path <- "/Users/janekom/Desktop/uni/THESIS/emissions_data"

# Create a function to read the files from the directory and then change the col names
update_and_read_data <- function(file_path) {
  data <- read.csv(file_path)
  new_colnames <- c("type_emission", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022")
  colnames(data) <- new_colnames
  return(data)
}

# Create a function to apply the "update_and_read_data" function to all files
companies <- c("roche", "astra_zeneca", "bayer", 
               "genmab", "gsk", "lonza_group", 
               "novartis", "novo_nordisk", "novozymes", 
               "sanofi")

data_list <- list()

# Loop through the companies
for (company in companies) {
  file_path <- paste0(company, "_emissions.csv")
  variable_name <- paste0(company, "_data")
  assign(variable_name, update_and_read_data(file_path))
  data_list[[variable_name]] <- get(variable_name)
}

# Define static colors for each emission type
emission_colors <- c("deeppink2", "darkolivegreen2", "cornflowerblue", 
                     "darkmagenta", "coral", "darkgrey", "yellow", "aquamarine", 
                     "darkseagreen2", "brown1", "skyblue", "darkgreen", 
                     "deepskyblue2", "maroon", "cornsilk")

# Create a mapping between emission types and colors
emission_types <- unique(unlist(sapply(data_list, function(x) x$type_emission)))
emission_color_mapping <- setNames(emission_colors[1:length(emission_types)], emission_types)

# Ui part
ui <- fluidPage(
  titlePanel("Footprint Assessment"),
  sidebarLayout(
    sidebarPanel(width = 3,
                 selectInput(
                   inputId = "company", 
                   label = "Select Company", 
                   choices = companies
                 ),
                 checkboxGroupInput(
                   inputId = "emission_type", 
                   label = "Select Emission Type", 
                   choices = unique(unlist(sapply(data_list, function(x) x$type_emission))))
    ),
    mainPanel(
      plotOutput("barPlot"),
      tableOutput("data_table")
    )
  )
)

# Server part
server <- function(input, output, session) {
  data <- reactive({
    get(paste0(input$company, "_data"))
  })
  
  observe({
    updateCheckboxGroupInput(session, 
                             "emission_type", 
                             choices = unique(unlist(sapply(data_list, function(x) x$type_emission))))
  })
  
  filtered_data <- reactive({
    data_subset <- data()
    emission_types <- input$emission_type
    
    if (length(emission_types) > 0) {
      data_subset <- data_subset[data_subset$type_emission %in% emission_types, ]
    }
    
    data_subset[, c("type_emission", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022")]
  })
  
  output$barPlot <- renderPlot({
    scope_data_long <- melt(filtered_data(),
                            id.vars = "type_emission",
                            variable.name = "year",
                            value.name = "emission")
    
    # Convert year to factor with original order
    scope_data_long$year <- factor(scope_data_long$year,
                                   levels = colnames(filtered_data())[2:ncol(filtered_data())])
    
    # Create a bar plot with different colors for different emission types
    ggplot(scope_data_long, aes(x = year, 
                                y = emission, 
                                fill = type_emission)) +
      geom_bar(stat = "identity") +
      labs(title = paste("Emission Over Years for", input$company),
           x = "Year",
           y = "Emission Value",
           fill = "Emission Type") +
      scale_y_continuous(labels = scales::comma) +
      scale_fill_manual(values = emission_color_mapping) +  # Set colors based on mapping
      theme_minimal()
  })
  
  output$data_table <- renderTable({
    filtered_data()
  })
}

shinyApp(ui, server)