combined_code.txt

## File: app.R 
 # Source the UI and server components
source("ui.R")
source("server.R")

# Run the app
shinyApp(ui = ui, server = server)


## File: data.R 
 ## data.R

#########################################
###
### Data manipulation within this section:
### 
### >>> Census data
###     > Further crops processing
###     > Animal summary processing
### >>> Emissions data
### >>> 
### >>> 2023 census module data
###
### --------------------------------
###
### How to update data:
###
### Individual instructions are available for each section. 
###
### Inputted will need to be in the same format as previous iterations unless
### code is edited to adapt this. 
###
### Current data within the section primarily comes from the publicly available publication tables.
###
### Download the most recent tables from the publication, add them to the project directory, and modify the file path.
###
### Data processing is designed to be robust, but small changes within publications will likely lead to bugs needing fixed
###
### Ensure when updating census information that the all subsections are updated below.
###
#########################################

# Libraries:


library(readxl)
library(openxlsx)
library(dplyr)
library(tidyr)


#########################################
###
###
### Census Data: 
###
### Xlsx file originates from the published data tables in supporting documents
### 2023 version: https://www.gov.scot/publications/results-scottish-agricultural-census-june-2023/documents/
###
### To update, download the excel document and insert file into compendium working directory / file
### Insert file path in the file_path below.
### Check table_names for changes vs. the previous year's names and order
### If the order is incorrect, change the table number accordingly
###
### Try to avoid renaming tables below unless necessary as these are referenced throughout the app
### If changing any names, ensure all sourcing of the data matches changing (print_code.R can help to QA this)
###
### The data processing was designed for the 2023 census tables, and changes to tables may cause bugs
### Processing aims to standardise all tables to make them compatible with the different visualisation modules
### This includes removing the metadata above the tables, renaming headers, removing problematic trailing spaces, etc.
###
### Additional processing can be included if necessary (e.g. 2022 poultry removed to emphasise change in methodology)
### Some data processing could be moved from within the server pages in the app to speed up loading & improve efficiency
###
### To update, run the code below.
### QA results before saving as RData
### RData is then loaded within options.RData
###
### Ensure the rest of the census code is ran subsequently after updating the base tables.
###
#########################################

# Define file path
file_path <- "June+Agricultural+Census+2023+Tables.xlsx"

# Define the simplified table names and corresponding sheet names
table_names <- c(
  "agricultural_area_hectares" = "Table_1",
  "vegetables_bulbs_fruit_area" = "Table_2",
  "number_of_cattle" = "Table_3 ", # this has a trailing space in the publication, if this gets fixed, change appropriately
  "number_of_sheep" = "Table_4 ",
  "number_of_pigs" = "Table_5",
  "number_of_poultry" = "Table_6",
  "number_of_other_livestock" = "Table_7",
  "occupiers_employees" = "Table_8",
  "occupiers_age_gender" = "Table_9",
  "legal_responsibility" = "Table_10",
  "owned_rented_land" = "Table_11",
  "farm_type" = "Table_12",
  "agricultural_area_lfa" = "Table_13",
  "holdings_crops_grass_subregion" = "Table_14",
  "crops_grass_area_subregion" = "Table_15",
  "holdings_livestock_region_subregion" = "Table_16",
  "livestock_subregion" = "Table_17",
  "holdings_occupiers_employees_subregion" = "Table_18",
  "occupiers_employees_subregion" = "Table_19",
  "arable_rotation" = "Table_20",
  "manure_fertiliser" = "Table_21"
)
# Function to remove rows until the first occurrence of "Source:" in the first column
remove_until_source <- function(data) {
  # Remove the first row (which is now the original header row)
  data <- data[-1, ]
  
  # Now remove rows until the first occurrence of "Source:"
  source_row <- which(str_detect(data[[1]], "^Source:"))
  if (length(source_row) > 0) {
    data <- data[(source_row + 1):nrow(data), ]
  }
  
  return(data)
}

# Function to remove columns and rows that are all NAs
clean_data <- function(data) {
  data <- data[, colSums(is.na(data)) < nrow(data)]  # Remove columns with all NAs
  data <- data[rowSums(is.na(data)) < ncol(data), ]  # Remove rows with all NAs
  return(data)
}

# Function to clean header names by removing text within brackets, any '\r\n', extra spaces, and specific region names
clean_headers <- function(headers) {
  headers <- str_replace_all(headers, "\\s*\\([^\\)]+\\)", "")  # Remove text within brackets
  headers <- str_replace_all(headers, "\r\n", " ")  # Remove \r\n
  headers <- str_replace_all(headers, "\\s+", " ")  # Replace multiple spaces with a single space
  headers <- str_replace_all(headers, "\\b(North West|North East|South East|South West)\\b", "")  # Remove specific region names
  headers <- str_trim(headers)  # Trim again to remove any resulting leading or trailing spaces
  
  # Make headers unique
  headers <- make.unique(headers, sep = "_")
  
  return(headers)
}

# Function to clean cell values by replacing multiple spaces with a single space
clean_cells <- function(data) {
  data <- data %>% mutate(across(where(is.character), ~str_replace_all(.x, "\\s+", " ")))
  return(data)
}

# Read each table, clean it, and assign to a variable
for (table in names(table_names)) {
  sheet_name <- table_names[table]
  data <- read_excel(file_path, sheet = sheet_name, col_names = FALSE)  # Read data without headers
  
  # Remove rows until the first occurrence of "Source:" in the first column
  data <- remove_until_source(data)
  
  # Clean headers manually since the first row was treated as data
  headers <- as.character(data[1, ])  # Take the first row as headers
  cleaned_headers <- clean_headers(headers)
  
  # Remove the first row that is now the headers
  data <- data[-1, ]
  
  # Assign cleaned headers to the data
  names(data) <- cleaned_headers
  
  # Remove columns with '5 year' in the header (case insensitive)
  columns_to_remove <- grep("5 year", cleaned_headers, ignore.case = TRUE)
  if (length(columns_to_remove) > 0) {
    data <- data[, -columns_to_remove]
    cleaned_headers <- cleaned_headers[-columns_to_remove]
  }
  
  # Convert all columns except the first to numeric, setting non-numeric values to NA
  data <- data %>% mutate(across(-1, ~ as.numeric(as.character(.))))
  
  # Remove '\r\n' from all character columns and clean multiple spaces in the first column
  data[[1]] <- str_replace_all(data[[1]], "\r\n", " ")
  data <- clean_cells(data)
  
  # Clean data
  cleaned_data <- clean_data(data)
  
  assign(table, cleaned_data)
}

# Set all values in the 2022 column to NA
number_of_poultry$`2022` <- NA

crops_grass_area_subregion <- crops_grass_area_subregion %>%
  rename(`Na h-Eileanan Siar` = `Na h Eileanan Siar`)

# Save all tables to an RData file
save(list = names(table_names), file = "census_data.RData")


# load to test
#load("census_data.RData")

#################################
###
### Processing for Crops / Fruit / Veg
###
### Run the code below to update
###
### Run whenever census data is updated
###
#################################

# Print unique values 

unique(agricultural_area_hectares$`Crop/Land use`)

unique(vegetables_bulbs_fruit_area$`Vegetables and fruits for human consumption`)

unique(crops_grass_area_subregion$`Land use by category`)


names(agricultural_area_hectares) <- names(agricultural_area_hectares) %>%
  str_replace_all("Area", "") %>%
  str_trim

names(vegetables_bulbs_fruit_area) <- names(vegetables_bulbs_fruit_area) %>%
  str_replace_all("Area", "") %>%
  str_trim

# Preprocess and round the summary crops data
crops_summary_data <- agricultural_area_hectares %>%
  filter(`Crop/Land use` %in% c("Total combine harvested crops", "Total crops for stockfeeding",
                                "Vegetables for human consumption", "Soft fruit")) %>%
  pivot_longer(
    cols = -`Crop/Land use`,
    names_to = "Year",
    values_to = "Value"
  ) %>%
  mutate(
    Year = as.numeric(Year),  # Ensure Year is numeric
    Value = if_else(
      abs(as.numeric(Value)) >= 1000,  # Round up for numbers with more than 3 significant figures
      round(as.numeric(Value)),
      round(as.numeric(Value), digits = 2)
    )
  )

land_use_data <- agricultural_area_hectares %>% 
  filter(`Crop/Land use` %in% c("Total crops, fallow, and set-aside", "Total grass", 
                                "Rough grazing", "Total sole right agricultural area", "Common grazings"))


land_use_subregion <- crops_grass_area_subregion %>%
  filter(`Land use by category` %in% c("Total agricultural area", "Total grass and rough grazing", 
                                       "Sole right grazing", "Total crops and fallow", "Common grazing", "Woodland"))


# Subset for cereals data
cereals_data <- agricultural_area_hectares %>%
  filter(`Crop/Land use` %in% c("Wheat", "Triticale", "Winter barley", "Spring barley", "Barley Total", 
                                "Winter oats", "Spring oats", "Oats Total", "Rye", "Mixed grain", 
                                "Total cereals"))

# Subset for oilseeds data
oilseed_data <- agricultural_area_hectares %>%
  filter(`Crop/Land use` %in% c("Winter oilseed rape", "Spring oilseed rape", "Linseed", "Total oilseeds"))

# Subset for potatoes data
potatoes_data <- agricultural_area_hectares %>%
  filter(`Crop/Land use` %in% c("Seed potatoes", "Ware potatoes", "Total potatoes"))

# Subset for beans data
beans_data <- agricultural_area_hectares %>%
  filter(`Crop/Land use` %in% c("Protein peas", "Field beans"))

# Subset for animal feed data
stockfeeding_data <- agricultural_area_hectares %>%
  filter(`Crop/Land use` %in% c("Turnips/swedes", "Kale/cabbage", "Maize", "Rape", "Fodder beet", 
                                "Lupins", "Other crops for stockfeeding", "Total crops for stockfeeding"))

# Subset for human vegetables data
human_vegetables_data <- vegetables_bulbs_fruit_area %>%
  filter(`Vegetables and fruits for human consumption` %in% c(
    "Peas for canning, freezing or drying",
    "Beans for canning, freezing or drying",
    "Turnips/swedes",
    "Calabrese",
    "Cauliflower",
    "Carrots",
    "Other vegetables",
    "Total vegetables"
  ))

# Subset for soft fruit data
fruit_data <- vegetables_bulbs_fruit_area %>%
  filter(`Vegetables and fruits for human consumption` %in% c(
    "Strawberries grown in the open",
    "Raspberries grown in the open",
    "Blueberries grown in the open",
    "Blackcurrants and other fruit grown in the open",
    "Total soft fruit grown in the open",
    "Tomatoes grown under cover",
    "Strawberries grown under cover",
    "Raspberries grown under cover",
    "Blueberries grown under cover",
    "Other fruit grown under cover",
    "Vegetables grown under cover",
    "Strawberries grown in open/under cover",
    "Raspberries grown in open/under cover",
    "Blackcurrants grown in open/under cover",
    "Blueberries grown in open/under cover",
    "Tomatoes grown in open/under cover",
    "Other fruit grown in open/under cover",
    "Total soft fruit"
  ))
# Subset for cereals_subregion
cereals_subregion <- crops_grass_area_subregion %>%
  filter(`Land use by category` %in% c(
    "Wheat",
    "Winter Barley",
    "Spring Barley",
    "Barley Total",
    "Oats, triticale and mixed grain"
  ))

# Subset for oilseed_subregion
oilseed_subregion <- crops_grass_area_subregion %>%
  filter(`Land use by category` %in% c(
    "Rape for oilseed and linseed"
  ))

# Subset for potato_subregion
potatoes_subregion <- crops_grass_area_subregion %>%
  filter(`Land use by category` %in% c(
    "Potatoes"
  ))

# Subset for beans_subregion
beans_subregion <- crops_grass_area_subregion %>%
  filter(`Land use by category` %in% c(
    "Peas and beans for combining"
  ))

# Subset for stockfeeding_subregion
stockfeeding_subregion <- crops_grass_area_subregion %>%
  filter(`Land use by category` %in% c(
    "Stockfeeding crops"
  ))

# Subset for human_veg_subregion
human_vegetables_subregion <- crops_grass_area_subregion %>%
  filter(`Land use by category` %in% c(
    "Vegetables for human consumption"
  ))

# Subset for fruit_subregion
fruit_subregion <- crops_grass_area_subregion %>%
  filter(`Land use by category` %in% c(
    "Orchard and soft fruit"
  ))

# Saving all the subsets to an RData file
save(
  crops_summary_data,
  land_use_data,
  land_use_subregion,
  cereals_data,
  oilseed_data,
  potatoes_data,
  beans_data,
  stockfeeding_data,
  human_vegetables_data,
  fruit_data,
  cereals_subregion,
  oilseed_subregion,
  potatoes_subregion,
  beans_subregion,
  stockfeeding_subregion,
  human_vegetables_subregion,
  fruit_subregion,
  file = "crops_data.RData"
)

### Animals summary data - Requires census tables

load("census.RData")


# Convert the wide format data into long format using pivot_longer
number_of_pigs_long <- number_of_pigs %>%
  pivot_longer(cols = -`Pigs by category`, names_to = "Year", values_to = "Total") %>%
  filter(`Pigs by category` == "Total pigs") %>%
  select(Year, `Total pigs` = Total)

number_of_poultry_long <- number_of_poultry %>%
  pivot_longer(cols = -`Poultry by category`, names_to = "Year", values_to = "Total") %>%
  filter(`Poultry by category` == "Total poultry") %>%
  select(Year, `Total poultry` = Total)

number_of_sheep_long <- number_of_sheep %>%
  pivot_longer(cols = -`Sheep by category`, names_to = "Year", values_to = "Total") %>%
  filter(`Sheep by category` == "Total sheep") %>%
  select(Year, `Total sheep` = Total)

number_of_cattle_long <- number_of_cattle %>%
  pivot_longer(cols = -`Cattle by category`, names_to = "Year", values_to = "Total") %>%
  filter(`Cattle by category` == "Total Cattle") %>%
  select(Year, `Total cattle` = Total)

# Merge the dataframes on the 'Year' column
total_animals <- number_of_pigs_long %>%
  inner_join(number_of_poultry_long, by = "Year") %>%
  inner_join(number_of_sheep_long, by = "Year") %>%
  inner_join(number_of_cattle_long, by = "Year")

# Convert Year column to numeric
total_animals$Year <- as.numeric(total_animals$Year)

# save total animals 
save(total_animals, file = "total_animals.RData")



#########################################################################
### 
###
### Emissions Data Processing
###
###
##########################################################################

#agri_gas = Gas Breakdown of Emissions from Scottish agriculture greenhouse gas emissions and nitrogen use
#subsector_total = Agricultural emissions broken down by subsector from Scottish agriculture greenhouse gas emissions and nitrogen use
#subsector_source = Breakdown of subsectors by source from Scottish agriculture greenhouse gas emissions and nitrogen use
#national_total = Yearly breakdown of Scottish Emissions from Scottish Greenhouse Gas Emissions publication

# As of 2022-23 emissions publication, these were made available in excel sheet before being inputted into R
# This could be repeated, or data could be read in from the difference sources, and manipulated into the same format

# To update data, replace file path and run the below script.

# Load  data from the Excel file
file_path <- "ghg_data.xlsx"

agri_gas <- read_excel(file_path, sheet = "agri_gas")
national_total <- read_excel(file_path, sheet = "national_total")
subsector_total <- read_excel(file_path, sheet = "subsector_total")
subsector_source <- read_excel(file_path, sheet = "subsector_source")

agri_gas <- agri_gas %>% 
  rename(Gas = ...1)

# Reshape the data to long format
agri_gas <- agri_gas %>% pivot_longer(cols = -Gas, names_to = "Year", values_to = "Value")
national_total <- national_total %>% pivot_longer(cols = -Industry, names_to = "Year", values_to = "Value")
subsector_total <- subsector_total %>% pivot_longer(cols = -Subsector, names_to = "Year", values_to = "Value")

# Convert Year to numeric
agri_gas$Year <- as.numeric(agri_gas$Year)
national_total$Year <- as.numeric(national_total$Year)
subsector_total$Year <- as.numeric(subsector_total$Year)

# Merge the specified sources into 'Other emission source'
subsector_source <- subsector_source %>%
  mutate(
    Source = ifelse(Source %in% c("Urea application", "Non-energy products from fuels and solvent use", "Other emission source"), 
                    "Other emission source", 
                    Source)
  ) %>%
  group_by(Source) %>%
  summarise(across(everything(), sum)) %>%
  ungroup()

save(subsector_total, agri_gas, national_total, subsector_source, file = "ghg_data.RData")

# load data to test
#load("ghg_data.RData")





################################################################################

###############################
####
#### Module 2023 data
####
#### This data comes from the 2023 Agricultural Census Module Results
####
#### https://www.gov.scot/publications/results-from-the-scottish-agricultural-census-module-june-2023/
####
#### The script extracts tables looking at soil management, fertiliser usage, manure, nitrogen
#### and formats the data into formatting to be used in the modules.
#### This minimises the amount of data processing done within the R Shiny app, improving running efficiency. 
####
#### This script should not need re-run as the module data will not be updated, though can be used
#### as a baseline to include future year's module data.
####
#################################

# Load necessary libraries
library(readxl)
library(stringr)
library(dplyr)

# Define the file path
file_path <- "June+Agricultural+Census+2023+-+Module+Report+-+Production+methods+and+nutrient+application+-+Tables.xlsx"

# Define the sheet names to read
sheets_to_read <- c("Table_4", "Table_5", "Table_7", "Table_8", "Table_9", "Table_12")

# Define shortened names for the dataframes
short_names <- c("soil_nutrient_mgmt", "grass_crop_nutrient_mgmt", "nitrogen_250", 
                 "nitrogen_400", "manure_qty", "fertiliser_use")

# Function to clean header names by removing text within brackets, any '\r\n', extra spaces, and specific region names
clean_headers <- function(headers) {
  headers <- str_replace_all(headers, "\\s*\\([^\\)]+\\)", "")  # Remove text within brackets
  headers <- str_replace_all(headers, "\r\n", " ")  # Remove \r\n
  headers <- str_replace_all(headers, "\\s+", " ")  # Replace multiple spaces with a single space
  headers <- str_replace_all(headers, "\\b(North West|North East|South East|South West)\\b", "")  # Remove specific region names
  headers <- str_trim(headers)  # Trim again to remove any resulting leading or trailing spaces
  return(headers)
}

# Function to read and process each sheet
read_and_process_sheet <- function(sheet) {
  df <- read_excel(file_path, sheet = sheet, skip = 5)  # Skip the first 5 rows
  colnames(df) <- clean_headers(colnames(df))  # Clean the headers
  return(df)
}

# Function to round all numeric columns to 2 decimal places
round_df <- function(df) {
  df[] <- lapply(df, function(x) if(is.numeric(x)) round(x, 2) else x)
  return(df)
}

# Read and process the specified sheets into a list of dataframes
data_frames <- lapply(sheets_to_read, read_and_process_sheet)

# Name the list elements with shortened names
names(data_frames) <- short_names

# Remove the 'Area' column from 'grass_crop_nutrient_mgmt' if it exists
if("Area" %in% colnames(data_frames$grass_crop_nutrient_mgmt)) {
  data_frames$grass_crop_nutrient_mgmt <- select(data_frames$grass_crop_nutrient_mgmt, -Area)
}

# Ensure 'Percentage of holdings' and 'Average holding area' are numeric in both dataframes
data_frames$soil_nutrient_mgmt$`Percentage of holdings` <- as.numeric(as.character(data_frames$soil_nutrient_mgmt$`Percentage of holdings`))
data_frames$grass_crop_nutrient_mgmt$`Percentage of holdings` <- as.numeric(as.character(data_frames$grass_crop_nutrient_mgmt$`Percentage of holdings`))

data_frames$soil_nutrient_mgmt$`Average holding area` <- as.numeric(as.character(data_frames$soil_nutrient_mgmt$`Average holding area`))
data_frames$grass_crop_nutrient_mgmt$`Average holding area` <- as.numeric(as.character(data_frames$grass_crop_nutrient_mgmt$`Average holding area`))

# Round all numeric columns to 2 decimal places in each dataframe
data_frames <- lapply(data_frames, round_df)

# Join 'soil_nutrient_mgmt' and 'grass_crop_nutrient_mgmt' data frames
combined_nutrient_mgmt <- bind_rows(data_frames$soil_nutrient_mgmt, data_frames$grass_crop_nutrient_mgmt)

# Remove specified rows from the 'Soil nutrient management' column in the combined data frame
remove_entries <- c("Holdings with grassland", "Cropland holdings", "Holdings with grass or crops", 
                    "Soil testing resulted in change of crop nutrient application (those that performed soil testing)", 
                    "Uses protected urea")

combined_nutrient_mgmt <- combined_nutrient_mgmt %>% 
  filter(!`Soil nutrient management` %in% remove_entries)

# Assign the combined data frame back to the list
data_frames$combined_nutrient_mgmt <- combined_nutrient_mgmt

# Remove the original separate data frames
data_frames$soil_nutrient_mgmt <- NULL
data_frames$grass_crop_nutrient_mgmt <- NULL

# Save the data frames as a .RData file
save(list = names(data_frames), file = "module_2023.RData", envir = list2env(data_frames))

################################

## File: hc_theme.R 
 # hc_theme.R

library(highcharter)

# Define the Highcharts theme
thm <- hc_theme(
  chart = list(
    style = list(
      fontFamily = "Arial, sans-serif",
      fontSize = "16px",
      color = "black"
    )
  ),
  title = list(
    style = list(
      fontFamily = "Arial, sans-serif",
      fontSize = "16px",
      color = "black"
    )
  ),
  subtitle = list(
    style = list(
      fontFamily = "Arial, sans-serif",
      fontSize = "16px",
      color = "black"
    )
  ),
  xAxis = list(
    labels = list(
      style = list(
        fontFamily = "Arial, sans-serif",
        fontSize = "16px",
        color = "black"
      )
    ),
    title = list(
      style = list(
        fontFamily = "Arial, sans-serif",
        fontSize = "16px",
        color = "black"
      )
    )
  ),
  yAxis = list(
    labels = list(
      style = list(
        fontFamily = "Arial, sans-serif",
        fontSize = "16px",
        color = "black"
      )
    ),
    title = list(
      style = list(
        fontFamily = "Arial, sans-serif",
        fontSize = "16px",
        color = "black"
      )
    )
  ),
 tooltip = list(
   style = list(
      fontFamily = "Arial, sans-serif",
      fontSize = "16px",
      color = "black"
    ),
    headerFormat = "<b>{point.key}</b><br/>"  
  ),
  
  legend = list(
    itemStyle = list(
      fontFamily = "Arial, sans-serif",
      fontSize = "16px",
      color = "black"
    )
  ),
  plotOptions = list(
    series = list(
      stickyTracking = FALSE,
      itemStyle = list(
        fontFamily = "Arial, sans-serif",
        fontSize = "16px",
        color = "black"
      ),
      dataLabels = list(
        style = list(
          fontFamily = "Arial, sans-serif",
          fontSize = "16px",  # Increase the font size for data labels
          color = "black"
        )
      ),
      marker = list(
        enabled = TRUE,
        states = list(
          hover = list(
            enabled = TRUE
          )
        )
      )
    )
  ),
  colors = c("#002d54", "#2b9c93", "#6a2063", "#e5682a", "#0b4c0b", "#5d9f3c", "#592c20", "#ca72a2")
)

# Apply the Highcharts theme globally
options(highcharter.theme = thm)

## File: home.R 
 

## File: module_animals_summary.R 
 # module_summary_animals.R

# Reactive data setup for Animals
full_data_animals <- reactive({
  total_animals %>%
    pivot_longer(
      cols = starts_with("Total"),
      names_to = "Animal_Type",
      values_to = "Value"
    ) %>%
    mutate(Value = as.numeric(Value))  # Ensure the 'Value' column is numeric
})


animalsSummaryUI <- function(id) {
  ns <- NS(id)
  tagList(
    sidebarLayout(
      sidebarPanel(
        width = 3,
        div("Adjust the sliders to compare data from different years.", 
            style = "font-size: 14px; font-weight: bold; margin-bottom: 10px;"),
        sliderInput(ns("summary_current_year_animals"), "Year of interest", min = 2012, max = census_year, value = census_year, step = 1, sep = ""),
        sliderInput(ns("summary_comparison_year_animals"), "Comparison year", min = 2012, max = census_year, value = census_year - 1, step = 1, sep = "")
      ),
      mainPanel(
        id = ns("mainpanel"),
        width = 9,
        tabsetPanel(
          id = ns("tabs"),
          tabPanel("Summary Page",
                   value = "Summary_Page",
                   fluidRow(
                     column(width = 6, valueBoxUI(ns("totalCattle")), style = "padding-right: 0; padding-left: 0; padding-bottom: 10px;"),
                     column(width = 6, valueBoxUI(ns("totalSheep")), style = "padding-right: 0; padding-left: 0; padding-bottom: 10px;")
                   ),
                   fluidRow(
                     column(width = 6, valueBoxUI(ns("totalPigs")), style = "padding-right: 0; padding-left: 0;"),
                     column(width = 6, valueBoxUI(ns("totalPoultry")), style = "padding-right: 0; padding-left: 0;")
                   ),
                   # Add the footer text
                   div(
                     style = "margin-top: 20px; padding: 10px; border-top: 1px solid #ddd;",
                     HTML("<strong>Note:</strong> Poultry estimates for 2023 are not comparable to previous years due to methodological improvements.")
                   )
          ),
          tabPanel("Data Table",
                   fluidRow(
                     column(12,
                            DTOutput(ns("data_table")),
                            tags$div(
                              style = "margin-top: 20px;",
                              downloadButton(ns("download_data"), "Download Data")
                            )
                     )
                   )
          ),
          footer = generateCensusTableFooter()  
        )
      )
    )
  )
}

animalsSummaryServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    current_year <- reactive({ input$summary_current_year_animals })
    comparison_year <- reactive({ input$summary_comparison_year_animals })
    
    valueBoxServer("totalCattle", full_data_animals, "Animal_Type", reactive("Total cattle"), current_year, comparison_year, "cattle")
    valueBoxServer("totalSheep", full_data_animals, "Animal_Type", reactive("Total sheep"), current_year, comparison_year, "sheep")
    valueBoxServer("totalPigs", full_data_animals, "Animal_Type", reactive("Total pigs"), current_year, comparison_year, "pigs")
    valueBoxServer("totalPoultry", full_data_animals, "Animal_Type", reactive("Total poultry"), current_year, comparison_year, "poultry")
    
    # Pivot the data wider
    pivoted_data <- reactive({
      full_data_animals() %>%
        pivot_wider(names_from = Animal_Type, values_from = Value) %>%
        mutate(across(where(is.numeric) & !contains("Year"), comma))
    })
    
    # Render the pivoted data table
    output$data_table <- renderDT({
      datatable(pivoted_data(), options = list(
        scrollX = TRUE,   # Enable horizontal scrolling
        pageLength = 20   # Show 20 entries by default
      ))
    })
    
    # Download handler for the pivoted data
    output$download_data <- downloadHandler(
      filename = function() {
        paste("Animal_Summary_Data_", Sys.Date(), ".csv", sep = "")
      },
      content = function(file) {
        write.csv(pivoted_data(), file, row.names = FALSE)
      }
    )
  })
}



# Testing module
content_demo <- function() {
  ui <- fluidPage(animalsSummaryUI("summary_animals_test"))
  server <- function(input, output, session) {
    animalsSummaryServer("summary_animals_test")
  }
  shinyApp(ui, server)
}

content_demo()

## File: module_area_chart.R 
 

## File: module_bar_chart.R 
 

## File: module_beans.R 
 # File: module_beans.R

beansUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = unique(beans_subregion$`Land use by category`)
        )
      ),     
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series' || input.tabsetPanel === 'Area Chart'",
        ns = ns,
        checkboxGroupInput(
          ns("timeseries_variables"),
          "Select Time Series Variables",
          choices = unique(beans_data$`Crop/Land use`),
          selected = c(
            "Protein peas",
            "Field beans"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Time Series Data" = "timeseries"),
          selected = "map"
        )
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Time Series", lineChartUI(ns("line"))),
        tabPanel("Area Chart", areaChartUI(ns("area"))),
        tabPanel("Data Table", 
                 DTOutput(ns("table")),
                 downloadButton(ns("download_data"), "Download Data"),
                 generateCensusTableFooter()

        )
      )
    )
  )
}

beansServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    beans_map <- beans_subregion %>%
      select(-Scotland) %>%
      mutate(across(everything(), as.character)) %>%
      pivot_longer(cols = -`Land use by category`, names_to = "sub_region", values_to = "value") %>%
      mutate(value = safe_as_numeric(value))
    
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        beans_map %>% filter(`Land use by category` == input$variable)
      }),
      unit = "hectares",
      footer = census_footer,
      variable = reactive(input$variable),
      title = paste("Beans distribution by region in Scotland in", census_year),
      legend_title = "Area (hectares)"
    )
    
    chart_data <- reactive({
      req(input$timeseries_variables)
      filtered_data <- beans_data %>%
        filter(`Crop/Land use` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
        mutate(year = as.numeric(year))  # Ensure year is numeric
      filtered_data
    })
    
    areaChartServer(
      id = "area",
      chart_data = chart_data,
      title = paste("Area used to grow peas and/or beans
                    over time"),
      yAxisTitle = "Area of peas/beans (1,000 hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = paste("Area used to grow peas and/or beans
                    over time"),
      yAxisTitle = "Area of peas/beans (1,000 hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    output$table <- renderDT({
      req(input$tabsetPanel == "Data Table")
      if (input$table_data == "map") {
        req(input$variable)
        datatable(
          beans_map %>%
            filter(`Land use by category` == input$variable) %>%
            pivot_wider(names_from = sub_region, values_from = value) %>%
            mutate(across(where(is.numeric), comma)), # Apply comma formatting to numeric columns
          options = list(scrollX = TRUE) # Enable horizontal scrolling
        )
      } else {
        datatable(
          beans_data %>%
            pivot_wider(names_from = year, values_from = value) %>%
            mutate(across(where(is.numeric) & !contains("Year"), comma)), # Apply comma formatting to numeric columns except 'Year'
          options = list(scrollX = TRUE) # Enable horizontal scrolling
        )
      }
    })
 
    output$download_data <- downloadHandler(
      filename = function() {
        if (input$table_data == "map") {
          paste("Beans_Subregion_Data_", Sys.Date(), ".csv", sep = "")
        } else {
          paste("Beans_Timeseries_Data_", Sys.Date(), ".csv", sep = "")
        }
      },
      content = function(file) {
        data <- if (input$table_data == "map") {
          beans_map %>%
            filter(`Land use by category` == input$variable) %>%
            pivot_wider(names_from = sub_region, values_from = value)
        } else {
          beans_data %>%
            pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
            pivot_wider(names_from = year, values_from = value)
        }
        write.csv(data, file, row.names = FALSE)
      }
    )
  })
}

beans_demo <- function() {
  ui <- fluidPage(beansUI("beans_test"))
  server <- function(input, output, session) {
    beansServer("beans_test")
  }
  shinyApp(ui, server)
}
beans_demo()

## File: module_breakdown_chart.R 
 

## File: module_cattle.R 
 # File: module_cattle.R
cattleUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = c(
            "Total Cattle" = "Total Cattle",
            "Total Female Dairy Cattle" = "Total Female Dairy Cattle",
            "Total Female Beef Cattle" = "Total Female Beef Cattle",
            "Total Male Cattle" = "Total Male Cattle",
            "Total Calves" = "Total Calves"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series' || input.tabsetPanel === 'Area Chart'",
        ns = ns,
        selectizeInput(
          ns("timeseries_variables"),
          "Click within the box to select variables (not all shown)",
          choices = unique(number_of_cattle$`Cattle by category`),
          selected = c(
            "Total Female Dairy Cattle",
            "Total Female Beef Cattle",
            "Total Male Cattle",
            "Total Calves"
          ),
          multiple = TRUE,
          options = list(
            plugins = list('remove_button')
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Time Series Data" = "timeseries"),
          selected = "map"
        )
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Time Series", lineChartUI(ns("line"), note_type = 2)),
        tabPanel("Area Chart", areaChartUI(ns("area"), note_type = 2)),
        tabPanel("Data Table", 
                 DTOutput(ns("table")),
                 downloadButton(ns("downloadData"), "Download Data"),
                 generateCensusTableFooter()
        )
      )
    )
  )
}


cattleServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    # Processing data for Map
    cattle_data <- livestock_subregion %>%
      filter(`Livestock by category` %in% c(
        "Total Female Dairy Cattle",
        "Total Female Beef Cattle",
        "Total Male Cattle",
        "Total Calves",
        "Total Cattle"
      )) %>%
      select(-Scotland) %>%
      mutate(across(everything(), as.character)) %>%
      pivot_longer(cols = -`Livestock by category`, names_to = "sub_region", values_to = "value") %>%
      mutate(value = safe_as_numeric(value))
    
    # Map Server
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        cattle_data %>% filter(`Livestock by category` == input$variable)
      }),
      footer = census_footer,
      variable = reactive(input$variable),
      title = paste("Cattle distribution by region in Scotland in", census_year),
      legend_title = "Number of cattle"
    )
    
    # Processing data for Area Chart and Time Series
    chart_data <- reactive({
      req(input$timeseries_variables)
      filtered_data <- number_of_cattle %>%
        filter(`Cattle by category` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Cattle by category`, names_to = "year", values_to = "value") %>%
        mutate(year = as.numeric(year))  # Ensure year is numeric
      filtered_data
    })
    
    # Area Chart Server
    areaChartServer(
      id = "area",
      chart_data = chart_data,
      title = "Number of cattle by category across time",
      yAxisTitle = "Number of cattle (1,000)",
      xAxisTitle = "Year",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    # Line Chart Server
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = "Number of cattle by category across time",
      yAxisTitle = "Number of cattle (1,000)",
      xAxisTitle = "Year",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    # Data Table output
    output$table <- renderDT({
      req(input$tabsetPanel == "Data Table")
      data <- if (input$table_data == "map") {
        cattle_data %>%
          filter(`Livestock by category` == input$variable) %>%
          pivot_wider(names_from = sub_region, values_from = value) %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) # Pivot wider for map data
      } else {
        number_of_cattle %>%
          pivot_longer(cols = -`Cattle by category`, names_to = "year", values_to = "value") %>%
          pivot_wider(names_from = year, values_from = value) %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) # Pivot wider for time series data
      }
      datatable(
        data,
        options = list(
          scrollX = TRUE,  # Enable horizontal scrolling
          pageLength = 20  # Show 20 entries by default
        )
      )
    })
    
    # Data Download Handler
    output$downloadData <- downloadHandler(
      filename = function() {
        if (input$table_data == "map") {
          paste("Cattle_Map_Data_", Sys.Date(), ".csv", sep = "")
        } else {
          paste("Cattle_Timeseries_Data_", Sys.Date(), ".csv", sep = "")
        }
      },
      content = function(file) {
        data <- if (input$table_data == "map") {
          cattle_data %>%
            filter(`Livestock by category` == input$variable) %>%
            pivot_wider(names_from = sub_region, values_from = value)
        } else {
          number_of_cattle %>%
            pivot_longer(cols = -`Cattle by category`, names_to = "year", values_to = "value") %>%
            pivot_wider(names_from = year, values_from = value)
        }
        write.csv(data, file, row.names = FALSE)
      }
    )
  })
}


cattle_demo <- function() {
  ui <- fluidPage(cattleUI("cattle_test"))
  server <- function(input, output, session) {
    cattleServer("cattle_test")
  }
  shinyApp(ui, server)
}

cattle_demo()

## File: module_cereals.R 
 # File: module_cereals.R

cerealsUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = unique(cereals_subregion$`Land use by category`)
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series' || input.tabsetPanel === 'Area Chart'",
        ns = ns,
        selectizeInput(
          ns("timeseries_variables"),
          "Click within the box to select variables",
          choices = unique(cereals_data$`Crop/Land use`),
          selected = c(
            "Wheat",
            #"Triticale",
            "Barley Total",
            "Oats Total"
            #"Rye",
           # "Mixed grain"
          ),
          multiple = TRUE,
          options = list(
            plugins = list('remove_button')
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Time Series Data" = "timeseries"),
          selected = "map"
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Cereals Summary'",
        ns = ns,
        div("Adjust the sliders to compare data from different years.", 
            style = "font-size: 14px; font-weight: bold; margin-bottom: 10px;"),
        sliderInput(ns("summary_current_year_cereals"), "Year of interest", min = 2012, max = census_year, value = census_year, step = 1, sep = ""),
        sliderInput(ns("summary_comparison_year_cereals"), "Comparison year", min = 2012, max = census_year, value = census_year-1, step = 1, sep = "")
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Time Series", lineChartUI(ns("line"), note_type = 2)),
        tabPanel("Area Chart", areaChartUI(ns("area"), note_type = 2)),
        tabPanel("Data Table", 
                 DTOutput(ns("table")),
                 downloadButton(ns("downloadData"), "Download Data"),
                 generateCensusTableFooter()

        ),
        tabPanel("Summary",
                 fluidRow(
                   column(width = 6, h3("Cereals summary section")),
                   column(width = 3, selectInput(ns("summary_variable"), "Select Variable", choices = unique(cereals_data$`Crop/Land use`), selected = "Total cereals"))
                 ),
                 fluidRow(
                   column(width = 6, p("This content is under development.")),
                   column(width = 6, valueBoxUI(ns("summaryValueBox")), style = "padding-right: 0; padding-left: 0; padding-bottom: 10px;")
                 )
        )
      )
    )
  )
}

cerealsServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    cereals_map <- cereals_subregion %>%
      select(-Scotland) %>%
      mutate(across(everything(), as.character)) %>%
      pivot_longer(cols = -`Land use by category`, names_to = "sub_region", values_to = "value") %>%
      mutate(value = safe_as_numeric(value))
    
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        cereals_map %>% filter(`Land use by category` == input$variable)
      }),
      unit = "hectares",
      footer = census_footer,
      variable = reactive(input$variable),
      title = paste("Cereals distribution by region in Scotland in", census_year),
      legend_title = "Area (hectares)"
    )
    
    chart_data <- reactive({
      req(input$timeseries_variables)
      filtered_data <- cereals_data %>%
        filter(`Crop/Land use` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
        mutate(year = as.numeric(year))  # Ensure year is numeric
      filtered_data
    })
    
    areaChartServer(
      id = "area",
      chart_data = chart_data,
      title = "Area used to grow cereals over time",
      yAxisTitle = "Area of cereals (1,000 hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = "Area used to grow cereals over time",
      yAxisTitle = "Area of cereals (1,000 hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    output$table <- renderDT({
      req(input$tabsetPanel == "Data Table")
      if (input$table_data == "map") {
        req(input$variable)
        cereals_map %>%
          filter(`Land use by category` == input$variable) %>%
          pivot_wider(names_from = sub_region, values_from = value) %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      } else {
        cereals_data %>%
          pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
          pivot_wider(names_from = year, values_from = value) %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      }
    })
    
    output$downloadData <- downloadHandler(
      filename = function() {
        if (input$table_data == "map") {
          paste("Cereals_Map_Data_", Sys.Date(), ".csv", sep = "")
        } else {
          paste("Cereals_Timeseries_Data_", Sys.Date(), ".csv", sep = "")
        }
      },
      content = function(file) {
        data <- if (input$table_data == "map") {
          cereals_map %>%
            filter(`Land use by category` == input$variable) %>%
            pivot_wider(names_from = sub_region, values_from = value)
        } else {
          cereals_data %>%
            pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
            pivot_wider(names_from = year, values_from = value)
        }
        write.csv(data, file, row.names = FALSE)
      }
    )
    
    # Reactive expression for the selected variable and years
    summary_data <- reactive({
      cereals_data %>%
        filter(`Crop/Land use` == input$summary_variable) %>%
        pivot_longer(cols = -`Crop/Land use`, names_to = "Year", values_to = "Value") %>%
        mutate(Year = as.numeric(Year))
    })
    
    current_year <- reactive({ input$summary_current_year_cereals })
    comparison_year <- reactive({ input$summary_comparison_year_cereals })
    
    # Value box for the selected variable
    valueBoxServer("summaryValueBox", summary_data, "Crop/Land use", reactive(input$summary_variable), current_year, comparison_year, "ha")
  })
}

# Testing module
cereals_demo <- function() {
  ui <- fluidPage(cerealsUI("cereals_test"))
  server <- function(input, output, session) {
    cerealsServer("cereals_test")
  }
  shinyApp(ui, server)
}

cereals_demo()

## File: module_crops_summary.R 
 
# File: module_crops_summary.R

# Reactive data setup for Crops
full_data_crops <- reactive({
  crops_summary_data 
})



# UI for Summary Crops Module
cropsSummaryUI <- function(id) {
  ns <- NS(id)
  tagList(
    sidebarLayout(
      sidebarPanel(
        width = 3,
        div("Adjust the sliders to compare data from different years.", 
            style = "font-size: 14px; font-weight: bold; margin-bottom: 10px;"),
        sliderInput(ns("summary_current_year_crops"), "Year of interest", min = 2012, max = census_year, value = census_year, step = 1, sep = ""),
        sliderInput(ns("summary_comparison_year_crops"), "Comparison year", min = 2012, max = census_year, value = census_year - 1, step = 1, sep = "")
      ),
      mainPanel(
        id = ns("mainpanel"),
        width = 9,
        tabsetPanel(
          id = ns("tabs"),
          tabPanel("Summary Page",
                   value = "Summary_Page",
                   fluidRow(
                     column(width = 6, valueBoxUI(ns("totalCombineHarvestedCrops")), style = "padding-right: 0; padding-left: 0; padding-bottom: 10px;"),
                     column(width = 6, valueBoxUI(ns("totalCropsForStockfeeding")), style = "padding-right: 0; padding-left: 0; padding-bottom: 10px;")
                   ),
                   fluidRow(
                     column(width = 6, valueBoxUI(ns("vegetablesForHumanConsumption")), style = "padding-right: 0; padding-left: 0;"),
                     column(width = 6, valueBoxUI(ns("softFruit")), style = "padding-right: 0; padding-left: 0;")
                   ),
                   # Add the footer text
                   div(
                     style = "margin-top: 20px; padding: 10px; border-top: 1px solid #ddd;",
                     
                   )
          ),
          tabPanel("Data Table",
                   fluidRow(
                     column(12, 
                            DTOutput(ns("data_table")),
                            tags$div(
                              style = "margin-top: 20px;",
                              downloadButton(ns("download_data"), "Download Data")
                            )
                     )
                   )
          ),
          footer = generateCensusTableFooter() 
        )
      )
    )
  )
}

library(scales)

cropsSummaryServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    current_year <- reactive({ input$summary_current_year_crops })
    comparison_year <- reactive({ input$summary_comparison_year_crops })
    
    valueBoxServer("totalCombineHarvestedCrops", full_data_crops, "Crop/Land use", reactive("Total combine harvested crops"), current_year, comparison_year, "hectares")
    valueBoxServer("totalCropsForStockfeeding", full_data_crops, "Crop/Land use", reactive("Total crops for stockfeeding"), current_year, comparison_year, "hectares")
    valueBoxServer("vegetablesForHumanConsumption", full_data_crops, "Crop/Land use", reactive("Vegetables for human consumption"), current_year, comparison_year, "hectares")
    valueBoxServer("softFruit", full_data_crops, "Crop/Land use", reactive("Soft fruit"), current_year, comparison_year, "hectares")
    
    # Pivot the data wider for the data table and format numbers with commas, excluding the 'Year' column
    pivoted_data <- reactive({
      full_data_crops() %>%
        pivot_wider(names_from = `Crop/Land use`, values_from = `Value`) %>%
        mutate(across(where(is.numeric) & !contains("Year"), comma))  # Format all numeric columns except 'Year' with commas
    })
    
    # Render the pivoted data table
    output$data_table <- renderDT({
      datatable(pivoted_data(), options = list(
        scrollX = TRUE,   # Enable horizontal scrolling
        pageLength = 20   # Show 20 entries by default
      ))
    })
    
    # Download handler for the pivoted data
    output$download_data <- downloadHandler(
      filename = function() {
        paste("Crops_Summary_Data_", Sys.Date(), ".csv", sep = "")
      },
      content = function(file) {
        write.csv(pivoted_data(), file, row.names = FALSE)
      }
    )
  })
}



# Testing module
content_demo <- function() {
  ui <- fluidPage(cropsSummaryUI("summary_crops_test"))
  server <- function(input, output, session) {
    cropsSummaryServer("summary_crops_test")
  }
  shinyApp(ui, server)
}

content_demo()

## File: module_data_functions.R 
 

## File: module_data_table.R 
 

## File: module_double_bar_chart.R 
 

## File: module_economy_summary.R 
 # module_economy_summary.R

economySummaryUI <- function(id) {
  ns <- NS(id)
  tagList(
    fluidPage(
      h3(strong("Content under development")),
      p("Estimates for the net income gained by the agriculture industry in Scotland are available in the ",
        a("Total income from farming (TIFF) publication.", href = "https://www.gov.scot/collections/total-income-from-farming/", target = "_blank")),
      p("Farm business level estimates of average incomes from commercial farms in Scotland are available in the ",
        a("Scottish farm business income publication.", href = "https://www.gov.scot/collections/scottish-farm-business-income-fbi-annual-estimates/", target = "_blank"))
    )
  )
}

economySummaryServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    # Add server logic if necessary
  })
}

## File: module_employees.R 
 # Coerce all relevant columns to character before pivoting
occupiers_employees_subregion <- occupiers_employees_subregion %>%
  mutate(across(-`Occupiers and employees by category`, as.character))

# Transform the data
regions_data <- occupiers_employees_subregion %>% 
  select(-Scotland) %>% 
  pivot_longer(cols = -`Occupiers and employees by category`, names_to = "sub_region", values_to = "value") %>%
  mutate(value = ifelse(value == "c", NA, safe_as_numeric(value)))

# Filter for the specific categories
categories <- c("Regular full-time staff total", 
                "Regular part-time staff total", 
                "Total Casual and seasonal staff", 
                "Total agricultural workforce")

filtered_regions_data <- regions_data %>%
  filter(`Occupiers and employees by category` %in% categories)

employeesMapUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      uiOutput(ns("sidebar_ui"))
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabs"),
        tabPanel("Map", mapUI(ns("map")), value = "map"),
        tabPanel("Time Series", 
                 lineChartUI(ns("line_chart"), note_type = 2),  # Use note_type = 2 for the second note
                 value = "timeseries"),
        tabPanel("Data Table", 
                 DTOutput(ns("data_table")),
                 div(
                   style = "background-color: #f0f0f0; padding: 15px; border-radius: 5px; margin-top: 10px; font-style: bold;",
                   "Note: Migrant labour is not directly comparable to other employee measures as it is measured in person working days."
                 ),  # Separate note about migrant labour
                 downloadButton(ns("downloadData"), "Download Data"),   
                 generateCensusTableFooter(),  # Existing footer

                 value = "data_table")
      )
    )
  )
}


employeesMapServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    # Data Processing for Timeseries
    occupiers_employees <- occupiers_employees %>%
      mutate(across(starts_with("20"), safe_as_numeric))
    
    # Reactive data for the time series chart
    chart_data <- reactive({
      occupiers_employees %>%
        pivot_longer(cols = -`Occupiers and employees by category`, names_to = "Year", values_to = "Value") %>%
        mutate(Year = as.numeric(Year)) %>%
        filter(!grepl("occupiers", `Occupiers and employees by category`, ignore.case = TRUE))
    })
    
    output$sidebar_ui <- renderUI({
      req(input$tabs)
      if (input$tabs == "map") {
        radioButtons(ns("variable"), "Select Variable", choices = categories)
      } else if (input$tabs == "data_table") {
        radioButtons(ns("data_source"), "Choose data to show:", choices = c("Chart Data", "Map Data"))
      } else if (input$tabs == "timeseries") {
        selectizeInput(
          ns("variables"), 
          "Click within the box to add more variables", 
          choices = unique(chart_data()$`Occupiers and employees by category`), 
          selected = c('Regular full-time staff total', 'Regular part-time staff total', 'Total Casual and seasonal staff'), 
          multiple = TRUE, 
          options = list(plugins = list('remove_button'), placeholder = "Click to add more variables")
        )
      }
    })
    
    # Pivot the chart data wider for the data table view
    pivoted_chart_data <- reactive({
      chart_data() %>%
        pivot_wider(names_from = Year, values_from = Value) %>%
        mutate(across(where(is.numeric) & !contains("Year"), comma))
    })
    
    # Pivot the map data wider for the data table view
    pivoted_regions_data <- reactive({
      filtered_regions_data %>%
        pivot_wider(names_from = sub_region, values_from = value) %>%
        mutate(across(where(is.numeric) & !contains("Year"), comma))
    })
    
    # Time series chart rendering
    lineChartServer(
      id = "line_chart",
      chart_data = reactive({
        req(input$variables)
        chart_data() %>%
          filter(`Occupiers and employees by category` %in% input$variables)
      }),
      title = "Agricultural employees over time",
      yAxisTitle = "Employees (1,000)",
      xAxisTitle = "Year",
      unit = "employees",
      footer = '<div style="font-size: 16px; font-weight: bold;"><a href="https://www.gov.scot/publications/results-scottish-agricultural-census-june-2023/documents/">Source: Scottish Agricultural Census: June 2023</a></div>',
      x_col = "Year",
      y_col = "Value"
    )
    
    # Map rendering
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        filtered_regions_data %>%
          filter(`Occupiers and employees by category` == input$variable)
      }),
      unit = "employees",
      footer = '<div style="font-size: 16px; font-weight: bold;"><a href="https://www.gov.scot/publications/results-scottish-agricultural-census-june-2023/documents/">Source: Scottish Agricultural Census: June 2023</a></div>',
      variable = reactive(input$variable),
      title = paste("Agricultural employees by region in Scotland in", census_year),
      legend_title = "Number of employees"
    )  
    
    # Render the data table with scrollable options for both chart and map data
    output$data_table <- renderDT({
      req(input$data_source)
      if (input$data_source == "Chart Data") {
        datatable(pivoted_chart_data(), options = list(
          scrollX = TRUE,
          pageLength = 26  # Show all 26 entries on a single page
        ))
      } else if (input$data_source == "Map Data") {
        datatable(pivoted_regions_data(), options = list(
          scrollX = TRUE,
          pageLength = 10  # You can adjust this if needed for the map data
        ))
      }
    })
    
    # Create a download handler with appropriate naming
    output$downloadData <- downloadHandler(
      filename = function() {
        if (input$data_source == "Chart Data") {
          paste("Scottish Agricultural Employees Timeseries Data - 2012 to 2023.csv", sep = "")
        } else if (input$data_source == "Map Data") {
          paste("Scottish Agricultural Employees Regional Data - 2023.csv", sep = "")
        }
      },
      content = function(file) {
        if (input$data_source == "Chart Data") {
          write.csv(pivoted_chart_data(), file, row.names = FALSE)
        } else if (input$data_source == "Map Data") {
          write.csv(pivoted_regions_data(), file, row.names = FALSE)
        }
      }
    )
  })
}

# Testing module
content_demo <- function() {
  ui <- fluidPage(employeesMapUI("employees_map_test"))
  server <- function(input, output, session) {
    employeesMapServer("employees_map_test")
  }
  shinyApp(ui, server)
}

content_demo()

## File: module_farm_types.R 
 # File: module_farm_types.R

farmTypesUI <- function(id) {
  ns <- NS(id)
  tagList(
    sidebarLayout(
      sidebarPanel(
        width = 3,
        radioButtons(ns("data_type"), "Data Type", choices = c("Holdings" = "holdings", "Area" = "area", "Total from Standard Outputs" = "total", "Average standard outputs per holding" = "average"), selected = "holdings"),
        conditionalPanel(
          condition = paste0("input['", ns("tabs"), "'] == '", ns("bar"), "'"),  # Show variable select only on Bar Chart tab
          uiOutput(ns("variable_select"))  # Variable select UI
        )
      ),
      mainPanel(
        id = ns("mainpanel"),
        width = 9,
        tabsetPanel(
          id = ns("tabs"),
          tabPanel("Bar Chart", barChartUI(ns("bar_chart")), value = ns("bar")),
          tabPanel("Data Table",
                   DTOutput(ns("data_table")),
                   downloadButton(ns("downloadData"), "Download Data"), 
                   generateCensusTableFooter(),

                   value = ns("data"))
        )
      )
    )
  )
}

farmTypesServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    # Create reactive data excluding 'All' and depending only on data_type (for chart)
    filtered_data_chart <- reactive({
      farm_type %>%
        filter(`Main farm type` != "All")
    })
    
    # Create reactive data for the table with commas added to numeric values
    filtered_data_table <- reactive({
      filtered_data_chart() %>%
        mutate(across(where(is.numeric), comma))
    })
    
    # Get filtered data based on selected variables (only for bar chart)
    chart_data <- reactive({
      data <- filtered_data_chart()
      if (input$tabs == ns("bar") && !is.null(input$variables)) {
        data <- data %>%
          filter(`Main farm type` %in% input$variables)
      }
      data
    })
    
    # Select the appropriate column based on data_type
    y_col <- reactive({
      switch(input$data_type,
             "holdings" = "Holdings",
             "area" = "Hectares",
             "total" = "Total from Standard Outputs",
             "average" = "Average standard outputs per holding")
    })
    
    yAxisTitle <- reactive({
      switch(input$data_type,
             "holdings" = "Number of Holdings (x 1000)",
             "area" = "Area (hectares)",
             "total" = "Total from Standard Outputs (£)",
             "average" = "Average Standard Outputs per Holding (£)")
    })
    
    
    tooltip_unit <- reactive({
      # Your logic to determine the unit based on input or other conditions
      switch(input$data_type,
             "holdings" = "holdings",
             "area" = "hectares",
             "total" = "(£)",
             "average" = "(£)")
    })
    # 
    # unit_position <- reactive({
    #   if (input$data_type %in% c("holdings", "area")) {
    #     return("after");
    #   } else {
    #     return("before");
    #   }
    # })
    
    # Render the data table based only on data_type selection with 20 entries by default
    output$data_table <- renderDT({
      datatable(
        filtered_data_table() %>%
          select(`Main farm type`, y_col()),
        colnames = c("Main Farm Type", yAxisTitle()),
        options = list(pageLength = 20, scrollX = TRUE)  # Show 20 entries by default, enable horizontal scrolling
      )
    })
    
    # Create a download handler for the data
    output$downloadData <- downloadHandler(
      filename = function() {
        paste("Farm_Types_", input$data_type, ".xlsx", sep = "")
      },
      content = function(file) {
        write.xlsx(filtered_data_table() %>% 
                     select(`Main farm type`, y_col()), file, rowNames = FALSE)
      }
    )
    
    # Render the variable selection UI dynamically
    output$variable_select <- renderUI({
      choices <- unique(farm_type$`Main farm type`)
      selected <- setdiff(choices, "All")
      selectizeInput(
        ns("variables"), 
        "Click within the box to select variables", 
        choices = choices, 
        selected = selected,
        multiple = TRUE,
        options = list(
          plugins = list('remove_button')
        )
      )
    })
    
    # Render the bar chart using the filtered data
    barChartServer(
      id = "bar_chart",
      chart_data = chart_data,
      title = paste("Farm types in Scotland in", census_year),
      yAxisTitle = yAxisTitle,
      xAxisTitle = "Main farm type",
      unit = input$data_type,
      footer = census_footer,
      x_col = "Main farm type",
      y_col = y_col,
      tooltip_unit = tooltip_unit,
      #unit_position = unit_position,
      maintain_order = FALSE
    )
  })
}


## File: module_fertiliser_usage.R 
 # File: module_fertiliser_usage.R

fertiliserUsageUI <- function(id) {
  ns <- NS(id)
  tagList(
    sidebarLayout(
      sidebarPanel(
        width = 3,
        radioButtons(ns("data_type"), "Data Type", choices = c("Holdings" = "holdings", "Area" = "area"), selected = "holdings")
      ),
      mainPanel(
        id = ns("mainpanel"),
        width = 9,
        tabsetPanel(
          id = ns("tabs"),
          tabPanel("Bar Chart", barChartUI(ns("bar_chart")), value = ns("bar")),
          tabPanel("Data Table",
                   DTOutput(ns("data_table")),
                   tags$div(
                     style = "margin-top: 20px;",
                     downloadButton(ns("downloadData"), "Download Data"),
                     generateCensusTableFooter()
                     
                   ),
                   value = ns("data"))
        )
      )
    )
  )
}

fertiliserUsageServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    # Data for the chart (keeps numeric values unformatted)
    chart_data <- reactive({
      data <- manure_fertiliser
      if (!is.null(input$variables)) {
        data <- data %>%
          filter(`Fertiliser by type` %in% input$variables)
      }
      data
    })
    
    # Data for the table (formats numeric values with commas)
    table_data <- reactive({
      chart_data() %>%
        mutate(across(where(is.numeric), comma)) %>% 
        rename(Holdings = `2023 holdings`,
               Area = `2023 area`)
    })
    
    output$variable_select <- renderUI({
      choices <- unique(manure_fertiliser$`Fertiliser by type`)
      selected <- choices
      checkboxGroupInput(ns("variables"), "Choose variables to add to chart", choices = choices, selected = selected)
    })
    
    observeEvent(input$select_all_button, {
      updateCheckboxGroupInput(session, ns("variables"), selected = unique(manure_fertiliser$`Fertiliser by type`))
    })
    
    observeEvent(input$deselect_all_button, {
      updateCheckboxGroupInput(session, ns("variables"), selected = character(0))
    })
    
    y_col <- reactive({
      if (input$data_type == "holdings") {
        "2023 holdings"
      } else {
        "2023 area"
      }
    })
    
    yAxisTitle <- reactive({
      if (input$data_type == "holdings") {
        "Holdings"
      } else {
        "Area (1,000 hectares)"
      }
    })
    
    tooltip_unit <- reactive({
      if (input$data_type == "holdings") {
        "holdings"
      } else {
        "hectares"
      }
    })
    
    # Bar chart rendering using unformatted data
    barChartServer(
      id = "bar_chart",
      chart_data = chart_data,
      title = "Fertiliser Usage by Type in Scotland",
      yAxisTitle = yAxisTitle,
      xAxisTitle = "Fertiliser Type",
      unit = "",
      footer = census_footer,
      x_col = "Fertiliser by type",
      y_col = y_col,
      tooltip_unit = tooltip_unit
    )
    
    # Data table rendering using formatted data
    output$data_table <- renderDT({
      datatable(
        table_data(),
        options = list(
          scrollX = TRUE,  # Enable horizontal scrolling
          pageLength = 20  # Show 20 entries by default
        )
      )
    })
    
    # Download handler for the data table with appropriate naming
    output$downloadData <- downloadHandler(
      filename = function() {
        paste("Fertiliser_Usage_Data_", Sys.Date(), ".csv", sep = "")
      },
      content = function(file) {
        write.csv(table_data(), file, row.names = FALSE)
      }
    )
  })
}


## File: module_fruit.R 
 # File: module_fruit.R

fruitUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = unique(fruit_subregion$`Land use by category`)
        )
      ),     
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series' || input.tabsetPanel === 'Area Chart'",
        ns = ns,
        selectizeInput(
          ns("timeseries_variables"),
          "Click within the box to select variables",
          choices = unique(fruit_data$`Vegetables and fruits for human consumption`),
          selected = c(
            "Strawberries grown in open/under cover",
            "Raspberries grown in open/under cover",
            "Blackcurrants grown in open/under cover",
            "Blueberries grown in open/under cover",
            "Tomatoes grown in open/under cover",
            "Other fruit grown in open/under cover"
          ),
          multiple = TRUE,
          options = list(
            plugins = list('remove_button')
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Time Series Data" = "timeseries"),
          selected = "map"
        )
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Time Series", lineChartUI(ns("line"), note_type = 2)),
        tabPanel("Area Chart", areaChartUI(ns("area"), note_type = 2)),
        tabPanel("Data Table", 
                 DTOutput(ns("table")),
                 downloadButton(ns("download_data"), "Download Data"),
                 generateCensusTableFooter()

        )
      )
    )
  )
}

fruitServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    fruit_map <- fruit_subregion %>%
      select(-Scotland) %>%
      mutate(across(everything(), as.character)) %>%
      pivot_longer(cols = -`Land use by category`, names_to = "sub_region", values_to = "value") %>%
      mutate(value = safe_as_numeric(value))
    
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        fruit_map %>% filter(`Land use by category` == input$variable)
      }),
      unit = "hectares",
      footer = census_footer,
      variable = reactive(input$variable),
      title = paste("Fruit distribution by region in Scotland in", census_year),
      legend_title = "Area (hectares)"
    )
    
    chart_data <- reactive({
      req(input$timeseries_variables)
      filtered_data <- fruit_data %>%
        filter(`Vegetables and fruits for human consumption` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Vegetables and fruits for human consumption`, names_to = "year", values_to = "value") %>%
        mutate(year = as.numeric(year))  # Ensure year is numeric
      filtered_data
    })
    
    areaChartServer(
      id = "area",
      chart_data = chart_data,
      title = "Area used to grow fruit across time",
      yAxisTitle = "Area of fruit (hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = "Area used to grow fruit across time",
      yAxisTitle = "Area of fruit (hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    output$table <- renderDT({
      req(input$tabsetPanel == "Data Table")
      if (input$table_data == "map") {
        req(input$variable)
        fruit_map %>%
          filter(`Land use by category` == input$variable) %>%
          pivot_wider(names_from = sub_region, values_from = value) %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      } else {
        fruit_data %>%
          pivot_longer(cols = -`Vegetables and fruits for human consumption`, names_to = "year", values_to = "value") %>%
          pivot_wider(names_from = year, values_from = value) %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      }
    })
    
    output$download_data <- downloadHandler(
      filename = function() {
        if (input$table_data == "map") {
          paste("Fruit_Subregion_Data_", Sys.Date(), ".csv", sep = "")
        } else {
          paste("Fruit_Timeseries_Data_", Sys.Date(), ".csv", sep = "")
        }
      },
      content = function(file) {
        data <- if (input$table_data == "map") {
          fruit_map %>%
            filter(`Land use by category` == input$variable) %>%
            pivot_wider(names_from = sub_region, values_from = value)
        } else {
          fruit_data %>%
            pivot_longer(cols = -`Vegetables and fruits for human consumption`, names_to = "year", values_to = "value") %>%
            pivot_wider(names_from = year, values_from = value)
        }
        write.csv(data, file, row.names = FALSE)
      }
    )
  })
}

fruit_demo <- function() {
  ui <- fluidPage(fruitUI("fruit_test"))
  server <- function(input, output, session) {
    fruitServer("fruit_test")
  }
  shinyApp(ui, server)
}
fruit_demo()

## File: module_gas_emissions.R 
 

## File: module_human_vegetables.R 
 # File: module_human_vegetables.R

humanVegetablesUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = unique(human_vegetables_subregion$`Land use by category`)
        )
      ),     
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series' || input.tabsetPanel === 'Area Chart'",
        ns = ns,
        checkboxGroupInput(
          ns("timeseries_variables"),
          "Select Time Series Variables",
          choices = unique(human_vegetables_data$`Vegetables and fruits for human consumption`),
          selected = c(
            "Peas for canning, freezing or drying",
            "Beans for canning, freezing or drying",
            "Turnips/swedes",
            "Calabrese",
            "Cauliflower",
            "Carrots"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Time Series Data" = "timeseries"),
          selected = "map"
        )
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Time Series", lineChartUI(ns("line"))),
        tabPanel("Area Chart", areaChartUI(ns("area"))),
        tabPanel("Data Table", 
                 DTOutput(ns("table")),
                 downloadButton(ns("download_data"), "Download Data"),
                 generateCensusTableFooter()

        )
      )
    )
  )
}

humanVegetablesServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    human_vegetables_map <- human_vegetables_subregion %>%
      select(-Scotland) %>%
      mutate(across(everything(), as.character)) %>%
      pivot_longer(cols = -`Land use by category`, names_to = "sub_region", values_to = "value") %>%
      mutate(value = safe_as_numeric(value))
    
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        human_vegetables_map %>% filter(`Land use by category` == input$variable)
      }),
      unit = "hectares",
      footer = census_footer,
      variable = reactive(input$variable),
      title = paste("Vegetables for human consumption distribution by region in Scotland in", census_year),
      legend_title = "Area (hectares)"
    )
    
    chart_data <- reactive({
      req(input$timeseries_variables)
      filtered_data <- human_vegetables_data %>%
        filter(`Vegetables and fruits for human consumption` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Vegetables and fruits for human consumption`, names_to = "year", values_to = "value") %>%
        mutate(year = as.numeric(year))  # Ensure year is numeric
      filtered_data
    })
    
    areaChartServer(
      id = "area",
      chart_data = chart_data,
      title = "Area used to grow vegetables for human consumption across time",
      yAxisTitle = "Area of vegetables (1,000 hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = "Area used to grow vegetables for human consumption across time",
      yAxisTitle = "Area of vegetables (1,000 hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    output$table <- renderDT({
      req(input$tabsetPanel == "Data Table")
      if (input$table_data == "map") {
        req(input$variable)
        human_vegetables_map %>%
          filter(`Land use by category` == input$variable) %>%
          pivot_wider(names_from = sub_region, values_from = value) %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      } else {
        human_vegetables_data %>%
          pivot_longer(cols = -`Vegetables and fruits for human consumption`, names_to = "year", values_to = "value") %>%
          pivot_wider(names_from = year, values_from = value) %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      }
    })
    
    output$download_data <- downloadHandler(
      filename = function() {
        if (input$table_data == "map") {
          paste("Human_Vegetables_Subregion_Data_", Sys.Date(), ".csv", sep = "")
        } else {
          paste("Human_Vegetables_Timeseries_Data_", Sys.Date(), ".csv", sep = "")
        }
      },
      content = function(file) {
        data <- if (input$table_data == "map") {
          human_vegetables_map %>%
            filter(`Land use by category` == input$variable) %>%
            pivot_wider(names_from = sub_region, values_from = value)
        } else {
          human_vegetables_data %>%
            pivot_longer(cols = -`Vegetables and fruits for human consumption`, names_to = "year", values_to = "value") %>%
            pivot_wider(names_from = year, values_from = value)
        }
        write.csv(data, file, row.names = FALSE)
      }
    )
  })
}

human_vegetables_demo <- function() {
  ui <- fluidPage(humanVegetablesUI("human_vegetables_test"))
  server <- function(input, output, session) {
    humanVegetablesServer("human_vegetables_test")
  }
  shinyApp(ui, server)
}
human_vegetables_demo()

## File: module_information.R 
 informationUI <- function(id) {
  ns <- NS(id)
  tagList(
    div(
      class = "container",
      # Add new section at the top
      tags$div(style = "font-size: 24px; font-weight: bold;", "Other Sources:"),  # Bigger and bold title
      tags$div(
        style = "margin-top: 20px;",
        tags$div(style = "font-size: 18px; font-weight: bold;", "Scottish Agriculture Greenhouse Gas Emissions and Nitrogen Use"),
        p(HTML('For comprehensive data and insights, visit the <a href="https://www.gov.scot/collections/scottish-agriculture-greenhouse-gas-emissions-and-nitrogen-use/" target="_blank">Scottish Agriculture Greenhouse Gas Emissions and Nitrogen Use</a> collection.'))
      ),
      # Existing sections
      tags$div(
        style = "margin-top: 20px;",
        tags$div(style = "font-size: 18px; font-weight: bold;", "Scottish Greenhouse Gas Statistics"),
        p(HTML('Official estimates of greenhouse gas emissions are available in the <a href="https://www.gov.scot/publications/scottish-greenhouse-gas-statistics-2022/documents/" target="_blank">Scottish Greenhouse Gas Statistics 2022</a> publication.')),
        tags$div(
          style = "margin-top: 20px;",
          tags$div(style = "font-size: 18px; font-weight: bold;", "British Survey of Fertiliser Practice Report"),
          p(HTML('To learn more about fertiliser practices in the UK, you can access the <a href="https://www.gov.uk/government/statistics/latest-british-survey-of-fertiliser-practice-report" target="_blank">British Survey of Fertiliser Practice Report</a>.'))
        ),
         tags$div(style = "font-size: 18px; font-weight: bold;", "Greenhouse Gas Inventory Reports"),
        p(a(href = "https://www.gov.scot/publications/estimated-dairy-emissions-mitigation-smart-inventory/", "Greenhouse gas inventory: estimated dairy emissions and their mitigation", target = "_blank")),
        tags$div(style = "font-size: 18px; font-weight: bold;", "Climate change evidence reports"),
        p(a(href = "https://www.gov.scot/publications/resas-climate-change-evidence-dairy-farmer-led-group/", "Dairy Farmer-led Group: climate change evidence", target = "_blank")),
        p(a(href = "https://www.gov.scot/publications/resas-climate-change-evidence-arable-farmer-led-group/", "Arable Farmer-led Group: climate change evidence", target = "_blank")),
        p(a(href = "https://www.gov.scot/publications/resas-climate-change-evidence-huc-farmer-led-group/", "Hill, Upland and Crofting Farmer-led Group: climate change evidence", target = "_blank")),
        p(a(href = "https://www.gov.scot/publications/pig-sector-flg-climate-change-greenhouse-gas-evidence/", "Pig Sector Farmer-Led Climate Change Group: climate change and greenhouse gas evidence", target = "_blank")),
        tags$div(style = "font-size: 18px; font-weight: bold;", "Greenhouse Gas Inventory Reports"),
        p(a(href = "https://www.gov.scot/publications/estimated-dairy-emissions-mitigation-smart-inventory/", "Greenhouse gas inventory: estimated dairy emissions and their mitigation", target = "_blank")),
        p(a(href = "https://www.gov.scot/publications/estimated-arable-emissions-mitigation-smart-inventory/", "Greenhouse gas inventory: estimated arable emissions and their mitigation", target = "_blank")),
        p(a(href = "https://www.gov.scot/publications/disaggregating-headline-smart-inventory-figures/", "Greenhouse gas emissions - agricultural: disaggregating headline figures", target = "_blank"))
      ),
      tags$div(style = "font-size: 24px; font-weight: bold; margin-top: 40px;", "Glossary:"),  # Bigger and bold title
      tags$div(
        style = "margin-top: 20px;",
        tags$div(style = "font-size: 18px; font-weight: bold;", "Carbon dioxide (CO2):"),
        p("Carbon dioxide is one of the main gases responsible for climate change. It is mostly emitted through the oxidation of carbon in fossil fuels, e.g. burning coal."),
        tags$div(style = "font-size: 18px; font-weight: bold;", "Greenhouse gas:"),
        p("A greenhouse gas is a gas which absorbs infrared radiation emitted from the surface of the Earth, helping to retain a portion of that energy in the atmosphere as heat."),
        tags$div(style = "font-size: 18px; font-weight: bold;", "LULUCF:"),
        p("Estimates of emissions and removals from land use, land use change and forestry (LULUCF) depend critically on assumptions made on the rate of loss or gain of carbon in Scotland’s carbon-rich soils. In Scotland, LULUCF activities, taken as a whole, acts as a slight source in recent years although acted as a net sink between 2009-2017, absorbing more greenhouse gas emissions than it releases."),
        tags$div(style = "font-size: 18px; font-weight: bold;", "Methane (CH4):"),
        p("Methane is a greenhouse gas that is around 28 times more potent in the atmosphere than CO2 over a 100-year time horizon. Main sources include agriculture and landfill."),
        tags$div(style = "font-size: 18px; font-weight: bold;", "Metric tonne of carbon dioxide equivalent (MtCO₂e):"),
        p("Provides an estimate of total GHG emissions taking into account the different effects that different gases have on climate change, known as their global warming potential (GWP)."),
        tags$div(style = "font-size: 18px; font-weight: bold;", "Nitrous oxide (N2O):"),
        p("Nitrous oxide is a greenhouse gas that is around 265 times more potent in the atmosphere than CO2 over a 100-year time horizon. The main source is agricultural soil.")
      )
    )
  )
}

## File: module_land_use_summary.R 
 landUseSummaryUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = unique(land_use_subregion$`Land use by category`)
        )
      ), 
      conditionalPanel(
        condition = "input.tabsetPanel === 'Summary'",
        ns = ns,
        div(
          style = "font-size: 24px; font-weight: bold;",
          " "
        )
      ), 
      conditionalPanel(
        condition = "input.tabsetPanel === 'Bar Chart'",
        ns = ns,
        checkboxGroupInput(
          ns("variables"), 
          "Choose variables to add to chart", 
          choices = c("Total crops, fallow, and set-aside", "Total grass", "Rough grazing", 
                      "Total sole right agricultural area", "Common grazings"),
          selected = c("Total crops, fallow, and set-aside", "Total grass", "Rough grazing", 
                       "Total sole right agricultural area", "Common grazings")
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series'",
        ns = ns,
        checkboxGroupInput(
          ns("timeseries_variables"),
          "Select Time Series Variables",
          choices = unique(land_use_data$`Crop/Land use`),
          selected = c(
            "Common grazings",
            "Rough grazing",
            "Total crops, fallow, and set-aside",
            "Total grass",
            "Cauliflower",
            "Total sole right agricultural area"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Time Series Data" = "timeseries"),
          selected = "map"
        )
      )
    ),
    mainPanel(
      id = ns("mainpanel"),
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Bar Chart", barChartUI(ns("bar_chart"))),
        tabPanel("Time Series", lineChartUI(ns("line"))),
        tabPanel("Data Table", 
                 DTOutput(ns("table")),
                 tags$div(
                   style = "margin-top: 20px;",
                   downloadButton(ns("download_data"), "Download Data"),
                   generateCensusTableFooter()
                 )
        )
      )
    )
  )
}


landUseSummaryServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    # Map data remains unformatted for proper rendering
    land_use_map <- reactive({
      land_use_subregion %>%
        select(-Scotland) %>%
        mutate(across(everything(), as.character)) %>%
        pivot_longer(cols = -`Land use by category`, names_to = "sub_region", values_to = "value") %>%
        mutate(value = as.numeric(value))
    })
    
    # Table data with formatted values for better readability
    table_data <- reactive({
      if (input$table_data == "map") {
        land_use_subregion %>%
          mutate(across(where(is.numeric), comma))  # Format numeric columns with commas
      } else {
        land_use_data %>%
          mutate(across(where(is.numeric), comma))  # Format numeric columns with commas
      }
    })
    
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        land_use_map() %>% filter(`Land use by category` == input$variable)
      }),
      unit = "hectares",
      footer = census_footer,
      variable = reactive(input$variable),
      title = paste("Land use by region in Scotland in", census_year),
      legend_title = "Area (hectares)"
    )
    
    chart_data <- reactive({
      agricultural_area_hectares %>%
        filter(`Crop/Land use` %in% input$variables) %>%
        select(`Crop/Land use`, `2023 Area`) %>%
        rename(Variable = `Crop/Land use`, Value = `2023 Area`)
    })
    
    timeseries_data <- reactive({
      req(input$timeseries_variables)
      land_use_data %>%
        filter(`Crop/Land use` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
        mutate(year = as.numeric(year))  # Ensure year is numeric
    })
    

    
    barChartServer(
      id = "bar_chart",
      chart_data = chart_data,
      title = paste("Agricultural area by land use type in", census_year),
      yAxisTitle = "Area (1,000 hectares)",
      xAxisTitle = "Land use type",
      unit = "hectares",
      footer = census_footer,
      x_col = "Variable",
      y_col = "Value",
      tooltip_unit= reactive("hectares")
    )
    
    lineChartServer(
      id = "line",
      chart_data = timeseries_data,
      title = "Land use over time",
      yAxisTitle = "Area of land use (1,000 hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    # Render the data table with formatted values
    output$table <- renderDT({
      datatable(
        table_data(),
        options = list(
          scrollX = TRUE,  # Enable horizontal scrolling
          pageLength = 20  # Show 20 entries by default
        )
      )
    })
    
    # Download handler with formatted values
    output$download_data <- createDownloadHandler(
      input = input,
      file_map_name = "Land Use Subregion Data 2023.xlsx",
      file_timeseries_name = "Land Use Timeseries Data 2012 to 2023.xlsx",
      map_data = table_data(),  # Use the formatted data for download
      timeseries_data = table_data()  # Use the formatted data for download
    )
  })
}


land_use_demo <- function() {
  ui <- fluidPage(landUseSummaryUI("land_use_test"))
  server <- function(input, output, session) {
    landUseSummaryServer("land_use_test")
  }
  shinyApp(ui, server)
}

land_use_demo()

## File: module_legal_responsibility.R 
 # File: module_legal_responsibility.R

legalResponsibilityUI <- function(id) {
  ns <- NS(id)
  tagList(
    sidebarLayout(
      sidebarPanel(
        width = 3,
        radioButtons(ns("data_type"), "Data Type", choices = c("Holdings" = "holdings", "Area" = "area"), selected = "holdings"),
        checkboxGroupInput(ns("selected_variables"), "Select Variables", choices = NULL, selected = NULL)
      ),
      mainPanel(
        id = ns("mainpanel"),
        width = 9,
        tabsetPanel(
          id = ns("tabs"),
          tabPanel("Bar Chart", barChartUI(ns("bar_chart")), value = ns("bar")),
          tabPanel("Data Table",
                   DTOutput(ns("data_table")),
                   downloadButton(ns("downloadData"), "Download Data"),
                   generateCensusTableFooter(),
                   value = ns("data"))
        )
      )
    )
  )
}
legalResponsibilityServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    # Chart data remains unformatted for proper rendering
    chart_data <- reactive({
      data <- legal_responsibility %>%
        filter(`Legal responsibility` != "Total")
      data
    })
    
    # Table data with formatted values for better readability
    table_data <- reactive({
      chart_data() %>%
        mutate(across(where(is.numeric), comma))  # Format numeric columns with commas
    })
    
    initial_order <- reactive({
      data <- chart_data() %>%
        arrange(desc(Holdings))
      data$`Legal responsibility`
    })
    
    observe({
      choices <- unique(chart_data()$`Legal responsibility`)
      selected <- choices
      updateCheckboxGroupInput(session, "selected_variables", choices = choices, selected = selected)
    })
    
    y_col <- reactive({
      if (input$data_type == "holdings") {
        "Holdings"
      } else {
        "Area of holdings"
      }
    })
    
    yAxisTitle <- reactive({
      if (input$data_type == "holdings") {
        "Number of Holdings (1,000)"
      } else {
        "Area of Holdings (1,000 hectares)"
      }
    })
    
    
    tooltip_unit <- reactive({
      if (input$data_type == "holdings") {
        "holdings"
      } else {
        "hectares"
      }
    })
    
    barChartServer(
      id = "bar_chart",
      chart_data = reactive({
        data <- chart_data() %>%
          filter(`Legal responsibility` %in% input$selected_variables) %>%
          mutate(`Legal responsibility` = factor(`Legal responsibility`, levels = initial_order())) %>%
          arrange(`Legal responsibility`)
        data
      }),
      title = paste("Legal responsibility of holdings in Scotland", census_year),
      yAxisTitle = yAxisTitle,
      xAxisTitle = "Legal responsibility",
      footer = census_footer,
      x_col = "Legal responsibility",
      y_col = y_col,
      tooltip_unit = tooltip_unit,
      maintain_order = TRUE
    )
    
    # Render the data table with formatted values
    output$data_table <- renderDT({
      datatable(
        table_data(),
        options = list(
          scrollX = TRUE,  # Enable horizontal scrolling
          pageLength = 20  # Show 20 entries by default
        )
      )
    })
    
    # Download handler with formatted values
    output$downloadData <- downloadHandler(
      filename = function() {
        paste("Legal_Responsibility_Data_", Sys.Date(), ".csv", sep = "")
      },
      content = function(file) {
        write.csv(table_data(), file, row.names = FALSE)
      }
    )
  })
}

## File: module_line_chart.R 
 

## File: module_manure.R 
 

# Load the data
load("module_2023.RData")

# Convert relevant columns to character before pivoting
manure_qty <- manure_qty %>%
  mutate(across(everything(), as.character))

# Transform the data, filtering out "All respondents"
manure_data_long <- manure_qty %>%
  filter(Region != "All respondents") %>%
  pivot_longer(cols = -Region, names_to = "variable", values_to = "value") %>%
  mutate(value = as.numeric(value)) %>%
  rename(region = Region)

# Extract the data for "All respondents"
national_data <- manure_qty %>%
  filter(Region == "All respondents") %>%
  select(-Region) %>%
  pivot_longer(cols = everything(), names_to = "variable", values_to = "value") %>%
  mutate(value = as.numeric(value)) # Ensure numeric values

# List of variables for radio buttons
variables_manure <- colnames(manure_qty)[-1]


# Helper function to format numbers with commas and appropriate decimal places
format_number <- function(number) {
  if (is.na(number)) {
    return("N/A")
  } else if (number %% 1 == 0) {
    format(number, big.mark = ",", scientific = FALSE)
  } else {
    format(round(number, 2), big.mark = ",", scientific = FALSE, nsmall = 2)
  }
}

valueBoxManureUI <- function(id, title, value, unit) {
  numeric_value <- as.numeric(value) # Ensure the value is numeric
  formatted_value <- format_number(numeric_value)
  
  box(
    class = "value-box",
    title = NULL,
    width = 12,
    solidHeader = TRUE,
    div(
      style = "display: flex; flex-direction: column; justify-content: center; align-items: center; height: 100%; padding: 5px;",  # Center content vertically and horizontally
      div(
        style = "font-size: 14px; font-weight: bold; margin-bottom: 15px; text-align: center;",  # Adjusted title style and added margin for vertical gap
        h5(class = "value-box-title", title)
      ),
      div(
        style = "text-align: center;",  # Center the value and unit together
        h3(HTML(formatted_value), style = "font-size: 24px; font-weight: bold; margin: 0;"),  # Main value centered
        div(style = "font-size: 16px; font-weight: normal; margin-top: 5px;", unit)  # Unit placed below the value, also centered
      )
    ),
    style = "border: 1px solid white; background-color: transparent; box-shadow: none; display: flex; align-items: center; justify-content: center;"  # Center the entire content of the box
  )
}



manureUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      uiOutput(ns("sidebar_ui"))
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabs"),
        tabPanel("Summary",
                 fluidRow(
                   column(width = 4,
                          valueBoxManureUI(ns("total_manure"), "Total manure applied", 
                                           national_data$value[national_data$variable == "Manure"], "tonnes"),
                          p(style = "color: white;", "/"),
                          valueBoxManureUI(ns("total_holdings"), "Total holdings", 
                                           national_data$value[national_data$variable == "Holdings"], "holdings"),
                          p(style = "color: white;", "/"),
                          valueBoxManureUI(ns("total_application_rate"), "Total application rate", 
                                           national_data$value[national_data$variable == "Application rate"], "tonnes / hectare"),
                          p(style = "color: white;", "/"),
                          valueBoxManureUI(ns("average_mixed_sward"), "Average mixed sward per holding", 
                                           national_data$value[national_data$variable == "Average mixed sward area per holding"], "hectares"),
                          p(style = "color: white;", "/"),
                          valueBoxManureUI(ns("average_grassland"), "Average grassland area per holding", 
                                           national_data$value[national_data$variable == "Average grassland area per holding"], "hectares")
                   ),
                   column(width = 8,
                          mapRegionsUI(ns("map"))
                   )
                 ),
                 value = "map"
        ),
        tabPanel("Data Table", 
                 DTOutput(ns("data_table")), 
                 downloadButton(ns("downloadData"), "Download Data"), 
                 value = "data_table") ,
        footer = generate2023ModuleTableFooter()  
  )
  )
  )
}

manureServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    output$sidebar_ui <- renderUI({
      req(input$tabs)
      if (input$tabs == "map") {
        radioButtons(ns("variable"), "Select Variable", choices = variables_manure)
      } else if (input$tabs == "data_table") {
        radioButtons(ns("data_source"), "Choose data to show:", choices = c("Map Data"))
      }
    })
    
    mapRegionsServer(
      id = "map",
      data = reactive({
        req(input$variable)
        manure_data_long %>%
          filter(variable == input$variable)
      }),
      unit = " ",
      footer = '<div style="font-size: 16px; font-weight: bold;"><a href="https://www.gov.scot/publications/results-from-the-scottish-agricultural-census-module-june-2023/" target="_blank">Source: Scottish Agricultural Census: Module June 2023</a></div>',
      variable = reactive(input$variable),
      title = paste("Manure quantity by region in Scotland in", census_year),
    )
    
    output$data_table <- renderDT({
      req(input$data_source)
      
      if (input$data_source == "Map Data") {
        datatable(
          manure_qty %>%
            mutate(across(where(is.numeric) & !contains("Year"), comma)),
          options = list(
            scrollX = TRUE,     # Enable horizontal scrolling
            pageLength = 20     # Show 20 entries by default
          )
        )
      }
    })
    
    output$downloadData <- downloadHandler(
      filename = function() {
        paste("map_data", Sys.Date(), ".csv", sep = "")
      },
      content = function(file) {
        write.csv(manure_qty, file, row.names = FALSE)
      }
    )
  })
}


# Test the module
content_demo <- function() {
  ui <- fluidPage(manureUI("manure_map_test"))
  server <- function(input, output, session) {
    manureServer("manure_map_test")
  }
  shinyApp(ui, server)
}

# Uncomment the line below to run the test
#content_demo()

## File: module_map.R 
 

## File: module_nitrogen_usage.R 
 # Load the data
load("module_2023.RData")  # Assuming nitrogen_400 and nitrogen_250 are loaded from this file

# Transform the data, filtering out "All respondents"
nitrogen_400_long <-  nitrogen_400 %>%
  filter(Region != "All respondents") %>%
  pivot_longer(cols = -Region, names_to = "variable", values_to = "value") %>%
  mutate(value = as.numeric(value)) %>%
  rename(region = Region)


nitrogen_250_long <-  nitrogen_250 %>%
  filter(Region != "All respondents") %>%
  pivot_longer(cols = -Region, names_to = "variable", values_to = "value") %>%
  mutate(value = as.numeric(value)) %>%
  rename(region = Region)


# Ensure that national_data_400 and national_data_250 are data frames
national_data_400 <- as.data.frame(
  nitrogen_400 %>%
    filter(Region == "All respondents") %>%
    select(-Region) %>%
    pivot_longer(cols = everything(), names_to = "variable", values_to = "value")
)

national_data_250 <- as.data.frame(
  nitrogen_250 %>%
    filter(Region == "All respondents") %>%
    select(-Region) %>%
    pivot_longer(cols = everything(), names_to = "variable", values_to = "value")
)

# Now use these data frames directly in your UI function


# List of variables for radio buttons
variables_nitrogen <- colnames(nitrogen_400)[-1]





valueBoxNitrogenUI <- function(id, title, value, unit) {
  numeric_value <- as.numeric(value) # Ensure the value is numeric
  formatted_value <- format_number(numeric_value)
  
  box(
    class = "value-box",
    title = NULL,
    width = 12,
    solidHeader = TRUE,
    div(
      style = "display: flex; flex-direction: column; justify-content: center; align-items: center; height: 100%; padding: 5px;",  # Center content vertically and horizontally
      div(
        style = "font-size: 14px; font-weight: bold; margin-bottom: 15px; text-align: center;",  # Adjusted title style and added margin for vertical gap
        h5(class = "value-box-title", title)
      ),
      div(
        style = "text-align: center;",  # Center the value and unit together
        h3(HTML(formatted_value), style = "font-size: 24px; font-weight: bold; margin: 0;"),  # Main value centered
        div(style = "font-size: 16px; font-weight: normal; margin-top: 5px;", unit)  # Unit placed below the value, also centered
      )
    ),
    style = "border: 1px solid white; background-color: transparent; box-shadow: none; display: flex; align-items: center; justify-content: center;"  # Center the entire content of the box
  )
}

nitrogenUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      uiOutput(ns("sidebar_ui"))
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabs"),
        tabPanel("Nitrogen Application (400 kg/ha cap)",
                 fluidRow(
                   column(width = 4,
                          valueBoxNitrogenUI(ns("total_nitrogen_400"), "Total nitrogen applied", 
                                             national_data_400[national_data_400$variable == "Total nitrogen", "value"], "kg"),
                          p(style = "color: white;", "/"),
                          valueBoxNitrogenUI(ns("total_holdings_400"), "Total holdings", 
                                             national_data_400[national_data_400$variable == "Holdings", "value"], "holdings"),
                          p(style = "color: white;", "/"),
                          valueBoxNitrogenUI(ns("total_application_rate_400"), "Average application rate", 
                                             national_data_400[national_data_400$variable == "Application rate", "value"], "kg / hectares"),
                          p(style = "color: white;", "/"),
                          valueBoxNitrogenUI(ns("average_mixed_sward_400"), "Average mixed sward per holding", 
                                             national_data_400[national_data_400$variable == "Average mixed sward area per holding", "value"], "ha"),
                          p(style = "color: white;", "/"),
                          valueBoxNitrogenUI(ns("average_grassland_400"), "Average grassland area per holding", 
                                             national_data_400[national_data_400$variable == "Average grassland area per holding", "value"], "ha")
                   ),
                   column(width = 8,
                          mapRegionsUI(ns("map_400"))
                   )
                 ),
                 value = "map_400"
        ),
        tabPanel("Nitrogen Application (250 kg/ha cap)",
                 fluidRow(
                   column(width = 4,
                          valueBoxNitrogenUI(ns("total_nitrogen_250"), "Total nitrogen applied", 
                                             national_data_250[national_data_250$variable == "Total nitrogen", "value"], "kg"),
                          p(style = "color: white;", "/"),
                          valueBoxNitrogenUI(ns("total_holdings_250"), "Total holdings", 
                                             national_data_250[national_data_250$variable == "Holdings", "value"], "holdings"),
                          p(style = "color: white;", "/"),
                          valueBoxNitrogenUI(ns("total_application_rate_250"), "Total application rate", 
                                             national_data_250[national_data_250$variable == "Application rate", "value"], "kg / hectare"),
                          p(style = "color: white;", "/"),
                          valueBoxNitrogenUI(ns("average_mixed_sward_250"), "Average mixed sward per holding", 
                                             national_data_250[national_data_250$variable == "Average mixed sward area per holding", "value"], "hectare"),
                          p(style = "color: white;", "/"),
                          valueBoxNitrogenUI(ns("average_grassland_250"), "Average grassland area per holding", 
                                             national_data_250[national_data_250$variable == "Average grassland area per holding", "value"], "hectare")
                   ),
                   column(width = 8,
                          mapRegionsUI(ns("map_250"))
                   )
                 ),
                 value = "map_250"
        ),
        tabPanel("Data Table", 
                 DTOutput(ns("data_table")),
                 downloadButton(ns("downloadData"), "Download Data"), 
                 value = "data_table"
        ),
        footer = generate2023ModuleTableFooter()  
      )
    )
  )
}



nitrogenServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    output$sidebar_ui <- renderUI({
      req(input$tabs)
      if (input$tabs == "map_400") {
        radioButtons(ns("variable_400"), "Select Variable", choices = variables_nitrogen)
      } else if (input$tabs == "map_250") {
        radioButtons(ns("variable_250"), "Select Variable", choices = variables_nitrogen)
      } else if (input$tabs == "data_table") {
        radioButtons(ns("data_source"), "Choose data to show:", choices = c("400 kg/ha cap", "250 kg/ha cap"))
      }
    })
    
    mapRegionsServer(
      id = "map_400",
      data = reactive({
        req(input$variable_400)
        nitrogen_400_long %>%
          filter(variable == input$variable_400)
      }),
      unit = " ",
      footer = '<div style="font-size: 16px; font-weight: bold;"><a href="https://www.gov.scot/publications/results-from-the-scottish-agricultural-census-module-june-2023/" target="_blank">Source: Scottish Agricultural Census: Module June 2023</a></div>',
      variable = reactive(input$variable_400),
      title = paste("Nitrogen usage (400 kg/ha cap) by region in Scotland in", census_year),
        )
    
    mapRegionsServer(
      id = "map_250",
      data = reactive({
        req(input$variable_250)
        nitrogen_250_long %>%
          filter(variable == input$variable_250)
      }),
      unit = " ",
      footer = '<div style="font-size: 16px; font-weight: bold;"><a href="https://www.gov.scot/publications/results-from-the-scottish-agricultural-census-module-june-2023/" target="_blank">Source: Scottish Agricultural Census: Module June 2023</a></div>',
      variable = reactive(input$variable_250),
      title = paste("Nitrogen usage (250 kg/ha cap) by region in Scotland in", census_year),
    )
    
    output$data_table <- renderDT({
      req(input$data_source)
      
      if (input$data_source == "400 kg/ha cap") {
        datatable(
          nitrogen_400 %>%
            mutate(across(where(is.numeric) & !contains("Year"), comma)),
          options = list(
            scrollX = TRUE,     # Enable horizontal scrolling
            pageLength = 20     # Show 20 entries by default
          )
        )
      } else if (input$data_source == "250 kg/ha cap") {
        datatable(
          nitrogen_250 %>%
            mutate(across(where(is.numeric) & !contains("Year"), comma)),
          options = list(
            scrollX = TRUE,     # Enable horizontal scrolling
            pageLength = 20     # Show 20 entries by default
          )
        )
      }
    })
    
    
    output$downloadData <- downloadHandler(
      filename = function() {
        if (input$data_source == "400 kg/ha cap") {
          paste("nitrogen_data_400", Sys.Date(), ".csv", sep = "")
        } else {
          paste("nitrogen_data_250", Sys.Date(), ".csv", sep = "")
        }
      },
      content = function(file) {
        if (input$data_source == "400 kg/ha cap") {
          write.csv(nitrogen_400, file, row.names = FALSE)
        } else {
          write.csv(nitrogen_250, file, row.names = FALSE)
        }
      }
    )
  })
}

# Test the module
content_demo <- function() {
  ui <- fluidPage(nitrogenUI("nitrogen_map_test"))
  server <- function(input, output, session) {
    nitrogenServer("nitrogen_map_test")
  }
  shinyApp(ui, server)
}

# Uncomment the line below to run the test
# content_demo()

## File: module_occupiers.R 
 occupiersUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      uiOutput(ns("sidebar_ui"))
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabs"),
        tabPanel("Map", mapUI(ns("map")), value = "map"),
        tabPanel("Population Pyramid", 
                 div(
                   htmlOutput(ns("pyramid_title")),
                   highchartOutput(ns("pyramid_chart"), height = "500px"),
                   htmlOutput(ns("pyramid_footer")),
                   div(
                     class = "note",
                     style = "margin-top: 20px; padding: 10px; border-top: 1px solid #ddd;",
                     HTML(
                       "<strong>Note:</strong><ul>
                          <li>To add or remove a series from the chart, select/deselect the variable from the sidebar menu.</li>
                          <li>You can see data values for a specific variable by hovering your mouse over the bar.</li>
                        </ul>"
                     )
                   )
                 ), 
                 value = "bar_chart"),
        tabPanel("Time Series", lineChartUI(ns("line_chart")), value = "timeseries"),
        tabPanel("Data Table", 
                 DTOutput(ns("data_table")),
                 downloadButton(ns("downloadData"), "Download Data"), 
                 generateCensusTableFooter(),

                 value = "data_table")
      )
    )
  )
}


occupiersServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    # Data Processing for Bar Chart
    chart_data <- reactive({
      occupiers_age_gender %>%
        filter(`Occupier working time` == "Total working occupiers") %>%
        select(-`Male Total`, -`Female Total`) %>%
        pivot_longer(cols = -`Occupier working time`, names_to = "Gender_Age", values_to = "Count") %>%
        separate(Gender_Age, into = c("Gender", "Age"), sep = " ", extra = "merge") %>%
        mutate(Count = as.numeric(Count))
    })
    
    
    # Data Processing for Map
    regions_data <- reactive({
      occupiers_employees_subregion %>%
        select(-Scotland) %>%
        mutate(across(everything(), as.character)) %>%  # Ensure all columns are characters
        pivot_longer(cols = -`Occupiers and employees by category`, names_to = "sub_region", values_to = "value") %>%
        mutate(value = ifelse(is.na(as.numeric(value)), NA, as.numeric(value))) %>%
        filter(`Occupiers and employees by category` %in% c(
          "Total working Occupiers", 
          "Occupiers not working on the holding"
        ))
    })
    
    # Data Processing for Timeseries
    occupiers_employees <- occupiers_employees %>%
      mutate(across(starts_with("20"), safe_as_numeric))
    
    occupiers_timeseries_data <- reactive({
      occupiers_employees %>%
        pivot_longer(cols = -`Occupiers and employees by category`, names_to = "Year", values_to = "Value") %>%
        mutate(Year = as.numeric(Year)) %>%
        filter(grepl("occupiers", `Occupiers and employees by category`, ignore.case = TRUE))
    })
    
    # Sidebar UI
    output$sidebar_ui <- renderUI({
      req(input$tabs)
      if (input$tabs == "map") {
        radioButtons(ns("variable"), "Select Variable", choices = c(
          "Total working Occupiers", 
          "Occupiers not working on the holding"
        ))
      } else if (input$tabs == "data_table") {
        radioButtons(ns("data_source"), "Choose data to show:", choices = c("Map Data", "Population Pyramid Data",  "Timeseries Data"))
      } else if (input$tabs == "timeseries") {
        checkboxGroupInput(
          ns("variables"), 
          "Choose variables to add to chart", 
          choices = unique(occupiers_timeseries_data()$`Occupiers and employees by category`), 
          selected = c('Occupiers - full time', 'Total working occupiers', 'Occupiers not working on the holding')
        )
      } else {
        checkboxGroupInput(ns("variables_bar"), "Choose age groups to display", choices = unique(chart_data()$Age), selected = unique(chart_data()$Age))
      }
    })
    
    # Bar Chart - Filtered Data
    filtered_data <- reactive({
      req(input$variables_bar)
      chart_data() %>%
        filter(Age %in% input$variables_bar) %>%
        mutate(Count = ifelse(Gender == "Female", -Count, Count))
    })
    
    # Bar Chart Title with Census Year
    output$pyramid_title <- renderUI({
      HTML(paste0("<div style='font-size: 20px; font-weight: bold;'>Breakdown of occupiers by age and gender in ", census_year, "</div>"))
    })
    
    # Bar Chart Footer
    output$pyramid_footer <- renderUI({
      HTML(census_footer)
    })
    
    # Bar Chart - Output
    output$pyramid_chart <- renderHighchart({
      data <- filtered_data()
      female_data <- data %>% filter(Gender == "Female") %>% select(y = Count) %>% pull()
      male_data <- data %>% filter(Gender == "Male") %>% select(y = Count) %>% pull()
      categories <- data %>% filter(Gender == "Male") %>% select(Age) %>% pull()
      max_count <- max(abs(c(male_data, female_data)), na.rm = TRUE)
      
      highchart() %>%
        hc_chart(type = "bar") %>%
        hc_xAxis(categories = categories, reversed = FALSE) %>%
        hc_yAxis(
          min = -max_count,
          max = max_count,
          title = list(text = "Number of working occupiers"), 
          labels = list(
            formatter = JS("function () {
          return Math.abs(this.value).toLocaleString();
        }")
          )
        ) %>%
        hc_plotOptions(bar = list(stacking = "normal")) %>%
        hc_add_series(name = "Female", data = as.list(female_data), color = "#002d54", tooltip = list(pointFormatter = JS("function() { return 'Female: ' + Math.abs(this.y).toLocaleString() + ' occupiers'; }"))) %>%
        hc_add_series(name = "Male", data = as.list(male_data), color = "#2b9c93", tooltip = list(pointFormatter = JS("function() { return 'Male: ' + this.y.toLocaleString() + ' occupiers'; }"))) %>%
        hc_tooltip(shared = FALSE) %>%
        hc_legend(align = "center") %>%
        hc_xAxis(title = list(text = "Age group")) %>%
        hc_yAxis(title = list(text = "Number of working occupiers"),
                 labels = list(formatter = JS("function () {
               return Math.abs(this.value).toLocaleString();
             }")))
    })
    
    # Timeseries Chart - Using the modular line chart
    filtered_timeseries_data <- reactive({
      req(input$variables)
      data <- occupiers_timeseries_data()
      data %>% filter(`Occupiers and employees by category` %in% input$variables)
    })
    
    lineChartServer(
      id = "line_chart",
      chart_data = filtered_timeseries_data,
      title = "Agricultural occupiers over time",
      yAxisTitle = "Occupiers (1,000)",
      xAxisTitle = "Year",
      footer = census_footer,
      x_col = "Year",
      y_col = "Value"
    )
    
    # Map - Use the new map module
    mapServer(
      id = "map",
      data = regions_data,
      variable = reactive(input$variable),
      footer = census_footer,
      title = paste("Occupiers by region in Scotland in", census_year),
      legend_title = "Number of occupiers"   
      )
    
    # Pivoting Chart Data Wider for Data Table View
    pivoted_chart_data <- reactive({
      chart_data() %>%
        pivot_wider(names_from = Age, values_from = Count)%>%
        mutate(across(where(is.numeric) & !contains("Year"), comma))
    })
    
    # Pivoting Map Data Wider for Data Table View
    pivoted_regions_data <- reactive({
      regions_data() %>%
        pivot_wider(names_from = sub_region, values_from = value)%>%
        mutate(across(where(is.numeric) & !contains("Year"), comma))
    })
    
    # Pivoting Timeseries Data Wider for Data Table View
    pivoted_timeseries_data <- reactive({
      occupiers_timeseries_data() %>%
        pivot_wider(names_from = Year, values_from = Value)%>%
        mutate(across(where(is.numeric) & !contains("Year"), comma))
    })
    
    # Data Table - Output
    output$data_table <- renderDT({
      req(input$data_source)
      if (input$data_source == "Population Pyramid Data") {
        datatable(pivoted_chart_data(), options = list(
          scrollX = TRUE,
          pageLength = 26  # Adjust this to show all entries
        ))
      } else if (input$data_source == "Map Data") {
        datatable(pivoted_regions_data(), options = list(
          scrollX = TRUE
        ))
      } else if (input$data_source == "Timeseries Data") {
        datatable(pivoted_timeseries_data(), options = list(
          scrollX = TRUE
        ))
      }
    })
    
    # Data Table - Download Handler
    output$downloadData <- downloadHandler(
      filename = function() {
        paste(input$data_source, Sys.Date(), ".csv", sep = "")
      },
      content = function(file) {
        if (input$data_source == "Population Pyramid Data") {
          write.csv(pivoted_chart_data(), file, row.names = FALSE)
        } else if (input$data_source == "Map Data") {
          write.csv(pivoted_regions_data(), file, row.names = FALSE)
        } else {
          write.csv(pivoted_timeseries_data(), file, row.names = FALSE)
        }
      }
    )
  })
}

# Testing module
content_demo <- function() {
  ui <- fluidPage(occupiersUI("occupiers_map_test"))
  server <- function(input, output, session) {
    occupiersServer("occupiers_map_test")
  }
  shinyApp(ui, server)
}

content_demo()

## File: module_oilseed.R 
 # File: module_oilseed.R

oilseedUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = unique(oilseed_subregion$`Land use by category`)
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series' || input.tabsetPanel === 'Area Chart'",
        ns = ns,
        checkboxGroupInput(
          ns("timeseries_variables"),
          "Select Time Series Variables",
          choices = unique(oilseed_data$`Crop/Land use`),
          selected = c(
            "Winter oilseed rape",
            "Spring oilseed rape",
            "Linseed"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Time Series Data" = "timeseries"),
          selected = "map"
        )
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Time Series", lineChartUI(ns("line"))),
        tabPanel("Area Chart", areaChartUI(ns("area"))),
        tabPanel("Data Table", 
                 DTOutput(ns("table")),
                 downloadButton(ns("downloadData"), "Download Data"),
                 generateCensusTableFooter()

        )
      )
    )
  )
}

oilseedServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    oilseed_map <- oilseed_subregion %>%
      select(-Scotland) %>%
      mutate(across(everything(), as.character)) %>%
      pivot_longer(cols = -`Land use by category`, names_to = "sub_region", values_to = "value") %>%
      mutate(value = safe_as_numeric(value))
    
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        oilseed_map %>% filter(`Land use by category` == input$variable)
      }),
      unit = "hectares",
      footer = census_footer,
      variable = reactive(input$variable),
      title = paste("Oilseed distribution by region in Scotland in", census_year),
      legend_title = "Area (hectares)"
    )
    
    chart_data <- reactive({
      req(input$timeseries_variables)
      filtered_data <- oilseed_data %>%
        filter(`Crop/Land use` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
        mutate(year = as.numeric(year))  # Ensure year is numeric
      filtered_data
    })
    
    areaChartServer(
      id = "area",
      chart_data = chart_data,
      title = "Area used to grow oilseed in Scotland over time",
      yAxisTitle = "Area of oilseed (1,000)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = "Oilseed Area Planted",
      yAxisTitle = "Area of oilseed (1,000)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    output$table <- renderDT({
      req(input$tabsetPanel == "Data Table")
      if (input$table_data == "map") {
        req(input$variable)
        oilseed_map %>%
          filter(`Land use by category` == input$variable) %>%
          pivot_wider(names_from = sub_region, values_from = value) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      } else {
        oilseed_data %>%
          pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
          pivot_wider(names_from = year, values_from = value) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      }
    })
    
    output$downloadData <- downloadHandler(
      filename = function() {
        if (input$table_data == "map") {
          paste("Oilseed_Map_Data_", Sys.Date(), ".csv", sep = "")
        } else {
          paste("Oilseed_Timeseries_Data_", Sys.Date(), ".csv", sep = "")
        }
      },
      content = function(file) {
        data <- if (input$table_data == "map") {
          oilseed_map %>%
            filter(`Land use by category` == input$variable) %>%
            pivot_wider(names_from = sub_region, values_from = value) %>%
            mutate(across(where(is.numeric) & !contains("Year"), comma))
        } else {
          oilseed_data %>%
            pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
            pivot_wider(names_from = year, values_from = value) %>%
            mutate(across(where(is.numeric) & !contains("Year"), comma))
        }
        write.csv(data, file, row.names = FALSE)
      }
    )
  })
}

# Testing module
oilseed_demo <- function() {
  ui <- fluidPage(oilseedUI("oilseed_test"))
  server <- function(input, output, session) {
    oilseedServer("oilseed_test")
  }
  shinyApp(ui, server)
}

oilseed_demo()

## File: module_other_animals.R 
 # File: module_other_animals.R

otherAnimalsUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = c(
            "Goats and kids" = "Goats and kids",
            "Deer" = "Deer",
            "Horses" = "Horses",
            "Donkeys" = "Donkeys",
            "Camelids" = "Camelids",
            "Beehives" = "Beehives"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series'",
        ns = ns,
        checkboxGroupInput(
          ns("timeseries_variables"),
          "Select Time Series Variables",
          choices = c(
            "Goats",
            "Deer",
            "Horses",
            "Donkeys",
            "Camelids",
            "Beehives"
          ),
          selected = c(
            "Goats",
            "Deer",
            "Horses",
            "Donkeys",
            "Camelids",
            "Beehives"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Chart Data" = "timeseries"),
          selected = "map"
        )
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Time Series", lineChartUI(ns("line"))),
        tabPanel("Data Table", 
                 DTOutput(ns("table")),
                 downloadButton(ns("downloadData"), "Download Data"),
                 generateCensusTableFooter()

                 
        )
      )
    )
  )
}

otherAnimalsServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    other_animals_data <- livestock_subregion %>%
      filter(`Livestock by category` %in% c(
        "Goats and kids",
        "Deer",
        "Horses",
        "Donkeys",
        "Camelids",
        "Beehives"
      )) %>%
      select(-Scotland) %>%
      mutate(across(everything(), as.character)) %>%
      pivot_longer(cols = -`Livestock by category`, names_to = "sub_region", values_to = "value") %>%
      mutate(value = safe_as_numeric(value))
    
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        other_animals_data %>% filter(`Livestock by category` == input$variable)
      }),
      footer = census_footer,
      variable = reactive(input$variable),
      title = paste("Other animals distribution by region in Scotland in", census_year),
      legend_title = "Number of animals"
    )
    
    chart_data <- reactive({
      req(input$timeseries_variables)
      filtered_data <- number_of_other_livestock %>%
        mutate(across(-`Livestock by category`, as.numeric)) %>%
        filter(`Livestock by category` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Livestock by category`, names_to = "year", values_to = "value") %>%
        mutate(year = as.numeric(year))  # Ensure year is numeric
      filtered_data
    })
    
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = "Number of other animals over time",
      yAxisTitle = "Number of animals (1,000)",
      xAxisTitle = "Year",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    output$table <- renderDT({
      req(input$tabsetPanel == "Data Table")
      if (input$table_data == "map") {
        req(input$variable)
        other_animals_data %>%
          filter(`Livestock by category` == input$variable) %>%
          pivot_wider(names_from = sub_region, values_from = value)  %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      } else {
        number_of_other_livestock %>%
          pivot_longer(cols = -`Livestock by category`, names_to = "year", values_to = "value") %>%
          pivot_wider(names_from = year, values_from = value)  %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      }
    })
    
    output$downloadData <- downloadHandler(
      filename = function() {
        if (input$table_data == "map") {
          paste("Other_Animals_Map_Data_", Sys.Date(), ".csv", sep = "")
        } else {
          paste("Other_Animals_Timeseries_Data_", Sys.Date(), ".csv", sep = "")
        }
      },
      content = function(file) {
        data <- if (input$table_data == "map") {
          other_animals_data %>%
            filter(`Livestock by category` == input$variable) %>%
            pivot_wider(names_from = sub_region, values_from = value)
        } else {
          number_of_other_livestock %>%
            pivot_longer(cols = -`Livestock by category`, names_to = "year", values_to = "value") %>%
            pivot_wider(names_from = year, values_from = value)
        }
        write.csv(data, file, row.names = FALSE)
      }
    )
  })
}

# Testing module
other_animals_demo <- function() {
  ui <- fluidPage(otherAnimalsUI("other_animals_test"))
  server <- function(input, output, session) {
    otherAnimalsServer("other_animals_test")
  }
  shinyApp(ui, server)
}

other_animals_demo()

## File: module_owned_land.R 
 

ownedLandUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      checkboxGroupInput(
        ns("variables"), 
        "Choose variables to add to chart", 
        choices = unique(owned_rented_land$`Area owned or rented`[-4]), 
        selected = unique(owned_rented_land$`Area owned or rented`[-4])
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabs"),
        tabPanel("Timeseries", 
                 lineChartUI(ns("line_chart")), 
                 value = "timeseries"),
        tabPanel("Data Table", 
                 DTOutput(ns("data_table")), 
                 downloadButton(ns("downloadData"), "Download Data"),   
                 generateCensusTableFooter(),
                 value = "data_table")
      )
    )
  )
}

ownedLandServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    # Data Processing for Timeseries
    owned_rented_land_filtered <- owned_rented_land %>% 
      filter(`Area owned or rented` != "Percentage of area rented")
    
    owned_rented_land_long <- owned_rented_land_filtered %>%
      pivot_longer(cols = -`Area owned or rented`, names_to = "Year", values_to = "Value") %>%
      mutate(Year = as.numeric(Year))
    
    filtered_owned_rented_land <- reactive({
      req(input$variables)
      data <- owned_rented_land_long %>%
        filter(`Area owned or rented` %in% input$variables)
      data
    })
    
    # Pivot Wider for Data Table
    wide_data <- reactive({
      filtered_owned_rented_land() %>%
        pivot_wider(names_from = `Area owned or rented`, values_from = Value)
    })
    
    # Line Chart
    lineChartServer(
      id = "line_chart",
      chart_data = filtered_owned_rented_land,
      title = "Area of holdings owned and rented across time",
      yAxisTitle = "Area (1,000 hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "Year",
      y_col = "Value"
    )
    
    # Data Table with Scrollable X-Axis
    output$data_table <- renderDT({
      datatable(
        wide_data()  %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)),
        options = list(
          scrollX = TRUE,
          pageLength = 20
        )
      )
    })
    
    # Download Handler
    output$downloadData <- downloadHandler(
      filename = function() {
        paste("owned_rented_land", Sys.Date(), ".csv", sep = "")
      },
      content = function(file) {
        write.csv(wide_data(), file, row.names = FALSE)
      }
    )
  })
}

# Testing module
content_demo <- function() {
  ui <- fluidPage(ownedLandUI("owned_land_test"))
  server <- function(input, output, session) {
    ownedLandServer("owned_land_test")
  }
  shinyApp(ui, server)
}

content_demo()

## File: module_percentage_bar_chart.R 
 

## File: module_pigs.R 
 # File: module_pigs.R

pigsUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = c(
            "Total pigs" = "Total pigs",
            "Female pigs breeding herd" = "Female pigs breeding herd",
            "All other non-breeding pigs" = "All other non-breeding pigs"
          ))
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series' || input.tabsetPanel === 'Area Chart'",
        ns = ns,
        selectizeInput(
          ns("timeseries_variables"),
          "Click within the box to select variables (not all shown)",
          choices = unique(number_of_pigs$`Pigs by category`),
          selected = c(
            "Total breeding herd",
            "80 kg liveweight and over",
            "50 kg and under 80 kg liveweight",
            "Under 50 kg liveweight"
          ),
          multiple = TRUE,
          options = list(
            plugins = list('remove_button')
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Time Series Data" = "timeseries"),
          selected = "map"
        )
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Time Series", lineChartUI(ns("line"), note_type = 2)),
        tabPanel("Area Chart", areaChartUI(ns("area"), note_type = 2)),
        tabPanel("Data Table", 
                 DTOutput(ns("table")),
                 downloadButton(ns("downloadData"), "Download Data"),
                 generateCensusTableFooter()

        )
      )
    )
  )
}

pigsServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    pigs_data <- livestock_subregion %>%
      filter(`Livestock by category` %in% c(
        "Female pigs breeding herd",
        "All other non-breeding pigs",
        "Total pigs"
      )) %>%
      select(-Scotland) %>%
      mutate(across(everything(), as.character)) %>%
      pivot_longer(cols = -`Livestock by category`, names_to = "sub_region", values_to = "value") %>%
      mutate(value = as.numeric(value))  # Ensure value is numeric
    
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        pigs_data %>% filter(`Livestock by category` == input$variable)
      }),
      footer = census_footer,
      variable = reactive(input$variable),
      title = paste("Pig distribution by region in Scotland in", census_year),
      legend_title = "Number of pigs"
    )
    
    chart_data <- reactive({
      req(input$timeseries_variables)
      filtered_data <- number_of_pigs %>%
        mutate(across(-`Pigs by category`, as.numeric)) %>%
        filter(`Pigs by category` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Pigs by category`, names_to = "year", values_to = "value") %>%
        mutate(value = as.numeric(value))  # Ensure value is numeric
      filtered_data
    })
    
    areaChartServer(
      id = "area",
      chart_data = chart_data,
      title = "Number of pigs by category over time",
      yAxisTitle = "Number of pigs (1,000)",
      xAxisTitle = "Year",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = "Number of pigs by category over time",
      yAxisTitle = "Number of pigs (1,000)",
      xAxisTitle = "Year",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    output$table <- renderDT({
      req(input$tabsetPanel == "Data Table")
      if (input$table_data == "map") {
        req(input$variable)
        pigs_data %>%
          filter(`Livestock by category` == input$variable) %>%
          pivot_wider(names_from = sub_region, values_from = value)  %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      } else {
        number_of_pigs %>%
          pivot_longer(cols = -`Pigs by category`, names_to = "year", values_to = "value") %>%
          pivot_wider(names_from = year, values_from = value)  %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma))%>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      }
    })
    
    output$downloadData <- downloadHandler(
      filename = function() {
        if (input$table_data == "map") {
          paste("Pigs_Map_Data_", Sys.Date(), ".csv", sep = "")
        } else {
          paste("Pigs_Timeseries_Data_", Sys.Date(), ".csv", sep = "")
        }
      },
      content = function(file) {
        data <- if (input$table_data == "map") {
          pigs_data %>%
            filter(`Livestock by category` == input$variable) %>%
            pivot_wider(names_from = sub_region, values_from = value)
        } else {
          number_of_pigs %>%
            pivot_longer(cols = -`Pigs by category`, names_to = "year", values_to = "value") %>%
            pivot_wider(names_from = year, values_from = value)
        }
        write.csv(data, file, row.names = FALSE)
      }
    )
  })
}

# Testing module
# pigs_demo <- function() {
#   ui <- fluidPage(pigsUI("pigs_test"))
#   server <- function(input, output, session) {
#     pigsServer("pigs_test")
#   }
#   shinyApp(ui, server)
# }
# 
# pigs_demo()

## File: module_potatoes.R 
 # File: module_potatoes.R

potatoesUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = unique(potatoes_subregion$`Land use by category`)
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series' || input.tabsetPanel === 'Area Chart'",
        ns = ns,
        checkboxGroupInput(
          ns("timeseries_variables"),
          "Select Time Series Variables",
          choices = unique(potatoes_data$`Crop/Land use`),
          selected = c(
            "Ware potatoes",
            "Seed potatoes"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Time Series Data" = "timeseries"),
          selected = "map"
        )
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Time Series", lineChartUI(ns("line"))),
        tabPanel("Area Chart", areaChartUI(ns("area"))),
        tabPanel("Data Table", 
                 DTOutput(ns("table")),
                 downloadButton(ns("download_data"), "Download Data"),
                  generateCensusTableFooter()
        )
      )
    )
  )
}

potatoesServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    potatoes_map <- potatoes_subregion %>%
      select(-Scotland) %>%
      mutate(across(everything(), as.character)) %>%
      pivot_longer(cols = -`Land use by category`, names_to = "sub_region", values_to = "value") %>%
      mutate(value = safe_as_numeric(value))
    
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        potatoes_map %>% filter(`Land use by category` == input$variable)
      }),
      unit = "hectares",
      footer = census_footer,
      variable = reactive(input$variable),
      title = paste("Potatoes distribution by region in Scotland in", census_year),
      legend_title = "Area (hectares)"
    )
    
    chart_data <- reactive({
      req(input$timeseries_variables)
      filtered_data <- potatoes_data %>%
        filter(`Crop/Land use` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
        mutate(year = as.numeric(year))  # Ensure year is numeric
      filtered_data
    })
    
    areaChartServer(
      id = "area",
      chart_data = chart_data,
      title = "Area used to grow potatoes over time",
      yAxisTitle = "Area of potatoes (1,000 hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = "Area used to grow potatoes over time",
      yAxisTitle = "Area of potatoes (1,000 hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    output$table <- renderDT({
      req(input$tabsetPanel == "Data Table")
      if (input$table_data == "map") {
        req(input$variable)
        potatoes_map %>%
          filter(`Land use by category` == input$variable) %>%
          pivot_wider(names_from = sub_region, values_from = value)  %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      } else {
        potatoes_data %>%
          pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
          pivot_wider(names_from = year, values_from = value)  %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma))%>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      }
    })
    
    output$download_data <- downloadHandler(
      filename = function() {
        if (input$table_data == "map") {
          paste("Potato_Subregion_Data_", Sys.Date(), ".csv", sep = "")
        } else {
          paste("Potato_Timeseries_Data_", Sys.Date(), ".csv", sep = "")
        }
      },
      content = function(file) {
        data <- if (input$table_data == "map") {
          potatoes_map %>%
            filter(`Land use by category` == input$variable) %>%
            pivot_wider(names_from = sub_region, values_from = value)
        } else {
          potatoes_data %>%
            pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
            pivot_wider(names_from = year, values_from = value)
        }
        write.csv(data, file, row.names = FALSE)
      }
    )
  })
}

potatoes_demo <- function() {
  ui <- fluidPage(potatoesUI("potatoes_test"))
  server <- function(input, output, session) {
    potatoesServer("potatoes_test")
  }
  shinyApp(ui, server)
}

potatoes_demo()

## File: module_poultry.R 
 # File: module_poultry.R
poultryUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = c(           
            "Total poultry" = "Total poultry",
            "Fowls for producing eggs" = "Fowls for producing eggs",
            "Fowls for breeding" = "Fowls for breeding",
            "Broilers and other table fowls and other poultry" = "Broilers and other table fowls and other poultry"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series' || input.tabsetPanel === 'Area Chart'",
        ns = ns,
        selectizeInput(
          ns("timeseries_variables"),
          "Click within the box to select variables",
          choices = unique(number_of_poultry$`Poultry by category`),
          selected = c(
            "Total fowls for producing eggs",
            "Total fowls for breeding",
            "Broilers and other table birds",
            "Total Poultry"
          ),
          multiple = TRUE,
          options = list(
            plugins = list('remove_button')
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Time Series Data" = "timeseries"),
          selected = "map"
        )
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Time Series", lineChartUI(ns("line"), note_type = 2)),
        tabPanel("Area Chart", areaChartUI(ns("area"), note_type = 2)),
        tabPanel("Data Table", 
                 DTOutput(ns("table")),
                 downloadButton(ns("downloadData"), "Download Data"),
                 generateCensusTableFooter()
        )
      ),
      div(
        style = "margin-top: 20px; padding: 10px; border-top: 1px solid #ddd;",
        HTML("<strong>Note:</strong> Poultry estimates for 2023 are not comparable to previous years due to methodological improvements.")
      )
    )
  )
}


poultryServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    poultry_data <- livestock_subregion %>%
      filter(`Livestock by category` %in% c(
        "Fowls for producing eggs",
        "Fowls for breeding",
        "Broilers and other table fowls and other poultry",
        "Total poultry"
      )) %>%
      select(-Scotland) %>%
      mutate(across(everything(), as.character)) %>%
      pivot_longer(cols = -`Livestock by category`, names_to = "sub_region", values_to = "value") %>%
      mutate(value = safe_as_numeric(value))
    
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        poultry_data %>% filter(`Livestock by category` == input$variable)
      }),
      footer = census_footer,
      variable = reactive(input$variable),
      title = paste("Poultry distribution by region in Scotland in", census_year),
      legend_title = "Number of poultry"
    )
    
    chart_data <- reactive({
      req(input$timeseries_variables)
      filtered_data <- number_of_poultry %>%
        filter(`Poultry by category` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Poultry by category`, names_to = "year", values_to = "value") %>%
        mutate(value = safe_as_numeric(value))
    })
    
    areaChartServer(
      id = "area",
      chart_data = chart_data,
      title = "Number of poultry by category across time",
      yAxisTitle = "Number of poultry (1,000)",
      xAxisTitle = "Year",
      footer = '<div style="font-size: 16px; font-weight: bold;">* Estimates for 2023 are not comparable to previous years due to methodological improvements.<br/><a href="https://www.gov.scot/publications/results-scottish-agricultural-census-june-2023/documents/">Source: Scottish Agricultural Census: June 2023</a></div>',
      x_col = "year",
      y_col = "value"
    )
    
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = "Number of poultry by category across time",
      yAxisTitle = "Number of poultry (1,000)",
      xAxisTitle = "Year",
      footer = '<div style="font-size: 16px; font-weight: bold;">* Estimates for 2023 are not comparable to previous years due to methodological improvements.<br/><a href="https://www.gov.scot/publications/results-scottish-agricultural-census-june-2023/documents/">Source: Scottish Agricultural Census: June 2023</a></div>',
      x_col = "year",
      y_col = "value"
    )
    
    output$table <- renderDT({
      req(input$tabsetPanel == "Data Table")
      if (input$table_data == "map") {
        req(input$variable)
        poultry_data %>%
          filter(`Livestock by category` == input$variable) %>%
          pivot_wider(names_from = sub_region, values_from = value)  %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      } else {
        number_of_poultry %>%
          pivot_longer(cols = -`Poultry by category`, names_to = "year", values_to = "value") %>%
          pivot_wider(names_from = year, values_from = value)  %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      }
    })
    
    output$downloadData <- downloadHandler(
      filename = function() {
        if (input$table_data == "map") {
          paste("Poultry_Map_Data_", Sys.Date(), ".csv", sep = "")
        } else {
          paste("Poultry_Timeseries_Data_", Sys.Date(), ".csv", sep = "")
        }
      },
      content = function(file) {
        data <- if (input$table_data == "map") {
          poultry_data %>%
            filter(`Livestock by category` == input$variable) %>%
            pivot_wider(names_from = sub_region, values_from = value)
        } else {
          number_of_poultry %>%
            pivot_longer(cols = -`Poultry by category`, names_to = "year", values_to = "value") %>%
            pivot_wider(names_from = year, values_from = value)
        }
        write.csv(data, file, row.names = FALSE)
      }
    )
  })
}

# Testing module
poultry_demo <- function() {
  ui <- fluidPage(poultryUI("poultry_test"))
  server <- function(input, output, session) {
    poultryServer("poultry_test")
  }
  shinyApp(ui, server)
}

poultry_demo()

## File: module_regions_map.R 
 

## File: module_sheep.R 
 # File: module_sheep.R


sheepUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = c(
            "Total sheep" = "Total sheep",
            "Ewes for breeding" = "Ewes for breeding",
            "Other sheep one year old and over for breeding" = "Other sheep one year old and over for breeding",
            "Rams to be used for service" = "Rams for service",
            "Lambs" = "Lambs"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series' || input.tabsetPanel === 'Area Chart'",
        ns = ns,
        checkboxGroupInput(
          ns("timeseries_variables"),
          "Select Time Series Variables",
          choices = c(
            "Ewes used for breeding in previous season",
            "Sheep for breeding aged 1 year and over",
            "Rams to be used for service",
            "Total other sheep 1 year and over",
            "Lambs",
            "Total sheep",
            "Other"
            
          ),
          selected = c(
            "Ewes used for breeding in previous season",
            "Rams to be used for service",
            "Sheep for breeding aged 1 year and over",
            "Total other sheep 1 year and over",
            "Lambs"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Time Series Data" = "timeseries"),
          selected = "map"
        )
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Time Series", lineChartUI(ns("line"))),
        tabPanel("Area Chart", areaChartUI(ns("area"))),
        tabPanel("Data Table", 
                 DTOutput(ns("table")),
                 downloadButton(ns("downloadData"), "Download Data"),
                 generateCensusTableFooter()

        )
      )
    )
  )
}

sheepServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    sheep_data <- livestock_subregion %>%
      filter(`Livestock by category` %in% c(
        "Ewes for breeding",
        "Other sheep one year old and over for breeding",
        "Rams for service",
        "Lambs",
        "Total sheep"
      )) %>%
      select(-Scotland) %>%
      mutate(across(everything(), as.character)) %>%
      pivot_longer(cols = -`Livestock by category`, names_to = "sub_region", values_to = "value") %>%
      mutate(value = safe_as_numeric(value))
    
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        sheep_data %>% filter(`Livestock by category` == input$variable)
      }),
      footer = census_footer,
      variable = reactive(input$variable),
      title = paste("Sheep distribution by region in Scotland in", census_year),
      legend_title = "Number of sheep"
    )
    
    chart_data <- reactive({
      req(input$timeseries_variables)
      filtered_data <- number_of_sheep %>%
        filter(`Sheep by category` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Sheep by category`, names_to = "year", values_to = "value") %>%
        mutate(year = as.numeric(year))  # Ensure year is numeric
      filtered_data
    })
    
    areaChartServer(
      id = "area",
      chart_data = chart_data,
      title = "Number of sheep by category across time",
      yAxisTitle = "Number of sheep (1,000)",
      xAxisTitle = "Year",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = "Number of sheep by category across time",
      yAxisTitle = "Number of sheep (1,000)",
      xAxisTitle = "Year",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    output$table <- renderDT({
      req(input$tabsetPanel == "Data Table")
      if (input$table_data == "map") {
        req(input$variable)
        sheep_data %>%
          filter(`Livestock by category` == input$variable) %>%
          pivot_wider(names_from = sub_region, values_from = value) %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>% 
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      } else {
        number_of_sheep %>%
          pivot_longer(cols = -`Sheep by category`, names_to = "year", values_to = "value") %>%
          pivot_wider(names_from = year, values_from = value) %>%
          mutate(across(where(is.numeric) & !contains("Year"), comma)) %>% 
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      }
    })
    
    output$downloadData <- downloadHandler(
      filename = function() {
        if (input$table_data == "map") {
          paste("Sheep_Map_Data_", Sys.Date(), ".csv", sep = "")
        } else {
          paste("Sheep_Timeseries_Data_", Sys.Date(), ".csv", sep = "")
        }
      },
      content = function(file) {
        data <- if (input$table_data == "map") {
          sheep_data %>%
            filter(`Livestock by category` == input$variable) %>%
            pivot_wider(names_from = sub_region, values_from = value) %>%
            mutate(across(where(is.numeric) & !contains("Year"), comma))
        } else {
          number_of_sheep %>%
            pivot_longer(cols = -`Sheep by category`, names_to = "year", values_to = "value") %>%
            pivot_wider(names_from = year, values_from = value) %>%
            mutate(across(where(is.numeric) & !contains("Year"), comma))
        }
        write.csv(data, file, row.names = FALSE)
      }
    )
  })
}

sheep_demo <- function() {
  ui <- fluidPage(sheepUI("sheep_test"))
  server <- function(input, output, session) {
    sheepServer("sheep_test")
  }
  shinyApp(ui, server)
}

sheep_demo()

## File: module_soil_testing.R 
 soilTestingUI <- function(id) {
  ns <- NS(id)
  tagList(
    tabsetPanel(
      tabPanel("Soil Testing",
               fluidRow(
                 column(12,
                        div(style = "font-size: 20px; font-weight: bold;", "Soil testing is more likely to be carried out on cropping land than grassland"),
                        percentageBarChartUI(ns("soilTestingOverview"), chart_height = 420)
                 )
               ),
               fluidRow(
                 column(12,
                        div(style = "font-size: 20px; font-weight: bold;", "Many holdings made changes to nutrient application based on soil testing"),
                        percentageBarChartUI(ns("soilTestingEffectiveness"), chart_height = 240)
                 )
               ),
               fluidRow(
                 column(12,
                        div(style = "font-size: 20px; font-weight: bold;", "pH testing is more likely to be carried out on cropping land than grassland"),
                        percentageBarChartUI(ns("phTestingEffectiveness"), chart_height = 340)
                 )
               )
      ),
      tabPanel("Nutrient Management Plans",
               fluidRow(
                 column(12,
                        div(style = "font-size: 20px; font-weight: bold;", "Nutrient management plans are more likely to be conducted on cropping land than grassland"),
                        percentageBarChartUI(ns("nutrientManagementOverview"), chart_height = 480)
                 )
               ),
               fluidRow(
                 column(12,
                        div(style = "font-size: 20px; font-weight: bold;", "Few holdings regularly update a nutrient management plan"),
                        percentageBarChartUI(ns("nutrientManagementEffectiveness"), chart_height = 240)
                 )
               )
      ),
      tabPanel("Data Table",
               fluidRow(
                 column(3,
                        radioButtons(
                          ns("table_type"),
                          "Select Chart Type",
                          choices = c("Soil Testing", "Nutrient Management"),
                          selected = "Soil Testing"
                        )
                 ),
                 column(9,
                        DTOutput(ns("table")),
                        tags$div(
                          style = "margin-top: 20px;",
                          downloadButton(ns("download_data"), "Download Data")
                        )
                 )
               )
      )
    ),
    generate2023ModuleTableFooter()  # Place the footer outside the tabsetPanel, but still inside the mainPanel
  )
}



# Server for Soil Testing and Nutrient Management Module
soilTestingServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    # Data filtering and preparation for Soil Testing
    overview_data <- reactive({
      combined_nutrient_mgmt %>% 
        filter(`Soil nutrient management` %in% c(
          "Soil testing on grassland in last five years",
          "Soil testing on cropland in last five years",
          "Soil testing on either grass or crops in last five years"
        )) 
    })
    
    effectiveness_data <- reactive({
      combined_nutrient_mgmt %>%
        filter(`Soil nutrient management` %in% c(
          "Soil testing resulted in change of crop nutrient application"
        ))
    })
    
    ph_testing_data <- reactive({
      combined_nutrient_mgmt %>%
        filter(`Soil nutrient management` %in% c(
          "Regular pH testing on grassland",
          "Regular pH testing on cropland"
        ))
    })
    
    # Data filtering and preparation for Nutrient Management
    nutrient_management_overview_data <- reactive({
      combined_nutrient_mgmt %>%
        filter(`Soil nutrient management` %in% c(
          "Nutrient management plan used on grassland",
          "Nutrient management plan used on cropland"
        ))
    })
    
    nutrient_management_effectiveness_data <- reactive({
      combined_nutrient_mgmt %>%
        filter(`Soil nutrient management` %in% c(
          "Wrote or updated nutrient management plan in last year"
        ))
    })
    
    precision_farming_data <- reactive({
      combined_nutrient_mgmt %>%
        filter(`Soil nutrient management` %in% c(
          "Uses precision farming technology to vary nitrogen application rate"
        ))
    })
    
    # Bar charts for Soil Testing
    percentageBarChartServer(
      id = "soilTestingOverview",
      chart_data = overview_data,
      title = NULL,  # Titles handled by the fluid rows
      yAxisTitle = "Percentage of Holdings",
      xAxisTitle = "",
      x_col = "Soil nutrient management",
      y_col = "Percentage of holdings",
      unit = "%"
    )
    
    percentageBarChartServer(
      id = "soilTestingEffectiveness",
      chart_data = effectiveness_data,
      title = NULL,
      yAxisTitle = "Percentage of Holdings",
      xAxisTitle = "",
      x_col = "Soil nutrient management",
      y_col = "Percentage of holdings",
      unit = "%"
    )
    
    percentageBarChartServer(
      id = "phTestingEffectiveness",
      chart_data = ph_testing_data,
      title = NULL,
      yAxisTitle = "Percentage of Holdings",
      xAxisTitle = "",
      x_col = "Soil nutrient management",
      y_col = "Percentage of holdings",
      unit = "%"
    )
    
    # Bar charts for Nutrient Management
    percentageBarChartServer(
      id = "nutrientManagementOverview",
      chart_data = nutrient_management_overview_data,
      title = NULL,  # Titles handled by the fluid rows
      yAxisTitle = "Percentage of Holdings",
      xAxisTitle = "",
      x_col = "Soil nutrient management",
      y_col = "Percentage of holdings",
      unit = "%"
    )
    
    percentageBarChartServer(
      id = "nutrientManagementEffectiveness",
      chart_data = nutrient_management_effectiveness_data,
      title = NULL,
      yAxisTitle = "Percentage of Holdings",
      xAxisTitle = "",
      x_col = "Soil nutrient management",
      y_col = "Percentage of holdings",
      unit = "%"
    )
    
    # Reactive data based on selected chart type
    table_data <- reactive({
      if (input$table_type == "Soil Testing") {
        combined_nutrient_mgmt %>% 
          filter(`Soil nutrient management` %in% c(
            "Soil testing on grassland in last five years",
            "Soil testing on cropland in last five years",
            "Soil testing on either grass or crops in last five years"
           
          )) %>%
              rename(`Soil testing` = `Soil nutrient management` )
      } else {
        combined_nutrient_mgmt %>%
          filter(`Soil nutrient management` %in% c(
            "Nutrient management plan used on grassland",
            "Nutrient management plan used on cropland"
          ))
      }
    })
    
    # Render data table
    output$table <- renderDT({
      datatable(table_data(), options = list(scrollX = TRUE))
    })
    
    # Download handler for data
    output$download_data <- downloadHandler(
      filename = function() {
        paste("Soil_testing_data", Sys.Date(), ".csv", sep = "")
      },
      content = function(file) {
        write.csv(table_data(), file, row.names = FALSE)
      }
    )

  })
}

# Testing function for the entire module
soilTestingDemo <- function() {
  ui <- fluidPage(soilTestingUI("soil_testing_demo"))
  server <- function(input, output, session) {
    soilTestingServer("soil_testing_demo")
  }
  shinyApp(ui, server)
}

soilTestingDemo()

## File: module_stockfeeding.R 
 # File: module_stockfeeding.R

stockfeedingUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = unique(stockfeeding_subregion$`Land use by category`)
        )
      ),     
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series' || input.tabsetPanel === 'Area Chart'",
        ns = ns,
        checkboxGroupInput(
          ns("timeseries_variables"),
          "Select Time Series Variables",
          choices = unique(stockfeeding_data$`Crop/Land use`),
          selected = c(
            "Turnips/swedes",
            "Kale/cabbage",
            "Maize",
            "Rape",
            "Fodder beet",
            "Lupins"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Time Series Data" = "timeseries"),
          selected = "map"
        )
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Time Series", lineChartUI(ns("line"))),
        tabPanel("Area Chart", areaChartUI(ns("area"))),
        tabPanel("Data Table", 
                 DTOutput(ns("table")),
                 downloadButton(ns("download_data"), "Download Data"),
                 generateCensusTableFooter()

        )
      )
    )
  )
}

stockfeedingServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    stockfeeding_map <- stockfeeding_subregion %>%
      select(-Scotland) %>%
      mutate(across(everything(), as.character)) %>%
      pivot_longer(cols = -`Land use by category`, names_to = "sub_region", values_to = "value") %>%
      mutate(value = safe_as_numeric(value))
    
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        stockfeeding_map %>% filter(`Land use by category` == input$variable)
      }),
      unit = "hectares",
      footer = census_footer,
      variable = reactive(input$variable),
      title = paste("Stockfeeding crops distribution by region in Scotland in", census_year),
      legend_title = "Area (hectares)"
    )
    
    chart_data <- reactive({
      req(input$timeseries_variables)
      filtered_data <- stockfeeding_data %>%
        filter(`Crop/Land use` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
        mutate(year = as.numeric(year))  # Ensure year is numeric
      filtered_data
    })
    
    areaChartServer(
      id = "area",
      chart_data = chart_data,
      title = "Area used to grow crops for stockfeeding over time",
      yAxisTitle = "Area of crops (1,000 hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = "Area used to grow crops for stockfeeding over time",
      yAxisTitle = "Area of crops (1,000 hectares)",
      xAxisTitle = "Year",
      unit = "hectares",
      footer = census_footer,
      x_col = "year",
      y_col = "value"
    )
    
    output$table <- renderDT({
      req(input$tabsetPanel == "Data Table")
      if (input$table_data == "map") {
        req(input$variable)
        stockfeeding_map %>%
          filter(`Land use by category` == input$variable) %>%
          pivot_wider(names_from = sub_region, values_from = value) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      } else {
        stockfeeding_data %>%
          pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
          pivot_wider(names_from = year, values_from = value) %>%
          datatable(
            options = list(
              scrollX = TRUE,  # Enable horizontal scrolling
              pageLength = 20  # Show 20 entries by default
            )
          )
      }
    })
    
    output$download_data <- downloadHandler(
      filename = function() {
        if (input$table_data == "map") {
          paste("Stockfeeding_Subregion_Data_", Sys.Date(), ".csv", sep = "")
        } else {
          paste("Stockfeeding_Timeseries_Data_", Sys.Date(), ".csv", sep = "")
        }
      },
      content = function(file) {
        data <- if (input$table_data == "map") {
          stockfeeding_map %>%
            filter(`Land use by category` == input$variable) %>%
            pivot_wider(names_from = sub_region, values_from = value) %>%
            mutate(across(where(is.numeric) & !contains("Year"), comma))
        } else {
          stockfeeding_data %>%
            pivot_longer(cols = -`Crop/Land use`, names_to = "year", values_to = "value") %>%
            pivot_wider(names_from = year, values_from = value) %>%
            mutate(across(where(is.numeric) & !contains("Year"), comma))
        }
        write.csv(data, file, row.names = FALSE)
      }
    )
  })
}

stockfeeding_demo <- function() {
  ui <- fluidPage(stockfeedingUI("stockfeeding_test"))
  server <- function(input, output, session) {
    stockfeedingServer("stockfeeding_test")
  }
  shinyApp(ui, server)
}
stockfeeding_demo()

## File: module_subsector_emissions.R 
 subsectorEmissionsUI <- function(id) {
  ns <- NS(id)
  tagList(
    sidebarLayout(
      sidebarPanel(
        width = 3,
        conditionalPanel(
          condition = "input.tabs != 'Summary_Page'",
          ns = ns,
          uiOutput(ns("variable_select")),
        ),
        conditionalPanel(
          condition = "input.tabs == 'Summary_Page'",
          ns = ns,
          div("Adjust the sliders to compare data from different years.", 
              style = "font-size: 14px; font-weight: bold; margin-bottom: 10px;"),
          sliderInput(ns("summary_current_year_subsector"), "Year of interest", min = 1990, max = emissions_year, value = emissions_year, step = 1, sep = ""),
          sliderInput(ns("summary_comparison_year_subsector"), "Comparison year", min = 1990, max = emissions_year, value = emissions_year - 1, step = 1, sep = "")
        )
      ),
      mainPanel(
        id = ns("mainpanel"),
        width = 9,
        tabsetPanel(
          id = ns("tabs"),
          tabPanel("Summary Page",
                   value = "Summary_Page",
                   fluidRow(
                     column(width = 12, div(class = "header-text", "Breakdown of Scottish agricultural emissions:"))
                   ),
                   fluidRow(
                     column(width = 4, chartUI(ns("industryPieChart_subsector")), style = "padding-right: 0; padding-left: 0;"),
                     column(width = 4, chartUI(ns("industryBarChart_subsector")), style = "padding-right: 0; padding-left: 0;"),
                     column(width = 4, chartUI(ns("industryLineChart_total")), style = "padding-right: 0; padding-left: 0;")
                   ),
                   fluidRow(
                     column(width = 12, div(class = "header-text", style = "padding-top: 20px;" ,"Emissions by subsector:"))
                   ),
                   fluidRow(
                     column(width = 4, valueBoxUI(ns("totalIndustry1_subsector")), style = "padding-right: 0; padding-left: 0;"),
                     column(width = 4, valueBoxUI(ns("totalIndustry2_subsector")), style = "padding-right: 0; padding-left: 0;"),
                     column(width = 4, valueBoxUI(ns("totalIndustry3_subsector")), style = "padding-right: 0; padding-left: 0;")
                   ),
                   fluidRow(
                     column(width = 4, valueBoxUI(ns("totalValue_subsector")), style = "padding-right: 0; padding-left: 0;"),
                     column(width = 4, valueBoxUI(ns("totalIndustry4_subsector")), style = "padding-right: 0; padding-left: 0;"),
                     column(width = 4, valueBoxUI(ns("totalIndustry5_subsector")), style = "padding-right: 0; padding-left: 0;")
                   ),
                   fluidRow(
                     column(width = 12, div(class = "header-text", style = "margin-top: 20px;" , "Scottish agricultural emissions by gas type:"))
                   ),
                   fluidRow(
                     column(width = 4, chartUI(ns("industryPieChart_gas"))),
                     column(width = 4, chartUI(ns("industryBarChart_gas")))
                   ),
                   fluidRow(
                     column(width = 12, 
                            div(
                              class = "footer-text", 
                              style = "font-size: 16px; font-weight: bold; text-align: left; margin-top: 5px;",
                              HTML(
                                '<a href="https://www.gov.scot/publications/scottish-agriculture-greenhouse-gas-emissions-and-nitrogen-use-2022-23/" target="_blank">Source: Scottish agriculture greenhouse gas emissions and nitrogen use 2022-23</a>, analysis based on results of the <a href="https://www.gov.scot/publications/scottish-greenhouse-gas-statistics-2022/" target="_blank">Scottish Greenhouse Gas Statistics 2022</a>.'
                              )
                            )
                     )
                   ),
                   fluidRow(
                     column(width = 12, 
                            div(
                              class = "note",
                              style = "margin-top: 20px; padding: 10px; border-top: 1px solid #ddd;",
                              HTML(
                                "<strong>Note:</strong><ul>
                                  <li>You can see data values by hovering your mouse over the content on charts.</li>
                                </ul>"
                              )
                            )
                     )
                   )
          ),
          tabPanel("Timelapse", timelapseBarChartUI(ns("timelapse_bar")), value = "Timelapse"),
          tabPanel("Sources of Agricultural Emissions", breakdownChartUI(ns("breakdown")), value = "Breakdown"),
          tabPanel("Time Series", lineChartUI(ns("line")), value = "Line_Chart"),
          tabPanel("Area Chart", areaChartUI(ns("area")), value = "Area_Chart"),
          tabPanel("Data Table",
                   DTOutput(ns("data_table")),
                   downloadButton(ns("downloadData"), "Download Data"),
                   generateEmissionsTableFooter(),
                   value = "Data_Table")
        )
      )
    )
  )
}




subsectorEmissionsServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    # Ensure full_data_subsector is defined correctly
    full_data_subsector <- reactive({ subsector_total })
        full_data_total <- reactive({ national_total })
    full_data_gas <- reactive({ agri_gas })
    
    # Reactive data for the line chart (Industry Emissions Over Time)
    line_chart_data <- reactive({
      data <- national_total
      #data <- data %>% filter(Industry %in% c("Agriculture", "Total"))
      data
    })
    
    # Reactive data for the gas charts (Pie and Bar charts)
    gas_chart_data <- reactive({
      data <- agri_gas

      data
    })
    
    # Reactive data for the subsector module
    chart_data <- reactive({
      data <- subsector_total
      if (!is.null(input$variables)) {
        data <- data %>%
          filter(Subsector %in% input$variables)
      }
      data
    })
    
    output$variable_select <- renderUI({
      choices <- unique(subsector_total$Subsector)
      selected <- setdiff(choices, "Total")
      checkboxGroupInput(ns("variables"), "Choose variables to add to chart", choices = choices, selected = selected)
    })
    
    observeEvent(input$select_all_button, {
      updateCheckboxGroupInput(session, ns("variables"), selected = setdiff(unique(subsector_total$Subsector), "Total"))
    })
    
    observeEvent(input$deselect_all_button, {
      updateCheckboxGroupInput(session, ns("variables"), selected = character(0))
    })
    
    # Load and render the subsector charts
    areaChartServer(
      id = "area",
      chart_data = chart_data,
      title = "Agricultural emissions by subsector over time",
      yAxisTitle = "Emissions (MtCO₂e)",
      xAxisTitle = "Year",
      unit = "MtCO₂e",
      footer = emissions_footer,
      x_col = "Year",
      y_col = "Value"
    )
    
    timelapseBarChartServer(
      id = "timelapse_bar",
      chart_data = chart_data,
      title = "Agricultural greenhouse gas emissions timelapse",
      yAxisTitle = "Emissions (MtCO₂e)",
      xAxisTitle = "Year",
      unit = "MtCO₂e",
      footer = emissions_footer,
      x_col = "Year",
      y_col = "Value"
    )
    
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = "Agricultural greenhouse gas emissions by subsector in Scotland",
      yAxisTitle = "Emissions (MtCO₂e)",
      xAxisTitle = "Year",
      unit = "MtCO₂e",
      footer = emissions_footer,  
      x_col = "Year",
      y_col = "Value"
    )
    
    breakdownChartServer(
      id = "breakdown",
      chart_data = reactive({ subsector_source }),  # Assuming subsector_source is your data
      title = "Sources of Scottish agricultural emissions by subsector and type, 2022-23",
      yAxisTitle = "Emissions (MtCO₂e)",
      xAxisTitle = "Subsector",
      unit = "MtCO₂e",
      footer = emissions_footer
      )
    
    
    
    render_data_table(
      table_id = "data_table",
      chart_data = chart_data,
      output = output
    )
    
    handle_data_download(
      download_id = "downloadData",
      chart_type = "Subsector",
      chart_data = chart_data,
      input = input,
      output = output,
      year_input = "year"
    )
    
    #  Module for Subsector Emissions
    current_year <- reactive({ input$summary_current_year_subsector })
    comparison_year <- reactive({ input$summary_comparison_year_subsector })
    
    first_col_name <- "Subsector"
    
    valueBoxServer(
      id = "totalIndustry1_subsector",
      data = full_data_subsector,
      category = first_col_name,
      industry = get_industry(1, full_data_subsector, current_year, first_col_name),
      current_year = current_year,
      comparison_year = comparison_year,
      unit = "MtCO₂e"
    )
    valueBoxServer(
      id = "totalIndustry2_subsector",
      data = full_data_subsector,
      category = first_col_name,
      industry = get_industry(2, full_data_subsector, current_year, first_col_name),
      current_year = current_year,
      comparison_year = comparison_year,
      unit = "MtCO₂e"
    )
    valueBoxServer(
      id = "totalIndustry3_subsector",
      data = full_data_subsector,
      category = first_col_name,
      industry = get_industry(3, full_data_subsector, current_year, first_col_name),
      current_year = current_year,
      comparison_year = comparison_year,
      unit = "MtCO₂e"
    )
    valueBoxServer(
      id = "totalIndustry4_subsector",
      data = full_data_subsector,
      category = first_col_name,
      industry = get_industry(4, full_data_subsector, current_year, first_col_name),
      current_year = current_year,
      comparison_year = comparison_year,
      unit = "MtCO₂e"
    )
    valueBoxServer(
      id = "totalIndustry5_subsector",
      data = full_data_subsector,
      category = first_col_name,
      industry = get_industry(5, full_data_subsector, current_year, first_col_name),
      current_year = current_year,
      comparison_year = comparison_year,
      unit = "MtCO₂e"
    )
    valueBoxServer(
      id = "totalValue_subsector",
      data = full_data_subsector,
      category = first_col_name,
      industry = reactive("Total"),
      current_year = current_year,
      comparison_year = comparison_year,
      unit = "MtCO₂e"
    )
    summaryPieChartServer(
      id = "industryPieChart_subsector",
      data = full_data_subsector,
      title = "Agricultural emissions by subsector",
      current_year = current_year,
      category = "Subsector",
      unit = "MtCO₂e"
    )
    
    summaryBarChartServer(
      id = "industryBarChart_subsector",
      data = full_data_subsector,
      title = "Agricultural emissions by subsector",
      current_year = current_year,
      comparison_year = comparison_year,
      category = "Subsector",
      unit = "MtCO₂e"
    )
    
    summaryLineChartServer(
      id = "industryLineChart_total",
      data = line_chart_data,
      title = "Agricultural emissions and total Scottish emissions",
      unit = "MtCO₂e",
      x_col = "Year",
      y_col = "Value"
    )

    
    summaryPieChartServer(
      id = "industryPieChart_gas",
      data = gas_chart_data,
      title = "Breakdown of gas type",
      current_year = current_year,
      category = "Gas",
      unit = "MtCO₂e"
    )
    
    summaryBarChartServer(
      id = "industryBarChart_gas",
      data = gas_chart_data,
      title = "Emissions by gas type",
      current_year = current_year,
      comparison_year = comparison_year,
      category = "Gas",
      unit = "MtCO₂e"
    )
  })
}


subsector_demo <- function() {
  ui <- fluidPage(subsectorEmissionsUI("subsector_test"))
  server <- function(input, output, session) {
    subsectorEmissionsServer("subsector_test")
  }
  shinyApp(ui, server)
}

subsector_demo()

## File: module_summary.R 
 

## File: module_timelapse_bar_chart.R 
 

## File: module_total_emissions.R 
 

## File: options.R 
 

## File: server.R 
 # server.R



server <- function(input, output, session) {
  # Maintain selected tab state based on URL query parameters
  observe({
    query <- parseQueryString(session$clientData$url_search)
    page <- query$page
    if (!is.null(page)) {
      updateTabsetPanel(session, "navbar", selected = page)
    }
  })
  
  observeEvent(input$navbar, {
    query <- parseQueryString(session$clientData$url_search)
    page <- query$page
    if (is.null(page) || input$navbar != page) {
      if (input$navbar != "home") {
        updateQueryString(paste0("?page=", input$navbar), mode = "push")
      }
    }
  })
  
  
  
  subsectorEmissionsServer("subsector")
  fertiliserUsageServer("fertiliser")
  employeesMapServer("employees")
  landUseSummaryServer("land_use")
  legalResponsibilityServer("legal_responsibility")
  farmTypesServer("farm_types")
  occupiersServer("occupiers")
  ownedLandServer("owned_land")
  cattleServer("cattle_module")
  sheepServer("sheep_module")
  pigsServer("pigs_module")
  poultryServer("poultry_module")
  otherAnimalsServer("other_animals_module")
  cerealsServer("cereals_module")
  oilseedServer("oilseed_module")
  potatoesServer("potatoes_module")
  beansServer("beans_module")
  stockfeedingServer("stockfeeding_module")
  humanVegetablesServer("human_vegetables_module")
  fruitServer("fruit_module")
  homeServer("home")
  animalsSummaryServer("animals_summary_module")
  soilTestingServer("soil")
  manureServer("manure")
  nitrogenServer("nitrogen")
  cropsSummaryServer("crops_summary_module")
}

## File: test.R 
 # File: module_map.R

library(shiny)
library(highcharter)
library(geojsonio)
library(dplyr)

# Load the GeoJSON file
geojson_data <- geojson_read("subregions_simplified.geojson", what = "sp")

# Convert GeoJSON to a Highcharts-compatible format
geojson_list <- geojson_list(geojson_data)

mapUI <- function(id) {
  ns <- NS(id)
  tagList(
    mainPanel(
      htmlOutput(ns("title")),
      highchartOutput(ns("map"), height = "75vh"),  # Set the height to be responsive
      htmlOutput(ns("footer")),
      div(
        class = "note",
        style = "margin-top: 20px; padding: 10px; border-top: 1px solid #ddd;",
        HTML(
          "<strong>Note:</strong><ul>
          <li>Where areas are shaded in grey, the data has been suppressed to prevent disclosure of individual holdings.</li>
            <li>To change the data shown, select a variable from the radio buttons within the sidebar.</li>
            <li>You can see data values for each variable by hovering your mouse over the region.</li>
            <li>To change the zoom level, use the + and - to the left of the graph, or scroll using your mouse wheel.</li>
          </ul>"
        )
      )
    )
  )
}

mapServer <- function(id, data, variable, unit = "", title, footer) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    output$title <- renderUI({
      HTML(paste0("<div style='font-size: 20px; font-weight: bold;'>", title, "</div>"))
    })
    
    output$footer <- renderUI({
      HTML(footer)
    })
    
    filtered_data <- reactive({
      req(variable)  # Ensure that variable is not null or missing
      req(variable())  # Ensure that variable() is not null or missing
      req(data())      # Ensure that data() is not null or missing
      
      first_col_name <- names(data())[1]  # Get the name of the first column dynamically
      data() %>%
        filter(!!sym(first_col_name) == variable())  # Use the first column name for filtering
    })
    output$map <- renderHighchart({
      data <- filtered_data()
      hc_data <- data %>% 
        mutate(sub_region = as.character(sub_region)) %>%
        select(sub_region, value) %>%
        list_parse()
      
      variable_name <- variable()  # Get the selected variable name
      
      highchart(type = "map") %>%
        hc_add_series(
          mapData = geojson_list, 
          joinBy = c("sub_region", "sub_region"),
          data = hc_data,
          borderColor = "#FFFFFF",
          borderWidth = 0.5,
          states = list(
            hover = list(
              color = "#BADA55"
            )
          ),
          dataLabels = list(
            enabled = FALSE  # Disable the overlays
          ),
          tooltip = list(
            useHTML = TRUE,
            headerFormat = "<b>{point.key}</b><br/>",
            pointFormatter = JS(sprintf("function() {
          var value = this.value;
          var formattedValue;
          if (value >= 1000) {
            formattedValue = value.toLocaleString(undefined, {minimumFractionDigits: 0, maximumFractionDigits: 0});
          } else {
            formattedValue = value.toLocaleString(undefined, {minimumFractionDigits: 0, maximumFractionDigits: 2});
          }
          return '<b>' + this.sub_region + '</b><br/>' +
                 '%s: ' + formattedValue + ' %s';
        }", variable_name, unit))
          ),
          nullColor = '#E0E0E0'  # Color for regions with no data
        ) %>%
        hc_mapNavigation(enabled = TRUE) %>%
        hc_colorAxis(
          min = 0,
          stops = color_stops(5),
          labels = list(
            format = "{value:,.0f}"  # Ensure the labels show the correct values
          )
        ) %>%
        hc_chart(reflow = TRUE) %>% # Make chart responsive
        hc_legend(
          layout = "vertical",        # Change layout to vertical
          align = "right",            # Align the legend to the right
          verticalAlign = "middle",   # Align the legend to the middle vertically
          title = list(text = "Legend", style = list(fontSize = '15px')),
          itemStyle = list(
            width = '150px',          # Adjust width as needed
            style = "padding-right: 20px;"  # Add custom padding-right
          ),
          symbolHeight = 300          # Adjust the height of the color bar
        )
    })
    
    
  })
}

# File: module_other_animals.R

otherAnimalsUI <- function(id) {
  ns <- NS(id)
  sidebarLayout(
    sidebarPanel(
      width = 3,
      conditionalPanel(
        condition = "input.tabsetPanel === 'Map'",
        ns = ns,
        radioButtons(
          ns("variable"), 
          "Select Variable", 
          choices = c(
            "Goats and kids" = "Goats and kids",
            "Deer" = "Deer",
            "Horses" = "Horses",
            "Donkeys" = "Donkeys",
            "Camelids" = "Camelids",
            "Beehives" = "Beehives"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Time Series'",
        ns = ns,
        checkboxGroupInput(
          ns("timeseries_variables"),
          "Select Time Series Variables",
          choices = c(
            "Goats",
            "Deer",
            "Horses",
            "Donkeys",
            "Camelids",
            "Beehives"
          ),
          selected = c(
            "Goats",
            "Deer",
            "Horses",
            "Donkeys",
            "Camelids",
            "Beehives"
          )
        )
      ),
      conditionalPanel(
        condition = "input.tabsetPanel === 'Data Table'",
        ns = ns,
        radioButtons(
          ns("table_data"),
          "Select Data to Display",
          choices = c("Map Data" = "map", "Chart Data" = "timeseries"),
          selected = "map"
        )
      )
    ),
    mainPanel(
      width = 9,
      tabsetPanel(
        id = ns("tabsetPanel"),
        tabPanel("Map", mapUI(ns("map"))),
        tabPanel("Time Series", lineChartUI(ns("line"))),
        tabPanel("Data Table", DTOutput(ns("table")))
      )
    )
  )
}

otherAnimalsServer <- function(id) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    other_animals_data <- livestock_subregion %>%
      filter(`Livestock by category` %in% c(
        "Goats and kids",
        "Deer",
        "Horses",
        "Donkeys",
        "Camelids",
        "Beehives"
      )) %>%
      select(-Scotland) %>%
      mutate(across(everything(), as.character)) %>%
      pivot_longer(cols = -`Livestock by category`, names_to = "sub_region", values_to = "value") %>%
      mutate(value = safe_as_numeric(value))
    
    mapServer(
      id = "map",
      data = reactive({
        req(input$variable)
        other_animals_data %>% filter(`Livestock by category` == input$variable)
      }),
      footer = '<div style="font-size: 16px; font-weight: bold;"><a href="https://www.gov.scot/publications/results-scottish-agricultural-census-june-2023/documents/">Source: Scottish Agricultural Census: June 2023</a></div>',
      variable = reactive(input$variable),
      title = "Other Animals Distribution by Region"
    )
    
    chart_data <- reactive({
      req(input$timeseries_variables)
      filtered_data <- number_of_other_livestock %>%
        mutate(across(-`Livestock by category`, as.numeric)) %>%
        filter(`Livestock by category` %in% input$timeseries_variables) %>%
        pivot_longer(cols = -`Livestock by category`, names_to = "year", values_to = "value") %>%
        mutate(year = as.numeric(year))  # Ensure year is numeric
      filtered_data
    })
    
    
    lineChartServer(
      id = "line",
      chart_data = chart_data,
      title = "Other Animals Area Chart Data",
      yAxisTitle = "Number of Animals (1,000)",
      xAxisTitle = "Year",
      footer = '<div style="font-size: 16px; font-weight: bold;"><a href="https://www.gov.scot/publications/results-scottish-agricultural-census-june-2023/documents/">Source: Scottish Agricultural Census: June 2023</a></div>',
      x_col = "year",
      y_col = "value"
    )
    
    output$table <- renderDT({
      req(input$tabsetPanel == "Data Table")
      if (input$table_data == "map") {
        req(input$variable)
        other_animals_data %>%
          filter(`Livestock by category` == input$variable) %>%
          datatable()
      } else {
        number_of_other_livestock %>%
          pivot_longer(cols = -`Cattle by category`, names_to = "year", values_to = "value") %>%
          datatable()
      }
    })
  })
}

# Testing module
other_animals_demo <- function() {
  ui <- fluidPage(otherAnimalsUI("other_animals_test"))
  server <- function(input, output, session) {
    otherAnimalsServer("other_animals_test")
  }
  shinyApp(ui, server)
}

other_animals_demo()

## File: ui.R 
 source("util_updates.R")
source("util_functions.R")
source("util_options.R")
source("hc_theme.R")


source("function_summary.R")
source("function_line_chart.R")
source("function_area_chart.R")
source("function_bar_chart.R")
source("function_percentage_bar_chart.R")
source("function_timelapse_bar_chart.R")
source("function_breakdown_chart.R")
source("function_data_table.R")
source("function_regions_map.R")
source("function_map.R")


source("module_subsector_emissions.R")
source("module_information.R")
source("module_fertiliser_usage.R")
source("module_employees.R")
source("module_legal_responsibility.R")
source("module_farm_types.R")
source("module_occupiers.R")
source("module_land_use_summary.R")
source("module_owned_land.R")
source("module_cattle.R")
source("module_sheep.R")
source("module_pigs.R")
source("module_poultry.R")
source("module_other_animals.R")
source("module_cereals.R")
source("module_oilseed.R")
source("module_potatoes.R")
source("module_beans.R")
source("module_stockfeeding.R")
source("module_human_vegetables.R")
source("module_fruit.R")
source("module_home.R")
source("module_economy_summary.R")
source("module_animals_summary.R")
source("module_soil_testing.R")
source("module_manure.R")
source("module_nitrogen_usage.R")
source("module_crops_summary.R")
source("module_structure_information.R")


create_footer <- function() {
  div(
    class = "footer",
    span(style = "font-weight: bold;", "Content in development "),
    span("\n | Last updated: 23/08/2024"),
    img(src = "sg.png", alt = "SG Logo", style = "height: 30px; margin-left: 10px;")
  )
}


# Integrate the home module into the main UI
ui <- fluidPage(
  useShinyjs(),  # Initialize shinyjs
  theme = shinytheme("flatly"),
  tags$head(
    tags$link(rel = "stylesheet", type = "text/css", href = "styles.css"),
    includeHTML("google-analytics.html"),
    tags$script(HTML("
      $(document).on('click', 'a[data-value=\"home\"]', function() {
        history.pushState(null, '', '/AgStatsHub');
      });
    "))
  ),
  div(class = "container-fluid full-height",
      div(class = "content",
          navbarPage(
            title = div(
              tags$li(class = "nav-item", img(src = "RESAS Logo.png", class = "header-logo"))
            ),
            id = "navbar",
            windowTitle = "RESAS Agricultural Statistics Hub",  # Set the name for the browser tab
            tabPanel("Home", value = "home", homeUI("home")),  # Set this tabPanel as the default page
            navbarMenu("Structure",
                       tabPanel("Land Use", value = "land_use", landUseSummaryUI("land_use")),
                       tabPanel("Farm Types", value = "farm_types", farmTypesUI("farm_types")),
                       tabPanel("Employees", value = "employees", employeesMapUI("employees")),
                       tabPanel("Occupiers", value = "occupiers", occupiersUI("occupiers")),
                       tabPanel("Ownership Status", value = "owned_land", ownedLandUI("owned_land")),
                       tabPanel("Legal Responsibility", value = "legal_responsibility", legalResponsibilityUI("legal_responsibility")),
                       tabPanel("Further Information", value = "structure_information", structureInformationUI("structure_information"))
            ),
            navbarMenu("Agri-Environment",
                       tabPanel("Agriculture Emissions", value = "subsector", subsectorEmissionsUI("subsector")),
                       tabPanel("Nitrogen Usage", value = "nitrogen", nitrogenUI("nitrogen")),
                       tabPanel("Manure Usage", value = "manure", manureUI("manure")),
                       tabPanel("Soil Testing", value = "soil", soilTestingUI("soil")),
                       tabPanel("Fertiliser Usage", value = "fertiliser", fertiliserUsageUI("fertiliser")),
                       tabPanel("Further Information", value = "info", informationUI("info"))
            ),
            navbarMenu("Livestock",
                       tabPanel("Summary", value= "animals_summary_module", animalsSummaryUI("animals_summary_module")),
                       tabPanel("Cattle", value = "cattle_module", cattleUI("cattle_module")),
                       tabPanel("Sheep", value = "sheep_module", sheepUI("sheep_module")),
                       tabPanel("Pigs", value = "pigs_module", pigsUI("pigs_module")),
                       tabPanel("Poultry", value = "poultry_module", poultryUI("poultry_module")),
                       tabPanel("Other Animals", value = "other_animals_module", otherAnimalsUI("other_animals_module"))
            ),
            navbarMenu("Crops",
                       tabPanel("Summary", value = "crops_summary_module", cropsSummaryUI("crops_summary_module")),
                       tabPanel("Cereals", value = "cereals_module", cerealsUI("cereals_module")),
                       tabPanel("Oilseed", value = "oilseed_module", oilseedUI("oilseed_module")),
                       tabPanel("Potatoes", value = "potatoes_module", potatoesUI("potatoes_module")),
                       tabPanel("Beans", value = "beans_module", beansUI("beans_module")),
                       tabPanel("Stockfeeding", value = "stockfeeding_module", stockfeedingUI("stockfeeding_module")),
                       tabPanel("Vegetables", value = "human_vegetables_module", humanVegetablesUI("human_vegetables_module")),
                       tabPanel("Fruit", value = "fruit_module", fruitUI("fruit_module"))
            ),
            navbarMenu("Economy",
                       tabPanel("Summary", value = "economy", economySummaryUI("economy"))
            )
          ),
          create_footer()
      )
  )
)

## File: utils.R