Home GHG emissions

 Load utility data
data_utilities <- read.csv("data/utilities.csv", sep=";", dec=",")

# Date format handling
data_utilities$date <- as.Date(paste0("01-", data_utilities$date), format = "%d-%m-%Y")

# Plot
library(ggplot2)

ggplot(data_utilities, aes(x = date)) +
  geom_line(aes(y = Gas, color = "Gas (m³)"), linewidth = 1) +
  geom_line(aes(y = Solar, color = "Solar (kWh)"), linewidth = 1) +
  geom_line(aes(y = Energy, color = "Electricity Usage (kWh)"), linewidth = 1) +
  labs(x = "Date",
       y = "Usage (kWh or m³)",
       color = "Utility Type") +
  theme(
    legend.position = "top",
    legend.justification = "center"
  )

# Energy Intensity data
# ember-energy requires an API key. This script will look for the existence of energy_intensity.csv, and otherwise requires an API key to fetch new data. 
API_KEY <- "YOUR-API-KEY"

# Check if the file 'energy_data.csv' exists
if (file.exists("data/energy_intensity.csv")) {
  # print("File 'energy_intensity.csv' already exists.")
  data_energy_intensity <- read.csv("data/energy_intensity.csv")
} else if (API_KEY == "YOUR-API-KEY") {
  print("No valid ember-energy API key")
} else {
  # Requesting the energy intensity data
  # Define the URL and the parameters
  url <- "https://api.ember-energy.org/v1/carbon-intensity/monthly"
  params <- list(
    entity = "Netherlands", 
    start_date = "2022-01", 
    include_all_dates_value_range = "false",
    api_key = API_KEY
  )
  library(httr)
  library(jsonlite)

  # Send the GET request
  response <- GET(url, query = params, add_headers("accept" = "application/json"))

  # Convert teh response to JSON
  data <- content(response, as = "text")
  data_json <- fromJSON(data)

  # Check if the JSON contains data or an error
  if (length(data_json$data) == 0) {
    print("Something went wrong with the API request: No data found in 'data_json$data'.")
  } else {
    energy <- as.data.frame(data_json$data)
    data_energy_intensity <- data.frame(
      date = energy$date,
      emissions_intensity_gco2_per_kwh = energy$emissions_intensity_gco2_per_kwh
    )
    
    # As energy_intensity.csv does not exist, we save the data
    write.csv(data_energy_intensity, "data/energy_intensity.csv")
  }
}


# Ensure 'date' is in Date format
data_energy_intensity$date <- as.Date(data_energy_intensity$date)


# Plot
ggplot(data_energy_intensity, aes(x = date, y = emissions_intensity_gco2_per_kwh)) +
  geom_line(color = "blue", linewidth = 1) +
  labs(
    x = "Date",
    y = expression("Emissions Intensity (g CO"[2] ~ "/ kWh)")
  )

# Merge datasets by date
data_emissions <- data_utilities %>%
  inner_join(data_energy_intensity, by = "date") %>%
  mutate(
    Electricity = (Energy * emissions_intensity_gco2_per_kwh) / 1000,  # Convert g to kg
    Gas = Gas * 1.78  # kg CO2 per m³) %>%
  select(date, Electricity, Gas)  # Keep relevant columns

# Convert to long format for ggplot
data_emissions_long <- data_emissions %>%
  pivot_longer(cols = c(Electricity, Gas), 
               names_to = "Source", values_to = "Emissions")

if (knitr::is_html_output()) {
  # Interactive Stacked Area Plot
ggplot(data_emissions_long, aes(x = date, y = Emissions, fill = Source)) +
  geom_area(alpha = 0.6) +  # Stacked area plot
  labs(
    x = "Date",
    y = expression("CO"[2] ~ " Emissions (kg)"),  # Fix CO₂ subscript
    fill = "Emission Source"
  ) +
  theme(
    legend.position = "top",  # Place legend at the top
    legend.justification = "center"  # Center-align the legend
  )
} else {
  # Static Stacked Area Plot (ggplot2)
  ggplot(data_emissions_long, aes(x = date, y = Emissions, fill = Source)) +
    geom_area(alpha = 0.6, position = "stack") +  # Stacked area plot
    labs(
      x = "Date",
      y = expression("CO"[2] ~ " Emissions (kg)"),  # Fix CO₂
      fill = "Emission Source"
    ) +
    theme(
      legend.position = "top",
      legend.justification = "center"
    )
}

# For the tables:
data_emissions$total <- data_emissions$Gas + data_emissions$Electricity

library(dplyr)
library(knitr)

# Ensure 'date' is in Date format
data_emissions$date <- as.Date(data_emissions$date)

# Extract year from date
data_emissions$year <- format(data_emissions$date, "%Y")

# Aggregate emissions per year
yearly_emissions <- data_emissions %>%
  group_by(year) %>%
  summarise(
    `Gas emissions (kg CO₂)` = sum(Gas, na.rm = TRUE),
    `Electricity emissions (kg CO₂)` = sum(Electricity, na.rm = TRUE),
    `Total emissions (kg CO₂)` = sum(total, na.rm = TRUE))

# Display the table in Quarto
kable(yearly_emissions, format = "html", digits = 2, align = "c")