Draw Postcode4 Map

This repository contains an R script for visualizing postcode (4) maps using the leaflet package. The script integrates geographical data with numerical values to create interactive maps.

Overview

The code provides a comprehensive approach to:

1. Install and Load Dependencies: Ensures necessary R packages are available.
2. Preprocess GeoJSON Data: Simplifies and stores geographical data in an .RData file for efficiency.
3. Merge Data: Combines geographical data with numerical values from an Excel file.
4. Plot the Data: Uses leaflet to generate interactive maps based on the data.

Instructions

1. Install and Load Dependencies

   # Clear environment and load dependencies
   rm(list = ls())
   if (!require("openxlsx")) install.packages("openxlsx")
   library(openxlsx)
   if (!require("dplyr")) install.packages("dplyr")
   library(dplyr)
   if (!require("sf")) install.packages("sf")
   library(sf)
   if (!require("leaflet")) install.packages("leaflet")
   library(leaflet)

2. Preprocess GeoJSON Data (optional)

# geojson_pc4 <- st_read("data/postcode4.geojson") 
# geojson_pc4 <- st_simplify(geojson_pc4, dTolerance = 2) 
# geojson_pc4$pc4_code <- as.numeric(geojson_pc4$pc4_code) 
# save(geojson_pc4, file = "data/postcode4.RData") 

3. Merge Data

load("data/postcode4.RData")
data <- read.xlsx("data/data.xlsx")
data <- merge(geojson_pc4, data, by.x = "pc4_code", by.y = "Postcode", all.x = TRUE)
data <- data %>% rename(Postcode = pc4_code)
rm(geojson_pc4)

4. Plot the Data

toplot <- names(st_drop_geometry(data[, 8])) 
pal <- colorNumeric(palette = "viridis", domain = range(data[[toplot]], na.rm = TRUE), na.color = "transparent")

leaflet(data = data) %>%
  addProviderTiles(providers$OpenStreetMap) %>%
  addPolygons(
    fillColor = ~pal(data[[toplot]]),
    weight = 1,
    opacity = 1,
    color = "black",
    dashArray = "3",
    fillOpacity = 0.5,
    popup = ~paste0(
      "Postcode: ", data$Postcode, "<br>",
      "Gemeente: ", data$gem_name, "<br>",
      toplot, ": ", data[[toplot]]
    )
  ) %>%
  addLegend(
    pal = pal,
    values = data[[toplot]],
    title = toplot,
    position = "bottomright"
  ) %>%
  setView(
    lng = mean(st_coordinates(data)[, 1]),
    lat = mean(st_coordinates(data)[, 2]),
    zoom = 10
  )

5. Function for Plotting

draw_postcode4_map <- function(postcode4_path, data_path, column_to_plot) {
  # Function to draw postcode4 maps. 
  # Requires a RData containing the processed geojson data at postcode4_path
  # Requires the excel to have a column "Postcode" containing the postcode4 codes and corresponding values in column "column_to_plot"
  if (!require("readxl")) install.packages("readxl")
  if (!require("dplyr")) install.packages("dplyr")
  if (!require("sf")) install.packages("sf")
  if (!require("leaflet")) install.packages("leaflet")
  library(readxl)
  library(dplyr)
  library(sf)
  library(leaflet)
  
  # Load data
  load(postcode4_path)
  data <- read_excel(data_path)
  
  # Merge the shapefile data with the Excel data
  data <- merge(geojson_pc4, data, by.x = "pc4_code", by.y = "Postcode", all.x = TRUE)
  
  # Rename for clarity
  data <- data %>%
    rename(Postcode = pc4_code)
  
  # Define the color palette based on the selected column
  pal <- colorNumeric(palette = "viridis", domain = range(data[[column_to_plot]], na.rm = TRUE), na.color = "transparent")
  
  # Create the map
  map <- leaflet(data = data) %>%
    addProviderTiles(providers$OpenStreetMap) %>%
    addPolygons(
      fillColor = ~pal(data[[column_to_plot]]),
      weight = 1,
      opacity = 1,
      color = "black",
      dashArray = "3",
      fillOpacity = 0.5,
      popup = ~paste0(
        "Postcode: ", data$Postcode, "<br>",
        "Gemeente: ", data$gem_name, "<br>",
        column_to_plot, ": ", data[[column_to_plot]]
        )
    ) %>%
    addLegend(
      pal = pal,
      values = data[[column_to_plot]],
      title = column_to_plot,
      position = "bottomright"
    ) %>%
    setView(
      lng = mean(st_coordinates(data)[, 1]),
      lat = mean(st_coordinates(data)[, 2]),
      zoom = 10
    )
  
  # Return the map object
  return(map)
}

# Example usage:
postcode4_map <- draw_postcode4_map("data/postcode4.RData", "data/data.xlsx", "population")
postcode4_map  # This will display the map
Note: Sample data is generated randomly and does not reflect real-world statistics.

More Information For more details and the complete code, please visit the GitHub repository.