Date: 2026-05-11

# Creating Maps and Plots

#### 2026-05-11

    library(manureshed)

## Overview

The `manureshed` package creates several types of visualizations:

1. Classification maps - Show nutrient balance categories
2. WWTP facility maps - Show treatment plant locations and sizes
3. Influence maps - Show how much WWTP contributes to each area
4. Comparison plots - Before/after adding WWTP data
5. Network plots - Show transitions between neighboring areas

## Quick Maps with quick_analysis()

The easiest way to get maps is with `quick_analysis()`:

    # This creates all maps automatically
    results <- quick_analysis(
      scale = "huc8",
      year = 2016,
      nutrients = "nitrogen",
      include_wwtp = TRUE,
      output_dir = "my_maps"
    )

    # Maps are saved in the output directory

## Step-by-Step Map Creation

### 1. Get Analysis Results First

    # Run analysis to get data for mapping
    results <- run_builtin_analysis(
      scale = "county",
      year = 2016,
      nutrients = c("nitrogen", "phosphorus"),
      include_wwtp = TRUE
    )

### 2. Agricultural Classification Maps

    # Basic nitrogen map
    nitrogen_map <- map_agricultural_classification(
      data = results$agricultural,
      nutrient = "nitrogen",
      classification_col = "N_class",
      title = "Agricultural Nitrogen Classifications"
    )

    # View the map
    print(nitrogen_map)

    # Save the map
    save_plot(nitrogen_map, "nitrogen_classes.png", width = 10, height = 8)

    # Phosphorus map
    phosphorus_map <- map_agricultural_classification(
      data = results$agricultural,
      nutrient = "phosphorus",
      classification_col = "P_class",
      title = "Agricultural Phosphorus Classifications"
    )

### 3. Combined Maps (Agricultural + WWTP)

    # Map showing effect of adding WWTP data
    combined_nitrogen <- map_agricultural_classification(
      data = results$integrated$nitrogen,
      nutrient = "nitrogen",
      classification_col = "combined_N_class",
      title = "Nitrogen with WWTP Integration"
    )

    combined_phosphorus <- map_agricultural_classification(
      data = results$integrated$phosphorus,
      nutrient = "phosphorus",
      classification_col = "combined_P_class",
      title = "Phosphorus with WWTP Integration"
    )

### 4. WWTP Facility Maps

    # Map showing WWTP locations colored by size
    facility_map <- map_wwtp_points(
      wwtp_sf = results$wwtp$nitrogen$spatial_data,
      nutrient = "nitrogen",
      title = "Nitrogen WWTP Facilities"
    )

    print(facility_map)

### 5. WWTP Influence Maps

    # Map showing how much WWTP contributes to each area
    influence_map <- map_wwtp_influence(
      data = results$integrated$nitrogen,
      nutrient = "nitrogen",
      title = "WWTP Contribution to Total Nitrogen"
    )

    print(influence_map)

## Comparison Plots

### Before/After WWTP Integration

    # Create summary data
    summary_data <- create_classification_summary(
      data = results$integrated$nitrogen,
      agricultural_col = "N_class",
      combined_col = "combined_N_class"
    )

    # Before/after bar chart
    comparison_plot <- plot_before_after_comparison(
      data = summary_data,
      nutrient = "nitrogen",
      title = "Effect of Adding WWTP Data"
    )

    print(comparison_plot)

    # Impact ratios
    impact_plot <- plot_impact_ratios(
      data = summary_data,
      title = "WWTP Impact on Classifications"
    )

    # Absolute changes
    change_plot <- plot_absolute_changes(
      data = summary_data,
      title = "Change in Number of Counties"
    )

## Network Plots

### Spatial Transition Networks

    # Add coordinates to the data
    centroids <- add_centroid_coordinates(results$integrated$nitrogen)

    # Calculate how often different classes are next to each other
    transitions <- calculate_transition_probabilities(
      centroids, "combined_N_class"
    )

    # Create network plot
    create_network_plot(
      transition_df = transitions,
      nutrient = "nitrogen",
      analysis_type = "WWTP + Agricultural",
      output_path = "nitrogen_network.png"
    )

    # View the transition table
    print(transitions)

## Customizing Maps

### Save Options

    # Different resolutions and formats
    save_plot(nitrogen_map, "map_web.png", width = 8, height = 6, dpi = 150)      # Web
    save_plot(nitrogen_map, "map_print.png", width = 10, height = 8, dpi = 300)   # Print
    save_plot(nitrogen_map, "map_publication.png", width = 12, height = 9, dpi = 600) # Publication

    # Vector formats
    save_plot(nitrogen_map, "map_vector.pdf", width = 10, height = 8)

### Custom Colors

    # Use different colors
    custom_map <- map_agricultural_classification(
      data = results$agricultural,
      nutrient = "nitrogen",
      classification_col = "N_class",
      title = "Custom Colors"
    ) +
      ggplot2::scale_fill_manual(
        values = c("Source" = "red", "Sink_Deficit" = "blue",
                   "Sink_Fertilizer" = "green", "Within_County" = "yellow",
                   "Excluded" = "gray"),
        labels = c("Source", "Sink Deficit", "Sink Fertilizer",
                   "Within County", "Excluded")
      )

## Working with Different Scales

### County Level

    # County analysis
    county_results <- run_builtin_analysis(scale = "county", year = 2016,
                                           nutrients = "nitrogen", include_wwtp = TRUE)

    county_map <- map_agricultural_classification(
      county_results$agricultural, "nitrogen", "N_class",
      "County-Level Nitrogen Classifications"
    )

### HUC8 Watersheds

    # Watershed analysis
    huc8_results <- run_builtin_analysis(scale = "huc8", year = 2016,
                                         nutrients = "nitrogen", include_wwtp = TRUE)

    huc8_map <- map_agricultural_classification(
      huc8_results$agricultural, "nitrogen", "N_class",
      "Watershed-Level Nitrogen Classifications"
    )

### HUC2 Regions

    # Regional analysis
    huc2_results <- run_builtin_analysis(scale = "huc2", year = 2016,
                                         nutrients = "nitrogen", include_wwtp = TRUE)

    huc2_map <- map_agricultural_classification(
      huc2_results$agricultural, "nitrogen", "N_class",
      "Regional-Level Nitrogen Classifications"
    )

## State-Specific Maps

    # Create maps for a specific state
    iowa_results <- run_state_analysis(
      state = "IA",
      scale = "county",
      year = 2016,
      nutrients = "nitrogen",
      include_wwtp = TRUE
    )

    iowa_map <- map_agricultural_classification(
      iowa_results$agricultural, "nitrogen", "N_class",
      "Iowa Nitrogen Classifications"
    )

    # Quick state maps
    texas_maps <- quick_state_analysis(
      state = "TX",
      scale = "huc8",
      year = 2015,
      nutrients = "phosphorus",
      create_maps = TRUE
    )

## Multi-Panel Figures

    # Create side-by-side comparison
    library(ggplot2)
    library(gridExtra)  # or cowplot

    # Create two maps
    map1 <- map_agricultural_classification(
      results$agricultural, "nitrogen", "N_class", "Agricultural Only"
    )

    map2 <- map_agricultural_classification(
      results$integrated$nitrogen, "nitrogen", "combined_N_class", "With WWTP"
    )

    # Combine them
    combined_figure <- grid.arrange(map1, map2, ncol = 2)

    # Save combined figure
    ggsave("combined_maps.png", combined_figure, width = 16, height = 8)

## Tips for Good Maps

### Map Quality

    # For presentations (screen)
    save_plot(map, "presentation.png", width = 12, height = 8, dpi = 150)

    # For reports (print)
    save_plot(map, "report.png", width = 10, height = 8, dpi = 300)

    # For journals (high quality)
    save_plot(map, "journal.png", width = 8, height = 6, dpi = 600)

### Color Choices

- Use the built-in colors for consistency
- Consider colorblind-friendly palettes
- Make sure colors show up in grayscale
- Use high contrast for important categories

### File Management

    # Organize your outputs
    create_maps_folder <- function(analysis_name) {
      dir.create(analysis_name, showWarnings = FALSE)
      dir.create(file.path(analysis_name, "maps"), showWarnings = FALSE)
      dir.create(file.path(analysis_name, "plots"), showWarnings = FALSE)
      dir.create(file.path(analysis_name, "data"), showWarnings = FALSE)
    }

    create_maps_folder("nitrogen_analysis_2016")

## Troubleshooting

### Common Issues

    # If maps are blank, check your data
    quick_check(results)

    # If colors are wrong, check classification column names
    table(results$agricultural$N_class)

    # If coordinates are missing
    centroids <- add_centroid_coordinates(results$agricultural)

    # If maps are too slow, try smaller scale or fewer years

This covers the essential mapping and visualization functions in `manureshed`. The package makes it easy to create publication-quality maps and plots for nutrient flow analysis.