Author: Olatunde D. Akanbi
Date: 2026-05-11

# Getting Started with manureshed

#### Olatunde D. Akanbi

#### 2026-05-11

    library(manureshed)

## What is manureshed?

The `manureshed` package analyzes agricultural nutrient balances at different spatial scales (county, HUC8 watershed, HUC2 region) and can integrate wastewater treatment plant (WWTP) effluent data to show how municipal nutrient loads affect agricultural areas.

## View cheatsheet

    view_cheatsheet()

## Quick Start - Complete Analysis

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

    # Complete analysis with maps and plots
    results <- quick_analysis(
      scale = "huc8",           # Choose: "county", "huc8", or "huc2"
      year = 2016,              # Any year 1987-2016
      nutrients = "nitrogen",   # Choose: "nitrogen", "phosphorus", or both
      include_wwtp = TRUE       # Include wastewater plants (2007-2016 only)
    )

This creates: - Classification maps - WWTP facility maps
- Network plots - Comparison charts - All saved to your output directory

## Step-by-Step Analysis

### 1. Check Available Data

    # See what data is available
    check_builtin_data()

    # Download all data (optional, ~40MB)
    download_all_data()

### 2. Basic Agricultural Analysis

    # Analyze just agricultural data
    results <- run_builtin_analysis(
      scale = "county",
      year = 2010,
      nutrients = "nitrogen",
      include_wwtp = FALSE    # No WWTP data
    )

    # Quick summary
    summarize_results(results)

### 3. Add WWTP Data

    # Analysis with wastewater plants (2007-2016 available)
    results_wwtp <- run_builtin_analysis(
      scale = "huc8",
      year = 2016,
      nutrients = c("nitrogen", "phosphorus"),
      include_wwtp = TRUE
    )

    # See the difference WWTP makes
    comparison <- compare_analyses(results, results_wwtp, "nitrogen")
    print(comparison)

## Understanding the Results

### Classifications

Each spatial unit gets classified into:

- Source: Has excess nutrients to export
- Sink Deficit: Needs nutrient imports
- Sink Fertilizer: Has fertilizer surplus, could accept manure
- Within Watershed/County: Balanced
- Excluded: Too little cropland to analyze

### Accessing Results

    # Agricultural data with classifications
    agri_data <- results$agricultural

    # WWTP facility data
    wwtp_facilities <- results$wwtp$nitrogen$facility_data

    # Combined results (agricultural + WWTP)
    combined_data <- results$integrated$nitrogen

    # Analysis settings
    parameters <- results$parameters

## Creating Maps

### Classification Maps

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

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

### WWTP Maps

    # Map WWTP facilities
    facility_map <- map_wwtp_points(
      results$wwtp$nitrogen$spatial_data,
      nutrient = "nitrogen",
      title = "Nitrogen WWTP Facilities"
    )

    # Map WWTP influence on agricultural areas
    influence_map <- map_wwtp_influence(
      results$integrated$nitrogen,
      nutrient = "nitrogen",
      title = "WWTP Influence on Nitrogen"
    )

## Working with Different Years

### Single Years

    # Any year 1987-2016 for agricultural data
    results_1990 <- run_builtin_analysis(scale = "county", year = 1990,
                                         nutrients = "nitrogen", include_wwtp = FALSE)

    results_2005 <- run_builtin_analysis(scale = "huc8", year = 2005,
                                         nutrients = "phosphorus", include_wwtp = FALSE)

    # WWTP data available 2007-2016
    results_2012 <- run_builtin_analysis(scale = "huc8", year = 2012,
                                         nutrients = "nitrogen", include_wwtp = TRUE)

### Multiple Years

    # Analyze several years at once
    batch_results <- batch_analysis_years(
      years = 2014:2016,
      scale = "county",
      nutrients = "nitrogen",
      include_wwtp = TRUE
    )

## Using Custom WWTP Data

For years outside 2007-2016, provide your own WWTP data:

    # Use your own WWTP files
    results_2020 <- run_builtin_analysis(
      scale = "huc8",
      year = 2020,  # Agricultural data available
      nutrients = "nitrogen",
      include_wwtp = TRUE,
      custom_wwtp_nitrogen = "my_wwtp_data_2020.csv",
      wwtp_load_units = "lbs"  # Handle different units
    )

## State-Specific Analysis

    # Analyze a specific state
    texas_results <- run_state_analysis(
      state = "TX",
      scale = "county",
      year = 2016,
      nutrients = "nitrogen",
      include_wwtp = TRUE
    )

    # Quick state analysis with maps
    ohio_quick <- quick_state_analysis(
      state = "OH",
      scale = "huc8",
      year = 2015,
      nutrients = "phosphorus"
    )

## Loading Individual Datasets

    # Load specific datasets
    county_2016 <- load_builtin_nugis("county", 2016)
    huc8_boundaries <- load_builtin_boundaries("huc8")
    wwtp_nitrogen <- load_builtin_wwtp("nitrogen", 2012)

    # Check what years are available
    list_available_years()

## Tips for Success

### Memory Management

    # For large analyses, clear cache if needed
    clear_data_cache()

    # Check package health
    health_check()

### Quality Checks

    # Always validate your results
    quick_check(results)

    # Get package citation
    citation_info()

## Next Steps

- Advanced Features: See `vignette("advanced-features")` for state analysis, custom thresholds, parallel processing
- Visualization Guide: See `vignette("visualization-guide")` for detailed mapping options
- Data Integration: See `vignette("data-integration")` for using custom datasets

## Getting Help

    # Function documentation
    ?run_builtin_analysis
    ?quick_analysis
    ?map_agricultural_classification

    # Package overview
    ?manureshed

    # Check if everything is working
    health_check()

That’s it! You now know the basics of using `manureshed` for nutrient flow analysis.