Author: Olatunde D. Akanbi
Date: 2026-03-09

# Using Your Own Data

#### Olatunde D. Akanbi

#### 2026-03-09

    library(manureshed)

## Overview

The `manureshed` package includes data from 1987-2016, but you can add your own:

- WWTP data for years outside 2007-2016
- Agricultural data from other sources
- Custom spatial boundaries
- Industrial or urban nutrient sources

## Using Custom WWTP Data

### For Years Outside 2007-2016

The package has WWTP data for 2007-2016. For other years, provide your own:

    # Use your WWTP files for 2020
    results_2020 <- run_builtin_analysis(
      scale = "huc8",
      year = 2020,  # Agricultural data available 1987-2016
      nutrients = "nitrogen",
      include_wwtp = TRUE,
      custom_wwtp_nitrogen = "nitrogen_2021.csv",
      wwtp_load_units = "kg"
    )

    # 3. Check results
    summarize_results(results_custom)
    quick_check(results_custom)

    # 4. Create maps
    nitrogen_map <- map_agricultural_classification(
      results_custom$integrated$nitrogen,
      "nitrogen", "combined_N_class",
      "2021 Nitrogen with Custom WWTP Data"
    )

    save_plot(nitrogen_map, "custom_analysis_2021.png")

    # 5. Save results
    save_spatial_data(
      results_custom$integrated$nitrogen,
      "nitrogen_2021_results.rds"
    )

This example shows the complete workflow from custom data to final maps and saved results.

## Advanced Custom Data Integration

### Adding Other Nutrient Sources

You can integrate additional nutrient sources beyond WWTP:

    # Example: Adding industrial sources
    industrial_data <- data.frame(
      Facility_Name = c("Steel Plant A", "Chemical Plant B", "Food Processor C"),
      Lat = c(41.5, 40.7, 42.1),
      Long = c(-81.7, -82.1, -83.5),
      N_Load_tons = c(50, 75, 30),
      P_Load_tons = c(10, 15, 8),
      Source_Type = "Industrial"
    )

    # Convert to spatial format
    industrial_sf <- st_as_sf(industrial_data,
                             coords = c("Long", "Lat"),
                             crs = 4326) %>%
      st_transform(5070)  # Transform to analysis CRS

    # Aggregate by spatial units (example for HUC8)
    boundaries <- load_builtin_boundaries("huc8")
    industrial_aggregated <- wwtp_aggregate_by_boundaries(
      industrial_sf, boundaries, "nitrogen", "huc8"
    )

    # Add to existing results
    # (You would modify the integration functions to include industrial sources)

### Working with Different Time Periods

    # Create a time series dataset
    years_to_analyze <- 2018:2022
    time_series_results <- list()

    for (year in years_to_analyze) {
      # Check if you have WWTP data for this year
      wwtp_file <- paste0("wwtp_nitrogen_", year, ".csv")

      if (file.exists(wwtp_file)) {
        time_series_results[[as.character(year)]] <- run_builtin_analysis(
          scale = "huc8",
          year = year,
          nutrients = "nitrogen",
          include_wwtp = TRUE,
          custom_wwtp_nitrogen = wwtp_file,
          save_outputs = FALSE,
          verbose = FALSE
        )
      } else {
        # Agricultural only if no WWTP data
        time_series_results[[as.character(year)]] <- run_builtin_analysis(
          scale = "huc8",
          year = year,
          nutrients = "nitrogen",
          include_wwtp = FALSE,
          save_outputs = FALSE,
          verbose = FALSE
        )
      }
    }

    # Compare results across years
    yearly_summary <- do.call(rbind, lapply(names(time_series_results), function(year) {
      result <- time_series_results[[year]]
      classes <- table(result$agricultural$N_class)

      data.frame(
        Year = as.numeric(year),
        Total_Units = sum(classes),
        Source_Units = classes["Source"] %||% 0,
        Sink_Units = sum(classes[c("Sink_Deficit", "Sink_Fertilizer")], na.rm = TRUE),
        Excluded_Units = classes["Excluded"] %||% 0
      )
    }))

    print(yearly_summary)

### Custom Agricultural Data

If you have agricultural data from other sources:

    # Example: Custom agricultural data format
    custom_farm_data <- data.frame(
      County_FIPS = c("39001", "39003", "39005"),
      Manure_N_kg = c(100000, 150000, 200000),
      Manure_P_kg = c(20000, 30000, 40000),
      Fertilizer_N_kg = c(50000, 75000, 100000),
      Fertilizer_P_kg = c(10000, 15000, 20000),
      N_Fixation_kg = c(25000, 40000, 35000),
      Crop_N_Removal_kg = c(80000, 120000, 140000),
      Crop_P_Removal_kg = c(15000, 25000, 30000),
      Cropland_acres = c(45000, 67000, 52000)
    )

    # Convert to package format
    standardized_farm_data <- data.frame(
      ID = custom_farm_data$County_FIPS,
      NAME = paste("County", substr(custom_farm_data$County_FIPS, 3, 5)),
      manure_N = custom_farm_data$Manure_N_kg,
      manure_P = custom_farm_data$Manure_P_kg,
      fertilizer_N = custom_farm_data$Fertilizer_N_kg,
      fertilizer_P = custom_farm_data$Fertilizer_P_kg,
      N_fixation = custom_farm_data$N_Fixation_kg,
      crop_removal_N = custom_farm_data$Crop_N_Removal_kg,
      crop_removal_P = custom_farm_data$Crop_P_Removal_kg,
      cropland = custom_farm_data$Cropland_acres
    )

    # Apply classifications
    custom_classified <- standardized_farm_data %>%
      agri_classify_nitrogen(cropland_threshold = 1234, scale = "county") %>%
      agri_classify_phosphorus(cropland_threshold = 1234, scale = "county")

    print(custom_classified)

### Data Validation and Quality Control

    # Function to validate your custom data
    validate_custom_data <- function(data, data_type = "wwtp") {

      issues <- list()

      if (data_type == "wwtp") {
        # Check required columns
        required_cols <- c("Facility_Name", "Lat", "Long")
        missing_cols <- setdiff(required_cols, names(data))
        if (length(missing_cols) > 0) {
          issues$missing_columns <- missing_cols
        }

        # Check coordinate ranges (for US)
        if ("Lat" %in% names(data) && "Long" %in% names(data)) {
          invalid_coords <- sum(data$Lat < 24 | data$Lat > 50 |
                               data$Long < -125 | data$Long > -66, na.rm = TRUE)
          if (invalid_coords > 0) {
            issues$invalid_coordinates <- invalid_coords
          }
        }

        # Check for negative loads
        load_cols <- names(data)[grepl("Load|load", names(data))]
        for (col in load_cols) {
          if (col %in% names(data)) {
            negative_loads <- sum(data[[col]] < 0, na.rm = TRUE)
            if (negative_loads > 0) {
              issues[[paste0("negative_", col)]] <- negative_loads
            }
          }
        }
      }

      if (data_type == "agricultural") {
        # Check for negative values in nutrient data
        nutrient_cols <- c("manure_N", "manure_P", "fertilizer_N", "fertilizer_P",
                           "crop_removal_N", "crop_removal_P", "cropland")

        for (col in nutrient_cols) {
          if (col %in% names(data)) {
            negative_values <- sum(data[[col]] < 0, na.rm = TRUE)
            if (negative_values > 0) {
              issues[[paste0("negative_", col)]] <- negative_values
            }
          }
        }
      }

      if (length(issues) == 0) {
        message("âœ“ Data validation passed")
      } else {
        message("âš  Data validation issues found:")
        for (issue in names(issues)) {
          message("  ", issue, ": ", issues[[issue]])
        }
      }

      return(issues)
    }

    # Use the validation function
    # validate_custom_data(your_wwtp_data, "wwtp")
    # validate_custom_data(your_farm_data, "agricultural")

### Exporting Results

    # Export results in different formats
    export_analysis_results <- function(results, output_dir = "exports") {

      dir.create(output_dir, showWarnings = FALSE)

      # Export agricultural data as CSV
      agri_csv <- st_drop_geometry(results$agricultural)
      write.csv(agri_csv, file.path(output_dir, "agricultural_results.csv"), row.names = FALSE)

      # Export as shapefile (if sf package available)
      if (requireNamespace("sf", quietly = TRUE)) {
        st_write(results$agricultural, file.path(output_dir, "agricultural_results.shp"),
                 delete_dsn = TRUE, quiet = TRUE)
      }

      # Export WWTP facilities if available
      if ("wwtp" %in% names(results)) {
        for (nutrient in names(results$wwtp)) {
          facility_data <- results$wwtp[[nutrient]]$facility_data
          write.csv(facility_data,
                    file.path(output_dir, paste0("wwtp_", nutrient, "_facilities.csv")),
                    row.names = FALSE)
        }
      }

      # Export integrated results if available
      if ("integrated" %in% names(results)) {
        for (nutrient in names(results$integrated)) {
          integrated_csv <- st_drop_geometry(results$integrated[[nutrient]])
          write.csv(integrated_csv,
                    file.path(output_dir, paste0("integrated_", nutrient, "_results.csv")),
                    row.names = FALSE)
        }
      }

      message("Results exported to: ", output_dir)
    }

    # Use the export function
    # export_analysis_results(results_custom, "my_exports")

## Troubleshooting Common Issues

### WWTP Data Issues

    # Common WWTP data problems and solutions

    # Problem 1: "No valid facilities remaining after cleaning"
    # Solution: Check coordinates and load values
    wwtp_data <- read.csv("your_wwtp_file.csv")
    summary(wwtp_data)  # Look for obvious issues

    # Check coordinate ranges
    summary(wwtp_data$Latitude)   # Should be ~24-50 for US
    summary(wwtp_data$Longitude)  # Should be ~-125 to -66 for US

    # Check load values
    summary(wwtp_data$Load)  # Should be positive numbers

    # Problem 2: Wrong coordinate system
    # Solution: Make sure coordinates are in decimal degrees (not UTM)
    # Example conversion if needed:
    # library(sf)
    # points_utm <- st_as_sf(data, coords = c("X_UTM", "Y_UTM"), crs = 32617)
    # points_dd <- st_transform(points_utm, 4326)
    # coords_dd <- st_coordinates(points_dd)

    # Problem 3: Mixed units in same file
    # Solution: Standardize units before analysis
    standardize_mixed_units <- function(data, load_col, unit_col) {
      data$standardized_load <- ifelse(
        data[[unit_col]] == "kg", data[[load_col]],
        ifelse(data[[unit_col]] == "lbs", data[[load_col]] / 2.20462,
               data[[load_col]] * 907.185)  # assuming tons
      )
      return(data)
    }

### Agricultural Data Issues

    # Common agricultural data problems

    # Problem: Impossible nutrient balances
    # Solution: Check your units and conversion factors
    check_nutrient_balance <- function(data) {
      # Calculate simple checks
      data$total_inputs_N <- data$manure_N + data$fertilizer_N + data$N_fixation
      data$efficiency_N <- data$crop_removal_N / data$total_inputs_N

      # Flag suspicious values
      suspicious <- data$efficiency_N > 2.0 | data$efficiency_N < 0.1

      if (sum(suspicious, na.rm = TRUE) > 0) {
        message("Warning: ", sum(suspicious, na.rm = TRUE), " units have suspicious N efficiency")
        print(data[suspicious, c("ID", "total_inputs_N", "crop_removal_N", "efficiency_N")])
      }

      return(data)
    }

    # Problem: Missing spatial boundaries
    # Solution: Use built-in boundaries or provide your own
    # county_boundaries <- load_builtin_boundaries("county")
    # your_data_with_boundaries <- merge(your_data, county_boundaries, by.x = "FIPS", by.y = "FIPS")

## Getting Help

### Common Questions

Q: What format should my WWTP data be in? A: CSV file with columns for facility name, latitude, longitude, annual load, and state. The package can auto-detect most EPA formats.

Q: What units can I use for loads?
A: “kg”, “lbs”, “pounds”, or “tons”. Specify with `wwtp_load_units`.

Q: Can I use data from other countries? A: The spatial boundaries are US-only, but you could provide your own boundaries and modify the coordinate system.

Q: How do I handle missing data? A: The package excludes facilities with missing coordinates or loads. Clean your data first for best results.

Q: My analysis is running slowly. What can I do? A: Try using a smaller spatial scale (HUC2 < HUC8 < County in terms of processing time), fewer years, or clear the data cache with `clear_data_cache()`.

Q: How do I cite the package and data? A: Use `citation_info()` to get proper citations for the package and underlying datasets.

### Function Help

    # Get help on specific functions
    ?load_user_wwtp
    ?run_builtin_analysis
    ?wwtp_clean_data

    # Check what data is available
    check_builtin_data()
    list_available_years()

    # Package health check
    health_check()

    # Test data download connection
    test_osf_connection()

### Getting More Help

- Check the other vignettes: `vignette("getting-started")`, `vignette("visualization-guide")`, `vignette("advanced-features")`
- Look at function documentation: `?function_name`
- Check the package website or GitHub repository for examples
- Use `quick_check()` to validate your results

The `manureshed` package is designed to work with your data as easily as possible. Start with the built-in examples, then gradually add your own data as needed.nitrogen_2020.csv” )

### Handling Different Data Formats

#### Standard EPA Format

Most EPA WWTP files work automatically:

    # Standard EPA format (auto-detected)
    results <- run_builtin_analysis(
      scale = "county",
      year = 2018,
      nutrients = c("nitrogen", "phosphorus"),
      include_wwtp = TRUE,
      custom_wwtp_nitrogen = "nitrogen_2018.csv",
      custom_wwtp_phosphorus = "phosphorus_2018.csv"
    )

#### Different Units

    # If your data uses pounds instead of kg
    results <- run_builtin_analysis(
      scale = "huc8",
      year = 2019,
      nutrients = "nitrogen",
      include_wwtp = TRUE,
      custom_wwtp_nitrogen = "nitrogen_pounds_2019.csv",
      wwtp_load_units = "lbs"  # Converts automatically
    )

    # Other units: "kg", "lbs", "pounds", "tons"

#### Different File Format

    # If headers are in different rows
    results <- run_builtin_analysis(
      scale = "county",
      year = 2021,
      nutrients = "nitrogen",
      include_wwtp = TRUE,
      custom_wwtp_nitrogen = "nitrogen_2021.csv",
      wwtp_skip_rows = 3,      # Skip first 3 rows
      wwtp_header_row = 1      # Headers in row 1 after skipping
    )

#### Custom Column Names

    # If your columns have different names
    custom_mapping <- list(
      facility_name = "Plant_Name",
      latitude = "Lat_DD",
      longitude = "Long_DD",
      pollutant_load = "Annual_Load_kg",
      state = "State_Code"
    )

    results <- run_builtin_analysis(
      scale = "huc8",
      year = 2022,
      nutrients = "nitrogen",
      include_wwtp = TRUE,
      custom_wwtp_nitrogen = "custom_format.csv",
      wwtp_column_mapping = custom_mapping
    )

### Manual WWTP Processing

For full control, process WWTP data step by step:

    # Step 1: Load your WWTP file
    wwtp_raw <- load_user_wwtp(
      file_path = "nitrogen_2020.csv",
      nutrient = "nitrogen",
      load_units = "lbs"
    )

    # Step 2: Clean the data
    wwtp_clean <- wwtp_clean_data(wwtp_raw, "nitrogen")

    # Step 3: Classify facilities by size
    wwtp_classified <- wwtp_classify_sources(wwtp_clean, "nitrogen")

    # Step 4: Convert to spatial format
    wwtp_spatial <- wwtp_to_spatial(wwtp_classified)

    # Step 5: Load boundaries and aggregate by spatial units
    boundaries <- load_builtin_boundaries("huc8")
    wwtp_aggregated <- wwtp_aggregate_by_boundaries(
      wwtp_spatial, boundaries, "nitrogen", "huc8"
    )

    # Step 6: Integrate with agricultural data
    agri_data <- load_builtin_nugis("huc8", 2020)
    agri_processed <- agri_classify_complete(agri_data, "huc8")

    integrated <- integrate_wwtp_agricultural(
      agri_processed, wwtp_aggregated, "nitrogen",
      cropland_threshold = 1234, scale = "huc8"
    )

## Unit Conversions

### Common Conversions

    # Convert between units
    kg_loads <- c(1000, 2000, 5000)
    tons_loads <- convert_load_units(kg_loads, "kg")

    lbs_loads <- c(2000, 4000, 10000)
    tons_loads <- convert_load_units(lbs_loads, "lbs")

    print(data.frame(
      Original_kg = kg_loads,
      Converted_tons = convert_load_units(kg_loads, "kg")
    ))

### Handling P2O5 vs P

The package automatically converts P2O5 to P, but you can do it manually:

    # If you have P2O5 data, convert to P
    p2o5_values <- c(100, 200, 300)  # kg P2O5
    p_values <- p2o5_values * P2O5_TO_P  # Convert to P

    print(paste("P2O5 to P conversion factor:", P2O5_TO_P))

## Working with Different Spatial Scales

### County Data (FIPS Codes)

    # County analysis - make sure you have 5-digit FIPS codes
    county_results <- run_builtin_analysis(
      scale = "county",
      year = 2016,
      nutrients = "nitrogen"
    )

    # Your county WWTP data should have state and county info

### HUC8 Watersheds

    # HUC8 analysis - 8-digit watershed codes
    huc8_results <- run_builtin_analysis(
      scale = "huc8",
      year = 2016,
      nutrients = "nitrogen"
    )

    # Make sure HUC8 codes are 8 digits (add leading zero if needed)
    huc8_codes <- c("4110001", "4110002")  # 7 digits
    formatted_codes <- format_huc8(huc8_codes)  # Adds leading zero
    print(formatted_codes)  # "04110001", "04110002"

### HUC2 Regions

    # HUC2 analysis - 2-digit regional codes
    huc2_results <- run_builtin_analysis(
      scale = "huc2",
      year = 2016,
      nutrients = "nitrogen"
    )

## State-Specific Analysis

### Single State

    # Analyze just one state
    iowa_results <- run_state_analysis(
      state = "IA",
      scale = "county",
      year = 2016,
      nutrients = "nitrogen",
      include_wwtp = TRUE
    )

    # With custom WWTP data
    texas_results <- run_state_analysis(
      state = "TX",
      scale = "huc8",
      year = 2020,
      nutrients = "nitrogen",
      include_wwtp = TRUE,
      custom_wwtp_nitrogen = "texas_wwtp_2020.csv"
    )

### Multiple States

    # Analyze several states
    midwest_states <- c("IA", "IL", "IN", "OH")
    state_results <- list()

    for (state in midwest_states) {
      state_results[[state]] <- run_state_analysis(
        state = state,
        scale = "county",
        year = 2016,
        nutrients = "nitrogen",
        include_wwtp = TRUE
      )
    }

## Quality Control

### Validate Your Data

    # Check your results make sense
    quick_check(results)

    # Look at the classifications
    table(results$agricultural$N_class)

    # Check WWTP facility counts
    if ("wwtp" %in% names(results)) {
      print(paste("WWTP facilities:", nrow(results$wwtp$nitrogen$facility_data)))
    }

### Common Data Issues

    # Problem: Negative nutrient values
    # Solution: Check your data source and units

    # Problem: All facilities excluded
    # Solution: Check coordinate system (should be decimal degrees)

    # Problem: No WWTP facilities found
    # Solution: Check facility coordinates are in correct format

    # Problem: Classification doesn't make sense
    # Solution: Check cropland threshold and nutrient balance calculations

## Working with Multiple Years

### Time Series Analysis

    # Analyze multiple years
    years_to_analyze <- 2014:2016

    batch_results <- batch_analysis_years(
      years = years_to_analyze,
      scale = "huc8",
      nutrients = "nitrogen",
      include_wwtp = TRUE
    )

    # With custom WWTP data for some years
    custom_wwtp_files <- list(
      "2018" = "nitrogen_2018.csv",
      "2019" = "nitrogen_2019.csv",
      "2020" = "nitrogen_2020.csv"
    )

    # Process each year with custom data
    custom_results <- list()
    for (year in names(custom_wwtp_files)) {
      custom_results[[year]] <- run_builtin_analysis(
        scale = "county",
        year = as.numeric(year),
        nutrients = "nitrogen",
        include_wwtp = TRUE,
        custom_wwtp_nitrogen = custom_wwtp_files[[year]]
      )
    }

## Data Management Tips

### File Organization

    # Organize your data files
    # project_folder/
    #   â”œâ”€â”€ wwtp_data/
    #   â”‚   â”œâ”€â”€ nitrogen_2018.csv
    #   â”‚   â”œâ”€â”€ nitrogen_2019.csv
    #   â”‚   â””â”€â”€ phosphorus_2018.csv
    #   â”œâ”€â”€ results/
    #   â””â”€â”€ maps/

    # Set working directory
    setwd("project_folder")

    # Run analysis with organized files
    results <- run_builtin_analysis(
      scale = "huc8",
      year = 2018,
      nutrients = "nitrogen",
      include_wwtp = TRUE,
      custom_wwtp_nitrogen = "wwtp_data/nitrogen_2018.csv",
      output_dir = "results"
    )

### Memory Management

    # For large datasets, clear cache periodically
    clear_data_cache()

    # Check package health
    health_check()

    # Free up memory between analyses
    rm(large_results)
    gc()

## Example: Complete Custom Workflow

Here’s a complete example using custom data:

    # 1. Prepare your WWTP file (nitrogen_2021.csv)
    # Make sure it has columns: Facility Name, Latitude, Longitude, Load (kg/yr), State

    # 2. Run analysis
    results_custom <- run_builtin_analysis(
      scale = "huc8",
      year = 2021,  # Agricultural data extrapolated to 2021
      nutrients = "nitrogen",
      include_wwtp = TRUE,
      custom_wwtp_nitrogen = "nitrogen_2021.csv",
      wwtp_load_units = "kg"
    )

    # 3. Check results
    summarize_results(results_custom)
    quick_check(results_custom)

    # 4. Create maps
    nitrogen_map <- map_agricultural_classification(
      results_custom$integrated$nitrogen,
      "nitrogen", "combined_N_class",
      "2021 Nitrogen with Custom WWTP Data"
    )

    save_plot(nitrogen_map, "custom_analysis_2021.png")

    # 5. Save results
    save_spatial_data(
      results_custom$integrated$nitrogen,
      "nitrogen_2021_results.rds"
    )

### Integration Issues

    # Common integration problems

    # Problem: WWTP facilities not matching spatial units
    # Solution: Check coordinate systems and boundary files
    check_wwtp_spatial_match <- function(wwtp_data, boundaries) {

      # Convert WWTP to spatial
      wwtp_sf <- st_as_sf(wwtp_data, coords = c("Long", "Lat"), crs = 4326)
      wwtp_projected <- st_transform(wwtp_sf, st_crs(boundaries))

      # Check spatial intersection
      intersections <- st_intersects(wwtp_projected, boundaries)

      # Count facilities per spatial unit
      matches <- lengths(intersections)

      message("WWTP spatial matching summary:")
      message("  Total facilities: ", nrow(wwtp_data))
      message("  Facilities matched to boundaries: ", sum(matches > 0))
      message("  Facilities not matched: ", sum(matches == 0))

      if (sum(matches == 0) > 0) {
        unmatched <- wwtp_data[matches == 0, ]
        message("  Check coordinates for unmatched facilities")
        print(head(unmatched))
      }

      return(matches)
    }

    # Problem: Scale mismatch between data sources
    # Solution: Ensure consistent spatial scales
    verify_scale_consistency <- function(agricultural_data, wwtp_data, scale) {

      message("Scale consistency check for: ", scale)

      # Check ID formats
      if (scale == "county") {
        # FIPS codes should be 5 digits
        invalid_fips <- sum(nchar(agricultural_data$ID) != 5)
        message("  Invalid FIPS codes: ", invalid_fips)
      } else if (scale == "huc8") {
        # HUC8 codes should be 8 digits
        invalid_huc8 <- sum(nchar(agricultural_data$ID) != 8)
        message("  Invalid HUC8 codes: ", invalid_huc8)
      }

      # Check coordinate ranges
      coord_summary <- list(
        lat_range = range(wwtp_data$Lat, na.rm = TRUE),
        long_range = range(wwtp_data$Long, na.rm = TRUE)
      )

      message("  WWTP coordinate ranges:")
      message("    Latitude: ", paste(round(coord_summary$lat_range, 2), collapse = " to "))
      message("    Longitude: ", paste(round(coord_summary$long_range, 2), collapse = " to "))

      return(coord_summary)
    }

## Best Practices for Custom Data

### File Organization

    # Recommended project structure
    create_project_structure <- function(project_name) {

      # Create main directories
      dir.create(project_name, showWarnings = FALSE)
      dir.create(file.path(project_name, "data"), showWarnings = FALSE)
      dir.create(file.path(project_name, "data", "wwtp"), showWarnings = FALSE)
      dir.create(file.path(project_name, "data", "agricultural"), showWarnings = FALSE)
      dir.create(file.path(project_name, "results"), showWarnings = FALSE)
      dir.create(file.path(project_name, "maps"), showWarnings = FALSE)
      dir.create(file.path(project_name, "exports"), showWarnings = FALSE)

      # Create README
      readme_content <- paste(
        "# Manureshed Analysis Project",
        "",
        "## Data Files",
        "- data/wwtp/ - WWTP facility data files",
        "- data/agricultural/ - Custom agricultural data",
        "",
        "## Outputs",
        "- results/ - Analysis results (.rds files)",
        "- maps/ - Generated maps (.png files)",
        "- exports/ - Exported data (.csv, .shp files)",
        "",
        "## Analysis Scripts",
        "- analysis.R - Main analysis script",
        "",
        sep = "\n"
      )

      writeLines(readme_content, file.path(project_name, "README.md"))

      message("Project structure created: ", project_name)
      message("Add your data files to the appropriate folders")
    }

    # Create organized project
    # create_project_structure("my_nutrient_analysis")

### Data Documentation

    # Document your custom data sources
    document_data_sources <- function(wwtp_files = NULL, agricultural_files = NULL,
                                     output_file = "data_documentation.txt") {

      doc_content <- c(
        "DATA SOURCES DOCUMENTATION",
        "============================",
        "",
        "Analysis Date: ", as.character(Sys.Date()),
        "Package Version: ", as.character(packageVersion("manureshed")),
        ""
      )

      if (!is.null(wwtp_files)) {
        doc_content <- c(doc_content,
          "WWTP DATA FILES:",
          "----------------"
        )

        for (file in wwtp_files) {
          if (file.exists(file)) {
            file_info <- file.info(file)
            doc_content <- c(doc_content,
              paste("File:", file),
              paste("  Size:", round(file_info$size / 1024, 2), "KB"),
              paste("  Modified:", file_info$mtime),
              paste("  Rows:", nrow(read.csv(file))),
              ""
            )
          }
        }
      }

      if (!is.null(agricultural_files)) {
        doc_content <- c(doc_content,
          "AGRICULTURAL DATA FILES:",
          "------------------------"
        )

        for (file in agricultural_files) {
          if (file.exists(file)) {
            file_info <- file.info(file)
            doc_content <- c(doc_content,
              paste("File:", file),
              paste("  Size:", round(file_info$size / 1024, 2), "KB"),
              paste("  Modified:", file_info$mtime),
              paste("  Rows:", nrow(read.csv(file))),
              ""
            )
          }
        }
      }

      doc_content <- c(doc_content,
        "ANALYSIS PARAMETERS:",
        "--------------------",
        "Built-in data years: 1987-2016 (Agricultural), 2007-2016 (WWTP)",
        "Spatial scales: county, huc8, huc2",
        "Default cropland threshold: 500 ha (1,234 acres)",
        ""
      )

      writeLines(doc_content, output_file)
      message("Data documentation saved to: ", output_file)
    }

    # Use documentation function
    # document_data_sources(
    #   wwtp_files = c("data/wwtp/nitrogen_2020.csv", "data/wwtp/phosphorus_2020.csv"),
    #   agricultural_files = c("data/agricultural/custom_farm_data.csv")
    # )

### Quality Assurance Workflow

    # Complete quality assurance workflow
    quality_assurance_workflow <- function(results, data_sources = NULL) {

      qa_report <- list()
      qa_report$timestamp <- Sys.time()
      qa_report$data_sources <- data_sources

      # 1. Basic data checks
      qa_report$basic_checks <- list(
        total_spatial_units = nrow(results$agricultural),
        missing_classifications = sum(is.na(results$agricultural$N_class))
      )

      # 2. Classification distribution
      if ("N_class" %in% names(results$agricultural)) {
        n_dist <- table(results$agricultural$N_class)
        qa_report$classification_distribution <- list(
          nitrogen = n_dist,
          excluded_percentage = round(n_dist["Excluded"] / sum(n_dist) * 100, 1)
        )
      }

      # 3. WWTP integration checks
      if ("wwtp" %in% names(results)) {
        qa_report$wwtp_checks <- list()

        for (nutrient in names(results$wwtp)) {
          facilities <- results$wwtp[[nutrient]]$facility_data
          qa_report$wwtp_checks[[nutrient]] <- list(
            total_facilities = nrow(facilities),
            facilities_with_loads = sum(!is.na(facilities[[paste0(toupper(substr(nutrient, 1, 1)), "_Load_tons")]]))
          )
        }
      }

      # 4. Spatial validation
      if (inherits(results$agricultural, "sf")) {
        qa_report$spatial_checks <- list(
          invalid_geometries = sum(!st_is_valid(results$agricultural)),
          crs = st_crs(results$agricultural)$input
        )
      }

      # 5. Range checks
      if ("integrated" %in% names(results)) {
        for (nutrient in names(results$integrated)) {
          data <- results$integrated[[nutrient]]
          surplus_col <- paste0(substr(nutrient, 1, 1), "_surplus")

          if (surplus_col %in% names(data)) {
            qa_report$range_checks[[nutrient]] <- list(
              min_surplus = min(data[[surplus_col]], na.rm = TRUE),
              max_surplus = max(data[[surplus_col]], na.rm = TRUE),
              extreme_values = sum(abs(data[[surplus_col]]) > 1000, na.rm = TRUE)
            )
          }
        }
      }

      # Generate QA summary
      qa_summary <- list(
        passed = TRUE,
        warnings = character(),
        errors = character()
      )

      # Check for issues
      if (qa_report$basic_checks$missing_classifications > 0) {
        qa_summary$warnings <- c(qa_summary$warnings,
                                 paste("Missing classifications:", qa_report$basic_checks$missing_classifications))
      }

      if (qa_report$classification_distribution$excluded_percentage > 50) {
        qa_summary$warnings <- c(qa_summary$warnings,
                                 paste("High exclusion rate:", qa_report$classification_distribution$excluded_percentage, "%"))
      }

      if ("spatial_checks" %in% names(qa_report) && qa_report$spatial_checks$invalid_geometries > 0) {
        qa_summary$errors <- c(qa_summary$errors,
                               paste("Invalid geometries:", qa_report$spatial_checks$invalid_geometries))
        qa_summary$passed <- FALSE
      }

      # Print summary
      message("Quality Assurance Summary:")
      message("  Status: ", ifelse(qa_summary$passed, "PASSED", "FAILED"))

      if (length(qa_summary$warnings) > 0) {
        message("  Warnings:")
        for (warning in qa_summary$warnings) {
          message("    - ", warning)
        }
      }

      if (length(qa_summary$errors) > 0) {
        message("  Errors:")
        for (error in qa_summary$errors) {
          message("    - ", error)
        }
      }

      qa_report$summary <- qa_summary
      return(qa_report)
    }

    # Run QA workflow
    # qa_results <- quality_assurance_workflow(results_custom, "Custom 2021 WWTP data")

## Getting Help

### Common Questions

Q: What format should my WWTP data be in? A: CSV file with columns for facility name, latitude, longitude, annual load, and state. The package can auto-detect most EPA formats.

Q: What units can I use for loads?
A: “kg”, “lbs”, “pounds”, or “tons”. Specify with `wwtp_load_units`.

Q: Can I use data from other countries? A: The spatial boundaries are US-only, but you could provide your own boundaries and modify the coordinate system.

Q: How do I handle missing data? A: The package excludes facilities with missing coordinates or loads. Clean your data first for best results.

Q: My analysis is running slowly. What can I do? A: Try using a smaller spatial scale (HUC2 < HUC8 < County in terms of processing time), fewer years, or clear the data cache with `clear_data_cache()`.

Q: How do I cite the package and data? A: Use `citation_info()` to get proper citations for the package and underlying datasets.

Q: Can I analyze partial years or seasonal data? A: The package is designed for annual data. For seasonal analysis, you would need to aggregate your data to annual totals first.

Q: What if my WWTP data has different pollutant measurements? A: The package expects total nitrogen and total phosphorus loads. If you have other forms (NO3, NH4, PO4), you’ll need to convert to total N and P first.

### Function Help

    # Get help on specific functions
    ?load_user_wwtp
    ?run_builtin_analysis
    ?wwtp_clean_data
    ?validate_columns

    # Check what data is available
    check_builtin_data()
    list_available_years()

    # Package health check
    health_check()

    # Test data download connection
    test_osf_connection()

### Getting More Help

- Check the other vignettes: `vignette("getting-started")`, `vignette("visualization-guide")`, `vignette("advanced-features")`
- Look at function documentation: `?function_name`
- Check the package website or GitHub repository for examples
- Use `quick_check()` to validate your results
- Run `health_check()` if you encounter installation or data loading issues

### Reporting Issues

If you encounter bugs or have feature requests:

1. Check that your data format matches the expected format
2. Run `health_check()` to verify package installation
3. Try with built-in data first to isolate the issue
4. Document the error message and steps to reproduce
5. Check the package documentation and vignettes
6. Report issues with a minimal reproducible example

The `manureshed` package is designed to work with your data as easily as possible. Start with the built-in examples, then gradually add your own data as needed. The key is to ensure your data is properly formatted and validated before running the analysis.