Type:	Package
Title:	Spatiotemporal Nutrient Balance Analysis Across Agricultural and Municipal Systems
Version:	0.1.0
Maintainer:	Olatunde D. Akanbi <olatunde.akanbi@case.edu>
Description:	A comprehensive framework for analyzing agricultural nutrient balances across multiple spatial scales (county, 'HUC8', 'HUC2') with integration of wastewater treatment plant ('WWTP') effluent loads for both nitrogen and phosphorus. Supports classification of spatial units as nutrient sources, sinks, or balanced areas based on agricultural surplus and deficit calculations. Includes visualization tools, spatial transition probability analysis, and nutrient flow network mapping. Built-in datasets include agricultural nutrient balance data from the Nutrient Use Geographic Information System ('NuGIS'; The Fertilizer Institute and Plant Nutrition Canada, 1987-2016) https://nugis.tfi.org/tabular_data/ and U.S. Environmental Protection Agency ('EPA') wastewater discharge data from the 'ECHO' Discharge Monitoring Report ('DMR') Loading Tool (2007-2016) https://echo.epa.gov/trends/loading-tool/water-pollution-search. Data are downloaded on demand from the Open Science Framework ('OSF') repository to minimize package size while maintaining full functionality. The integrated 'manureshed' framework methodology is described in Akanbi et al. (2025) <doi:10.1016/j.resconrec.2025.108697>. Designed for nutrient management planning, environmental analysis, and circular economy research at watershed/administrative to national scales. This material is based upon financial support by the National Science Foundation, EEC Division of Engineering Education and Centers, NSF Engineering Research Center for Advancing Sustainable and Distributed Fertilizer Production (CASFER), NSF 20-553 Gen-4 Engineering Research Centers award 2133576. We thank Dr. Robert D. Sabo (U.S. Environmental Protection Agency) for his valuable contributions to the conceptual development and review of this work.
License:	MIT + file LICENSE
URL:	https://osf.io/g39xa/
Encoding:	UTF-8
RoxygenNote:	7.3.3
Depends:	R (≥ 4.0.0)
Imports:	dplyr (≥ 1.0.0), sf (≥ 1.0.0), ggplot2 (≥ 3.3.0), tidyr (≥ 1.1.0), jsonlite (≥ 1.7.0), rlang (≥ 0.4.0), magrittr, scales (≥ 1.1.0), igraph (≥ 1.2.0), tigris (≥ 1.5.0), stats, utils, tools
Suggests:	nhdplusTools (≥ 0.5.0), RColorBrewer (≥ 1.1.0), cowplot (≥ 1.1.0), ggpubr (≥ 0.4.0), viridis (≥ 0.6.0), testthat (≥ 3.0.0), knitr, progress, rmarkdown
SystemRequirements:	GDAL (>= 2.0.1), GEOS (>= 3.4.0), PROJ (>= 4.8.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-12-17 18:15:16 UTC; oda10
Author:	Olatunde D. Akanbi [aut, cre, cph], Vibha Mandayam [aut], Atharva Gupta [aut], K. Colton Flynn [aut], Jeffrey Yarus [aut], Erika I. Barcelos [aut, cph], Roger H. French [aut, cph]
Repository:	CRAN
Date/Publication:	2025-12-22 18:10:02 UTC

manureshed: Manureshed Analysis with WWTP Integration

Description

This package provides comprehensive tools for analyzing agricultural nutrient balances at multiple spatial scales with optional integration of wastewater treatment plant (WWTP) nutrient loads for both nitrogen and phosphorus.

Details

All datasets are downloaded on-demand from OSF repository to minimize package size while maintaining full functionality.

Main Functions

Data Loading and Management:

• load_builtin_nugis: Load NuGIS data from OSF

• load_builtin_boundaries: Load spatial boundaries from OSF

• load_builtin_wwtp: Load WWTP data from OSF (2016)

• check_builtin_data: Check available datasets and cache status

• download_all_data: Download all datasets at once

• download_osf_data: Download specific dataset

• clear_data_cache: Clear cached datasets

High-Level Workflows:

• run_builtin_analysis: Complete end-to-end analysis workflow

• quick_analysis: Analysis with automatic visualizations

• batch_analysis_years: Multi-year analysis workflow

Agricultural Classification:

• agri_process_nugis: Process and standardize NuGIS data

• agri_classify_nitrogen: Classify nitrogen balance

• agri_classify_phosphorus: Classify phosphorus balance

• agri_classify_complete: Complete agricultural pipeline

WWTP Processing:

• load_user_wwtp: Load custom WWTP data with flexible formatting

• wwtp_clean_data: Clean and filter WWTP data

• wwtp_classify_sources: Classify WWTP facilities by load size

• convert_load_units: Handle different load units (kg, lbs, tons)

Data Integration:

• integrate_wwtp_agricultural: Combine WWTP and agricultural data

• integrate_complete: Complete integration pipeline

• add_texas_huc8: Add Texas HUC8 supplemental data

Visualization and Mapping:

• map_agricultural_classification: Map nutrient classifications

• map_wwtp_points: Map WWTP facility locations

• map_wwtp_influence: Map WWTP influence/proportion

• get_state_boundaries: Get US state boundaries for mapping

Spatial Analysis:

• calculate_transition_probabilities: Spatial transition analysis

• create_network_plot: Network visualization of transitions

• add_centroid_coordinates: Calculate spatial centroids

Comparison Analysis:

• create_classification_summary: Before/after comparison summaries

• plot_before_after_comparison: Comparison bar plots

• plot_impact_ratios: Impact ratio visualizations

• plot_absolute_changes: Absolute change plots

Utility Functions:

• get_nutrient_colors: Get color schemes for nutrients

• clean_category_names: Clean classification names for display

• format_huc8: Format HUC8 codes with leading zeros

• get_cropland_threshold: Calculate exclusion thresholds

Spatial Scales

The package supports analysis at three spatial scales:

• County: US county boundaries (3,000+ units)

• HUC8: 8-digit Hydrologic Unit Code watersheds (2,000+ units)

• HUC2: 2-digit Hydrologic Unit Code regions (18 units)

Nutrients Supported

The package supports analysis for both major nutrients with appropriate methodologies:

• Nitrogen: Uses 0.5 availability factor for manure nitrogen in calculations

• Phosphorus: Direct calculation without availability factor

Users can analyze one nutrient, both nutrients, or different combinations in the same workflow: nutrients = c("nitrogen", "phosphorus")

Classification System

Spatial units are classified into five categories based on nutrient balance:

• Source: Net nutrient surplus available for export

• Sink Deficit: Total deficit requiring nutrient imports

• Sink Fertilizer: Fertilizer surplus available for manure import

• Within Watershed/County: Balanced for internal nutrient transfers

• Excluded: Insufficient cropland for meaningful analysis (<500 ha equivalent)

Data Sources

The package provides access to comprehensive built-in datasets:

NuGIS Data:

• County-level data (1987 - 2016)

• HUC8 watershed data (1987 - 2016)

• HUC2 regional data (1987 - 2016)

• All nutrient balance components (manure, fertilizer, removal, fixation)

Spatial Boundaries:

• US county boundaries (CONUS)

• HUC8 watershed boundaries

• HUC2 regional boundaries

• All in Albers Equal Area Conic projection (EPSG:5070)

WWTP Data:

• Nitrogen discharge data (2007 - 2016)

• Phosphorus discharge data (2007 - 2016)

• Pre-processed and classification-ready

• Includes facility metadata and spatial coordinates

Supplemental Data:

• Texas HUC8 data (automatically integrated for HUC8 analyses)

• Texas spatial boundaries

OSF Data Repository

All datasets are hosted on OSF and downloaded on-demand:

• Repository: https://osf.io/g39xa/

• Automatic caching: Data downloaded once and reused

• Flexible loading: Load only the data you need

• Version control: Permanent DOI for reproducibility

• Size efficiency: Package <1MB, full datasets ~25MB

WWTP Data Flexibility

The package handles varying EPA WWTP data formats across different years:

• Unit Conversion: Automatic conversion between kg, lbs, pounds, and tons

• Column Mapping: Flexible mapping to handle EPA naming changes

• Header Detection: Support for different header row positions

• Built-in 2016: Ready-to-use cleaned data for immediate analysis

• Custom Integration: Easy integration of user data for other years

Workflow Examples

# Check what data is available
check_builtin_data()

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

# Basic analysis using built-in data - any year 2007-2016
results <- run_builtin_analysis(
  scale = "huc8",
  year = 2012,  # Any year 2007-2016 now supported
  nutrients = c("nitrogen", "phosphorus"),
  include_wwtp = TRUE
)

wwtp_n_2010 <- load_builtin_wwtp("nitrogen", year = 2010)
wwtp_p_2015 <- load_builtin_wwtp("phosphorus", year = 2015)


# Quick analysis with automatic visualizations
viz_results <- quick_analysis(
  scale = "county",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

# Historical analysis without WWTP
historical <- run_builtin_analysis(
  scale = "huc8",
  year = 2010,
  nutrients = c("nitrogen", "phosphorus"),
  include_wwtp = FALSE
)

# Load specific datasets manually
county_2016 <- load_builtin_nugis("county", 2016)
boundaries <- load_builtin_boundaries("county")
wwtp_n <- load_builtin_wwtp("nitrogen")

Analysis Outputs

The package generates comprehensive outputs for each nutrient analyzed:

• Spatial Data: Classification results as sf objects

• Maps: Classification, influence, and facility maps

• Networks: Transition probability visualizations

• Comparisons: Before/after WWTP integration analysis

• Data Files: CSV centroids, RDS spatial data

• Metadata: Analysis parameters and processing information

Performance and Scalability

• Optimized for CONUS-scale analysis (1000s of spatial units)

• Memory-efficient spatial operations

• On-demand data loading reduces memory footprint

• Progress reporting for long-running analyses

• Automatic garbage collection for memory management

Author(s)

Maintainer: Olatunde D. Akanbi olatunde.akanbi@case.edu (ORCID) [copyright holder]

Authors:

• Vibha Mandayam (ORCID)

• Atharva Gupta (ORCID)

• K. Colton Flynn (ORCID)

• Jeffrey Yarus (ORCID)

• Erika I. Barcelos erika.barcelos@case.edu (ORCID) [copyright holder]

• Roger H. French roger.french@case.edu (ORCID) [copyright holder]

See Also

Useful links:

• https://osf.io/g39xa/

Package Startup Message for OSF Data Approach

Description

Displays informative startup message when package is loaded

Usage

.onAttach(libname, pkgname)

Arguments

Package Version and Build Information

Description

Internal function to provide package build information

Usage

.package_info()

Value

List with package metadata

CONUS States

Description

Vector of Continental United States state abbreviations

Usage

CONUS_STATES

Format

An object of class character of length 49.

Conversion Factor: Kilograms to US Tons

Description

Conversion Factor: Kilograms to US Tons

Usage

KG_TO_TONS

Format

An object of class numeric of length 1.

Conversion Factor: Pounds to Kilograms

Description

Conversion Factor: Pounds to Kilograms

Usage

LBS_TO_KG

Format

An object of class numeric of length 1.

Conversion Factor: Pounds to US Tons

Description

Conversion Factor: Pounds to US Tons

Usage

LBS_TO_TONS

Format

An object of class numeric of length 1.

Standard CRS for Manureshed Analysis

Description

Albers Equal Area Conic projection (EPSG:5070)

Usage

MANURESHED_CRS

Format

An object of class numeric of length 1.

Conversion Factor: P2O5 to P

Description

Conversion Factor: P2O5 to P

Usage

P2O5_TO_P

Format

An object of class numeric of length 1.

Add Centroid Coordinates to Spatial Data

Description

Calculate centroid coordinates for spatial units

Usage

add_centroid_coordinates(spatial_data)

Arguments

Value

Data frame with centroid coordinates added

Add Texas HUC8 Data (Updated for OSF)

Description

Add manually supplied Texas HUC8 data for missing watersheds Uses OSF data loading instead of built-in data

Usage

add_texas_huc8(huc8_data, year = 2016, cropland_threshold, verbose = TRUE)

Arguments

Value

sf object with Texas data added

Complete Agricultural Classification Pipeline

Description

Run complete agricultural nutrient classification analysis for both N and P

Usage

agri_classify_complete(
  nugis_data,
  scale,
  cropland_threshold = NULL,
  county_data = NULL
)

Arguments

Value

Data frame with complete agricultural classifications for both nutrients

Complete Agricultural Classification Pipeline with Custom Efficiency Factors

Description

Run complete agricultural nutrient classification analysis for both N and P with user-specified efficiency factors for sensitivity analysis.

Usage

agri_classify_complete_custom(
  nugis_data,
  scale,
  cropland_threshold = NULL,
  county_data = NULL,
  n_efficiency = 0.5,
  p_efficiency = 1
)

Arguments

Value

Data frame with complete agricultural classifications for both nutrients

Examples

# Load county data
nugis_data <- load_builtin_nugis("county", 2016)

# Standard analysis
results_standard <- agri_classify_complete_custom(
  nugis_data, "county"
)

# Sensitivity analysis with varied nitrogen efficiency
results_high_n <- agri_classify_complete_custom(
  nugis_data, "county",
  n_efficiency = 0.7
)

# Analysis with both custom efficiencies
results_custom <- agri_classify_complete_custom(
  nugis_data, "county",
  n_efficiency = 0.6,
  p_efficiency = 0.9
)

Classify Agricultural Nitrogen Status

Description

Classify spatial units based on nitrogen balance using standard 0.5 efficiency factor

Usage

agri_classify_nitrogen(data, cropland_threshold, scale = "huc8")

Arguments

Value

Data frame with nitrogen classification added

Classify Agricultural Nitrogen Status with Custom Efficiency Factor

Description

Classify spatial units based on nitrogen balance with user-specified efficiency factor. This function allows sensitivity analysis by varying the nitrogen efficiency assumption. The default value of 0.5 represents typical losses during nutrient cycling, uptake, and application, but regional conditions may warrant different values.

Usage

agri_classify_nitrogen_custom(
  data,
  cropland_threshold,
  scale = "huc8",
  n_efficiency = 0.5
)

Arguments

Value

Data frame with nitrogen classification added

Examples

# Load and process data first
nugis_data <- load_builtin_nugis("county", 2016)
processed_data <- agri_process_nugis(nugis_data, "county")
cropland_threshold <- 500 * 2.47105  # 500 ha in acres

# Standard analysis with default 0.5 efficiency
results_default <- agri_classify_nitrogen_custom(
  processed_data, cropland_threshold = cropland_threshold, n_efficiency = 0.5
)

# Sensitivity analysis with higher efficiency (e.g., improved management)
results_high <- agri_classify_nitrogen_custom(
  processed_data, cropland_threshold = cropland_threshold, n_efficiency = 0.7
)

# Conservative analysis with lower efficiency
results_low <- agri_classify_nitrogen_custom(
  processed_data, cropland_threshold = cropland_threshold, n_efficiency = 0.3
)

# Compare classification changes across efficiency scenarios
table(results_default$N_class)
table(results_high$N_class)
table(results_low$N_class)

Classify Agricultural Phosphorus Status

Description

Classify spatial units based on phosphorus balance (no efficiency factor for P)

Usage

agri_classify_phosphorus(data, cropland_threshold, scale = "huc8")

Arguments

Value

Data frame with phosphorus classification added

Classify Agricultural Phosphorus Status with Custom Efficiency Factor

Description

Classify spatial units based on phosphorus balance with user-specified efficiency factor. While standard phosphorus classification uses 100\ allows sensitivity analysis by varying the phosphorus efficiency assumption for different management scenarios or application methods.

Usage

agri_classify_phosphorus_custom(
  data,
  cropland_threshold,
  scale = "huc8",
  p_efficiency = 1
)

Arguments

Value

Data frame with phosphorus classification added

Examples

# Load and process data first
nugis_data <- load_builtin_nugis("county", 2016)
processed_data <- agri_process_nugis(nugis_data, "county")
cropland_threshold <- 500 * 2.47105  # 500 ha in acres

# Standard analysis with default 1.0 efficiency (100%)
results_default <- agri_classify_phosphorus_custom(
  processed_data, cropland_threshold = cropland_threshold, p_efficiency = 1.0
)

# Analysis with reduced efficiency (e.g., accounting for losses)
results_reduced <- agri_classify_phosphorus_custom(
  processed_data, cropland_threshold = cropland_threshold, p_efficiency = 0.8
)

# Conservative analysis with lower efficiency
results_conservative <- agri_classify_phosphorus_custom(
  processed_data, cropland_threshold = cropland_threshold, p_efficiency = 0.6
)

# Compare classification changes across efficiency scenarios
table(results_default$P_class)
table(results_reduced$P_class)
table(results_conservative$P_class)

Process NuGIS Data for Manureshed Analysis

Description

Clean and standardize NuGIS data for agricultural nutrient analysis

Usage

agri_process_nugis(nugis_data, scale)

Arguments

Value

Data frame with standardized columns for analysis

Enhanced Batch Analysis with Full Visualizations

Description

Run batch analysis with comprehensive visualization output for each year

Usage

batch_analysis_enhanced(
  years,
  scale = "huc8",
  nutrients = c("nitrogen", "phosphorus"),
  include_wwtp = TRUE,
  output_base_dir = tempdir(),
  create_all_visualizations = TRUE,
  create_comparative_plots = TRUE,
  show_progress = TRUE,
  verbose = TRUE,
  ...
)

Arguments

Value

List of results with comprehensive outputs

Examples

## Not run: 
# This function is computationally intensive
# See vignette("advanced-features") for examples
results <- batch_analysis_enhanced(years = 2015:2016)

## End(Not run)

Batch Analysis with Parallel Processing

Description

Run batch analysis using multiple cores for faster processing

Usage

batch_analysis_parallel(years, n_cores = NULL, ...)

Arguments

Value

List of results

Examples

results <- batch_analysis_parallel(
  years = 2015:2016,  # Use valid years only
  n_cores = 2,        # Max 2 cores for CRAN
  scale = "county",   # Use county for faster processing
  nutrients = "nitrogen"
)

Batch Analysis Across Multiple Years

Description

Run manureshed analysis across multiple years with consistent parameters

Usage

batch_analysis_years(
  years,
  scale = "huc8",
  nutrients = c("nitrogen", "phosphorus"),
  include_wwtp = TRUE,
  output_base_dir = tempdir(),
  create_comparative_plots = TRUE,
  verbose = TRUE,
  ...
)

Arguments

Value

List of results for each year

Examples

# Analyze trends with WWTP for subset of supported range
batch_results <- batch_analysis_years(
  years = 2010:2012,  # Use smaller range for examples
  scale = "huc8",
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

# Historical analysis without WWTP
historical_results <- batch_analysis_years(
  years = 1990:1992,  # Use smaller range
  scale = "county",
  nutrients = c("nitrogen", "phosphorus"),
  include_wwtp = FALSE
)

# Mixed analysis: some years with WWTP, some without
mixed_results <- batch_analysis_years(
  years = c(2005, 2010, 2015),  # 2010,2015 will have WWTP
  scale = "huc8",
  nutrients = "nitrogen",
  include_wwtp = TRUE  # Will only apply to 2010,2015
)

Benchmark Analysis Performance

Description

Test analysis speed and memory usage

Usage

benchmark_analysis(
  scale = "huc8",
  year = 2016,
  nutrients = "nitrogen",
  n_runs = 3,
  include_wwtp = TRUE
)

Arguments

Value

List with timing statistics and memory usage

Examples

# Benchmark HUC8 analysis - use smaller scale for faster testing
benchmark <- benchmark_analysis(
  scale = "county",  # Use county for faster testing
  year = 2016,
  nutrients = "nitrogen",
  n_runs = 2  # Reduce runs for faster testing
)
print(benchmark)

Calculate Cropland Threshold for Exclusion

Description

Calculate cropland threshold for excluding small agricultural areas Uses county 500ha baseline to determine percentile for other scales

Usage

calculate_cropland_threshold(
  county_data,
  target_data,
  county_cropland_col,
  target_cropland_col,
  baseline_ha = 500
)

Arguments

Value

Numeric. Threshold value for target scale

Calculate Spatial Transition Probabilities

Description

Calculate transition probabilities between adjacent spatial units

Usage

calculate_transition_probabilities(
  data,
  class_column,
  longitude_col = "longitude",
  latitude_col = "latitude"
)

Arguments

Value

Data frame with transition probabilities as percentages

Check Data Availability from OSF

Description

Check Data Availability from OSF

Usage

check_builtin_data(verbose = FALSE)

Arguments

Value

List showing available datasets and cache status

Display Package Citation Information

Description

Provides citation information for the package and data sources. Prints formatted citation text to the console for the manureshed package, the underlying research methodology paper (Akanbi et al., 2026), and the primary data sources (NuGIS agricultural data and EPA WWTP discharge data). The function is designed for users to easily obtain proper citations for publications and reports.

Usage

citation_info()

Details

This function takes no arguments. It prints citation information directly to the console using message() functions, which can be suppressed with suppressMessages() if needed.

Value

No return value, called for side effects. The function prints citation information to the console including:

• Package citation with version and OSF repository

• Research methodology paper citation

• NuGIS data source attribution

• EPA WWTP data source attribution

• Contact information for data sources

Note

This function requires no arguments and can be called simply as citation_info().

See Also

check_builtin_data for data availability, health_check for package diagnostics

Examples

# Display citation information
citation_info()

Clean Category Names for Display

Description

Clean Category Names for Display

Usage

clean_category_names(names)

Arguments

Value

Character vector of cleaned names

Clean Text Data

Description

Remove extra quotes and whitespace from text fields

Usage

clean_text(text)

Arguments

Value

Character vector of cleaned text

Clear Data Cache

Description

Remove cached datasets to free up disk space

Usage

clear_data_cache(confirm = TRUE, verbose = TRUE)

Arguments

Value

Logical. TRUE if successful

Compare Two Analysis Results

Description

Compare classifications between two analysis results

Usage

compare_analyses(results1, results2, nutrient = "nitrogen")

Arguments

Value

Data frame with comparison

Examples

results_2010 <- run_builtin_analysis(scale = "county", year = 2010)
results_2016 <- run_builtin_analysis(scale = "county", year = 2016)
comparison <- compare_analyses(results_2010, results_2016, "nitrogen")

Convert Load Units to Tons

Description

Convert loads from various units to US tons

Usage

convert_load_units(load_values, from_unit)

Arguments

Value

Numeric vector of loads in US tons

Examples

# Convert from kilograms to tons
kg_loads <- c(1000, 2000, 3000)
tons_loads <- convert_load_units(kg_loads, "kg")

# Convert from pounds to tons
lbs_loads <- c(5000, 10000, 15000)
tons_loads <- convert_load_units(lbs_loads, "lbs")

Create Analysis Report

Description

Generate comprehensive HTML or PDF report of analysis results

Usage

create_analysis_report(
  results,
  output_path,
  format = "html",
  title = "Manureshed Analysis Report",
  include_maps = TRUE
)

Arguments

Value

Character. Path to generated report

Examples

# Generate HTML report - use tempdir to avoid check directory pollution
results <- run_builtin_analysis(scale = "county", year = 2016)
report_path <- file.path(tempdir(), "analysis_report.html")
create_analysis_report(results, report_path)

Create Batch Summary Report

Description

Generate comprehensive summary report for batch analysis

Usage

create_batch_summary_report(
  batch_results,
  years,
  nutrients,
  output_dir,
  verbose
)

Arguments

Value

Summary data frame

Create Classification Summary Table

Description

Create summary table of classification counts for both nutrients

Usage

create_classification_summary(data, agricultural_col, combined_col)

Arguments

Value

Data frame with classification counts and changes

Create Classification Trend Plot

Description

Create Classification Trend Plot

Usage

create_classification_trend_plot(trend_data, nutrient)

Arguments

Value

ggplot object

Create Comprehensive Batch Comparisons

Description

Generate detailed comparative visualizations across years

Usage

create_comprehensive_comparisons(batch_results, nutrients, output_dir, verbose)

Arguments

Value

List of created comparison files

Create Network Plot from Transition Probabilities

Description

Create network visualization of spatial transition probabilities

Usage

create_network_plot(
  transition_df,
  nutrient,
  analysis_type,
  output_path,
  highlight_transitions = TRUE
)

Arguments

Value

NULL (saves plot to file)

Create Stacked Area Plot

Description

Create Stacked Area Plot

Usage

create_stacked_area_plot(trend_data, nutrient)

Arguments

Value

ggplot object

Create Year-over-Year Comparison Plots

Description

Internal function to create comparative visualizations across years

Usage

create_year_comparison_plots(year_summaries, nutrients, output_dir, verbose)

Arguments

Value

List of created plot files

Create Year-over-Year Change Plot

Description

Create Year-over-Year Change Plot

Usage

create_yoy_change_plot(trend_data, nutrient)

Arguments

Value

ggplot object

NuGIS and EPA WWTP Data Sources

Description

This package uses two primary data sources:

NuGIS Agricultural Data

The Nutrient Use Geographic Information System (NuGIS) presents cropland nutrient balances for the conterminous United States from 1987-2016.

Source: The Fertilizer Institute (TFI) and Plant Nutrition Canada (PNC)

Website: https://nugis.tfi.org/tabular_data

Contact: nugis@tfi.org

Components:

• County-level crop and livestock data from USDA Census of Agriculture

• Fertilizer use data from AAPFCO

• Geospatial nutrient balance estimates

• Available for counties, HUC8, and HUC2 watersheds

Data Processing: The manureshed package uses cleaned versions of NuGIS data with resolved metadata issues and enhanced spatial integration, as detailed in the manureshed methodology paper (Akanbi et al., 2026).

EPA WWTP Data

Source: U.S. Environmental Protection Agency

System: Discharge Monitoring Report (DMR) Loading Tool via ECHO

Website: https://echo.epa.gov/trends/loading-tool/water-pollution-search

Data Years: 2007-2016 (nitrogen and phosphorus loads)

License: Public domain (U.S. Government work)

Data Attribution

When using this package, please cite both the package, methodology paper, and the underlying data sources. Use citation_info() to display full citation information.

Download All Datasets from OSF

Description

Convenience function to download all available datasets from OSF

Usage

download_all_data(force_download = FALSE, verbose = TRUE)

Arguments

Value

Logical. TRUE if all downloads successful

Download and Cache Data from OSF

Description

Download built-in datasets from OSF repository using Files API

Usage

download_osf_data(
  dataset_name,
  force_download = FALSE,
  cache_dir = NULL,
  verbose = TRUE
)

Arguments

Value

Path to cached data file

Export Results for GIS Applications

Description

Export spatial results in GIS-ready formats

Usage

export_for_gis(
  results,
  output_dir = file.path(tempdir(), "gis_export"),
  formats = c("shapefile", "geojson")
)

Arguments

Value

List of created files

Examples

# Use tempdir to avoid polluting check directory
results <- run_builtin_analysis(scale = "county", year = 2016)
output_dir <- file.path(tempdir(), "gis_outputs")
gis_files <- export_for_gis(results, output_dir)

Export for Policy Briefs

Description

Create simplified outputs for policy makers

Usage

export_for_policy(results, output_dir = file.path(tempdir(), "policy_export"))

Arguments

Value

List of created files

Export for Publication

Description

Create high-resolution outputs suitable for publication

Usage

export_for_publication(
  results,
  output_dir = file.path(tempdir(), "publication_export"),
  dpi = 600
)

Arguments

Value

List of created files

Extract Trend Data from Batch Results

Description

Extract Trend Data from Batch Results

Usage

extract_trend_data(batch_results, nutrient)

Arguments

Value

Data frame with trend data

Filter Data by State

Description

Filter spatial data to a specific state

Usage

filter_by_state(data, state, scale, boundaries = NULL)

Arguments

Value

Filtered data for the specified state

Format HUC8 Codes

Description

Add leading zeros to 7-digit HUC8 codes to make them 8-digit

Usage

format_huc8(huc_codes)

Arguments

Value

Character vector of properly formatted 8-digit HUC codes

Generate Report Content

Description

Internal function to generate R Markdown content for analysis report

Usage

generate_report_content(results, title, include_maps)

Arguments

Value

Character vector. R Markdown content

Get Cropland Threshold by Scale

Description

Get appropriate cropland threshold based on spatial scale

Usage

get_cropland_threshold(
  scale,
  county_data = NULL,
  target_data = NULL,
  baseline_ha = 500
)

Arguments

Value

Numeric. Threshold value

Default Color Schemes for Nutrient Classifications

Description

Default Color Schemes for Nutrient Classifications

Usage

get_nutrient_colors(nutrient = "nitrogen")

Arguments

Value

Named vector of colors for classification categories

Get State Boundaries for Mapping

Description

Get US state boundaries excluding non-CONUS states

Usage

get_state_boundaries()

Value

sf object with state boundaries

Get State FIPS Code

Description

Convert state abbreviation to FIPS code

Usage

get_state_fips(state_abbr)

Arguments

Value

Character. Two-digit state FIPS code

Check Package Health and Dependencies

Description

Diagnostic function to check package installation and dependencies

Usage

health_check(verbose = FALSE)

Arguments

Value

Logical. TRUE if all checks pass

Examples

# Quick health check
health_check()

# Detailed diagnostic information
health_check(verbose = TRUE)

Complete Integration Pipeline (Updated)

Description

Run complete integration of WWTP and agricultural data for both nutrients

Usage

integrate_complete(
  agri_data,
  wwtp_nitrogen_aggregated,
  wwtp_phosphorus_aggregated,
  cropland_threshold,
  scale = "huc8",
  add_texas = FALSE,
  year = 2016
)

Arguments

Value

List with integrated nitrogen and phosphorus data

Integrate WWTP Data with Agricultural Classifications (Updated)

Description

Combine WWTP loads with agricultural nutrient balance classifications

Usage

integrate_wwtp_agricultural(
  agri_data,
  wwtp_aggregated,
  nutrient,
  cropland_threshold,
  scale = "huc8"
)

Arguments

Value

Data frame with combined WWTP + agricultural classifications

List Available Built-in Years

Description

Show available years for each data type

Usage

list_available_years(scale = NULL)

Arguments

Value

Data frame with available years by data type

Load Built-in Spatial Boundaries from OSF

Description

Load built-in spatial boundary data for specified scale from OSF repository

Usage

load_builtin_boundaries(scale, force_download = FALSE, verbose = TRUE)

Arguments

Value

sf object with spatial boundaries

Load Built-in NuGIS Data from OSF

Description

Load built-in NuGIS data from OSF repository for specified year and spatial scale. Data includes all years from 1987 through 2016.

Usage

load_builtin_nugis(scale, year = 2016, force_download = FALSE, verbose = TRUE)

Arguments

Value

Data frame of NuGIS data for specified scale and year

Examples

# Load county data for 2016
county_2016 <- load_builtin_nugis("county", 2016)

# Load HUC8 data for 2010
huc8_2010 <- load_builtin_nugis("huc8", 2010)

# Load county data for 2010, force fresh download
county_2010 <- load_builtin_nugis("county", 2010, force_download = TRUE)

Load Built-in WWTP Data from OSF

Description

Load built-in WWTP data for specified year from OSF repository (2007-2016 available)

Load built-in WWTP data for specified year from OSF repository (2007-2016 available)

Usage

load_builtin_wwtp(
  nutrient,
  year = 2016,
  force_download = FALSE,
  verbose = TRUE
)

load_builtin_wwtp(
  nutrient,
  year = 2016,
  force_download = FALSE,
  verbose = TRUE
)

Arguments

Value

Data frame with cleaned WWTP data for specified year

Data frame with cleaned WWTP data for specified year

Examples

# Load WWTP data for different years (2007-2016 available)
wwtp_n_2016 <- load_builtin_wwtp("nitrogen", 2016)
wwtp_n_2012 <- load_builtin_wwtp("nitrogen", 2012)
wwtp_n_2007 <- load_builtin_wwtp("nitrogen", 2007)

# Load phosphorus data
wwtp_p_2015 <- load_builtin_wwtp("phosphorus", 2015)
wwtp_p_2010 <- load_builtin_wwtp("phosphorus", 2010)

# Force re-download
wwtp_fresh <- load_builtin_wwtp("nitrogen", 2014, force_download = TRUE)

Load Texas HUC8 Boundaries from OSF

Description

Load Texas HUC8 spatial boundaries from OSF repository

Usage

load_texas_huc8_boundaries(verbose = FALSE)

Arguments

Value

sf object with Texas HUC8 boundaries

Load Texas HUC8 Data from OSF

Description

Load Texas HUC8 supplemental data from OSF repository

Usage

load_texas_huc8_data(year = 2016, verbose = FALSE)

Arguments

Value

Data frame with Texas HUC8 data for specified year

Load User WWTP Data

Description

Load and standardize user-provided WWTP data with flexible formatting

Load and standardize user-provided WWTP data with flexible formatting

Usage

load_user_wwtp(
  file_path,
  nutrient,
  column_mapping = NULL,
  skip_rows = 0,
  header_row = 1,
  load_units = "kg"
)

load_user_wwtp(
  file_path,
  nutrient,
  column_mapping = NULL,
  skip_rows = 0,
  header_row = 1,
  load_units = "kg"
)

Arguments

Value

Data frame with standardized WWTP data

Data frame with standardized WWTP data

Examples

## Not run: 
# Standard EPA format but will not run because data needs to be supplied as custom
# Load custom WWTP data (for years outside 2007-2016)
wwtp_data <- load_user_wwtp("nitrogen_2020.csv", "nitrogen")

# For years 2007-2016, consider using built-in data:
# wwtp_builtin <- load_builtin_wwtp("nitrogen", 2015)

# Custom format with different units
wwtp_data <- load_user_wwtp("custom_wwtp.csv", "phosphorus",
                          load_units = "lbs", skip_rows = 3)


# Custom column mapping
custom_map <- list(facility_name = "Plant_Name",
                  pollutant_load = "Load_lbs_per_year")
wwtp_data <- load_user_wwtp("custom.csv", "nitrogen", custom_map)

## End(Not run)
## Not run: 

# Standard EPA format
wwtp_data <- load_user_wwtp("nitrogen_2020.csv", "nitrogen")

# Custom format with different units
wwtp_data <- load_user_wwtp("custom_wwtp.csv", "phosphorus",
                          load_units = "lbs", skip_rows = 3)

# Custom column mapping
custom_map <- list(facility_name = "Plant_Name",
                  pollutant_load = "Load_lbs_per_year")
wwtp_data <- load_user_wwtp("custom.csv", "nitrogen", custom_map)

## End(Not run)

Create Agricultural Classification Map

Description

Create map showing agricultural nutrient classifications

Usage

map_agricultural_classification(data, nutrient, classification_col, title)

Arguments

Value

ggplot object

Map WWTP Column Names to Standard Format

Description

Create mapping between EPA WWTP data column names and standardized format. Handles various EPA data formats across different years.

Usage

map_wwtp_columns(raw_data, nutrient, custom_mapping = NULL)

Arguments

Value

Named list with column mappings

Examples

## Not run: 
mapping <- map_wwtp_columns(raw_wwtp_data, "nitrogen")

# Custom mapping for different format
custom_map <- list(facility_name = "Plant_Name",
                  pollutant_load = "Annual_Load_kg")
mapping <- map_wwtp_columns(raw_data, "nitrogen", custom_map)

## End(Not run)

Create WWTP Influence Map

Description

Create map showing WWTP contribution as proportion of total nutrient load

Usage

map_wwtp_influence(data, nutrient, title)

Arguments

Value

ggplot object

Create WWTP Point Map

Description

Create map showing WWTP locations classified by load size

Usage

map_wwtp_points(wwtp_sf, nutrient, title)

Arguments

Value

ggplot object

Create Absolute Change Plot

Description

Create plot showing absolute changes in classification counts

Usage

plot_absolute_changes(data, title)

Arguments

Value

ggplot object

Create Before/After Comparison Plot

Description

Create side-by-side comparison of agricultural vs WWTP+agricultural classifications

Usage

plot_before_after_comparison(data, nutrient, title)

Arguments

Value

ggplot object

Create Impact Ratio Plot

Description

Create plot showing impact of WWTP addition as ratios

Usage

plot_impact_ratios(data, title)

Arguments

Value

ggplot object

Quick Analysis with Visualization

Description

Run analysis and automatically generate key visualizations for specified nutrients. This is a convenience function that combines run_builtin_analysis with automatic visualization generation.

Usage

quick_analysis(
  scale = "huc8",
  year = 2016,
  nutrients = c("nitrogen", "phosphorus"),
  include_wwtp = TRUE,
  output_dir = tempdir(),
  create_maps = TRUE,
  create_networks = TRUE,
  create_comparisons = TRUE,
  create_wwtp_maps = TRUE,
  wwtp_load_units = "kg",
  map_resolution = "medium",
  generate_report = FALSE,
  verbose = TRUE,
  ...
)

Arguments

Value

List with results and file paths of created visualizations

Examples

# Quick analysis with all visualizations (2007-2016 WWTP available)
results <- quick_analysis(
  scale = "huc8",
  year = 2012,  # Use valid year
  nutrients = c("nitrogen", "phosphorus"),
  include_wwtp = TRUE,
  generate_report = TRUE
)

# Agricultural only analysis for pre-WWTP year
results <- quick_analysis(
  scale = "county",
  year = 2005,  # Before WWTP data
  nutrients = "nitrogen",
  include_wwtp = FALSE,
  create_networks = FALSE
)

# High-resolution analysis with expanded year range
results <- quick_analysis(
  scale = "huc8",
  year = 2008,  # Use valid WWTP year
  nutrients = "phosphorus",
  include_wwtp = TRUE,
  map_resolution = "high"
)

Quick Data Check

Description

Perform quick validation checks on analysis results

Usage

quick_check(results, verbose = TRUE)

Arguments

Value

Logical. TRUE if all checks pass

Quick State Analysis with Visualization

Description

Run state-level analysis with automatic visualizations

Usage

quick_state_analysis(
  state,
  scale = "huc8",
  year = 2016,
  nutrients = c("nitrogen", "phosphorus"),
  include_wwtp = TRUE,
  output_dir = file.path(tempdir(), paste0("state_", tolower(state), "_results")),
  create_maps = TRUE,
  create_networks = TRUE,
  create_comparisons = TRUE,
  verbose = TRUE,
  ...
)

Arguments

Value

List with results and visualizations

Examples

# Quick state analysis - use states with good data coverage
results <- quick_state_analysis(
  state = "TX",  # Texas has good data coverage
  scale = "county",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

Complete Manureshed Analysis Workflow (Built-in Data)

Description

Run complete manureshed analysis using built-in NuGIS data (start-2016) and optional WWTP data. For WWTP analysis beyond 2016, users must provide their own data. Supports analysis of nitrogen, phosphorus, or both nutrients simultaneously.

Usage

run_builtin_analysis(
  scale = "huc8",
  year = 2016,
  nutrients = c("nitrogen", "phosphorus"),
  output_dir = tempdir(),
  include_wwtp = TRUE,
  wwtp_year = NULL,
  custom_wwtp_nitrogen = NULL,
  custom_wwtp_phosphorus = NULL,
  wwtp_column_mapping = NULL,
  wwtp_skip_rows = 0,
  wwtp_header_row = 1,
  wwtp_load_units = "kg",
  add_texas = FALSE,
  save_outputs = TRUE,
  cropland_threshold = NULL,
  verbose = TRUE
)

Arguments

Value

List with all analysis results for specified nutrients

Examples

# Basic analysis using built-in data (2007-2016 WWTP available)
results_2016 <- run_builtin_analysis(
  scale = "huc8",
  year = 2016,
  nutrients = c("nitrogen", "phosphorus"),
  include_wwtp = TRUE
)

# Analysis for earlier year (no WWTP available) - nitrogen only
results_2010 <- run_builtin_analysis(
  scale = "county",
  year = 2010,
  nutrients = "nitrogen",
  include_wwtp = FALSE
)

# Analysis for earlier year with WWTP now available
results_2010 <- run_builtin_analysis(
  scale = "county",
  year = 2010,
  nutrients = "nitrogen",
  include_wwtp = TRUE  # Now supported for 2010!
)

# Analysis for year before WWTP availability
results_2005 <- run_builtin_analysis(
  scale = "huc8",
  year = 2005,
  nutrients = "phosphorus",
  include_wwtp = FALSE  # No WWTP data before 2007
)

Run State-Level Analysis

Description

Run manureshed analysis for a specific state

Usage

run_state_analysis(
  state,
  scale = "huc8",
  year = 2016,
  nutrients = c("nitrogen", "phosphorus"),
  include_wwtp = TRUE,
  output_dir = file.path(tempdir(), paste0("state_", tolower(state), "_results")),
  verbose = TRUE,
  ...
)

Arguments

Value

List with analysis results for the state

Examples

# Use Texas which has more data
texas_results <- run_state_analysis(
  state = "TX",
  scale = "county",  # Use county for faster processing
  year = 2016,
  nutrients = "nitrogen",  # Single nutrient for speed
  include_wwtp = TRUE
)

# California county-level analysis
ca_results <- run_state_analysis(
  state = "CA",
  scale = "county",
  year = 2010,
  nutrients = "nitrogen"
)

Save Analysis Summary

Description

Save comprehensive summary of analysis parameters and results

Usage

save_analysis_summary(results, file_path = NULL, format = "rds")

Arguments

Value

Character. Path to saved file

Examples

# Create analysis results first
results <- run_builtin_analysis(scale = "county", year = 2016)

# Save complete analysis summary
summary_path <- file.path(tempdir(), "analysis_summary_2016.json")
save_analysis_summary(results, summary_path, format = "json")

Save Centroid Data

Description

Save centroid data to CSV file for transition probability analysis

Usage

save_centroid_data(
  data,
  file_path = NULL,
  scale = "huc8",
  nutrient = "nitrogen",
  analysis_type = "centroids",
  year = format(Sys.Date(), "%Y")
)

Arguments

Value

Character. Path to saved file

Examples

# Create some example data first
results <- run_builtin_analysis(scale = "county", year = 2016, include_wwtp = TRUE)

# Save centroids for transition analysis
if ("integrated" %in% names(results) && "nitrogen" %in% names(results$integrated)) {
  centroids <- add_centroid_coordinates(results$integrated$nitrogen)
  save_centroid_data(centroids, scale = "county", nutrient = "nitrogen")
}

Save Plot

Description

Save ggplot object to file with publication-quality settings

Usage

save_plot(
  plot,
  file_path,
  width = 11,
  height = 6,
  dpi = 300,
  units = "in",
  device = NULL
)

Arguments

Value

Character. Path to saved file

Examples

# Create a simple plot for demonstration
library(ggplot2)
p <- ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point()

# Save with default settings (300 DPI, 11x6 inches)
save_plot(p, file.path(tempdir(), "test_plot.png"))

# Save with custom dimensions for presentation
save_plot(p, file.path(tempdir(), "presentation_plot.png"), width = 16, height = 9)

# Save as PDF for publication
save_plot(p, file.path(tempdir(), "publication_figure.pdf"), width = 8, height = 6)

Save Spatial Data

Description

Save spatial data to RDS file with standardized naming

Usage

save_spatial_data(
  data,
  file_path = NULL,
  scale = "huc8",
  nutrient = "both",
  analysis_type = "combined",
  year = format(Sys.Date(), "%Y")
)

Arguments

Value

Character. Path to saved file

Examples

# Create some example results first
results <- run_builtin_analysis(scale = "county", year = 2016)

# Save with auto-generated filename
save_spatial_data(results$agricultural, scale = "county", year = 2016)

# Save with custom filename
save_spatial_data(results$agricultural,
                  file.path(tempdir(), "my_results.rds"))

Save Transition Probability Matrix

Description

Save transition probability matrix to CSV with metadata

Usage

save_transition_matrix(
  transition_df,
  file_path,
  nutrient,
  analysis_type = "combined",
  metadata = NULL
)

Arguments

Value

Character. Path to saved file

Examples

# Create example analysis results first
results <- run_builtin_analysis(scale = "county", year = 2016, include_wwtp = TRUE)

# Save transition probabilities (only if integrated results exist)
if ("integrated" %in% names(results) && "nitrogen" %in% names(results$integrated)) {
  centroids <- add_centroid_coordinates(results$integrated$nitrogen)
  transitions <- calculate_transition_probabilities(centroids, "combined_N_class")
  save_transition_matrix(transitions,
                        file.path(tempdir(), "transitions_nitrogen.csv"),
                        "nitrogen")
}

Print Summary of Analysis Results

Description

Print formatted summary of manureshed analysis results to the console. The summary includes analysis configuration parameters (scale, year, nutrients, WWTP inclusion), spatial coverage statistics, agricultural nutrient classifications with counts and percentages, WWTP integration metrics (if applicable), integrated classifications (if available), output file information, and processing time.

Usage

summarize_results(results, detailed = FALSE)

Arguments

Details

The summary output is organized into sections:

Analysis Configuration

Scale, year, nutrients analyzed, WWTP inclusion, state (if applicable)

Spatial Coverage

Total number of spatial units analyzed

Agricultural Classifications

Nitrogen and phosphorus classification counts and percentages

WWTP Integration

Number of facilities and total loads by nutrient (if applicable)

Integrated Classifications

Combined agricultural-WWTP classes (if detailed = TRUE)

Output Files

Number and types of created files (if saved)

Processing Time

Analysis duration in minutes (if available)

Classification names are cleaned for display (underscores replaced with spaces, line breaks removed). Percentages are rounded to one decimal place. All console output uses message and can be suppressed with suppressMessages.

Value

Invisibly returns the input results list unchanged. The function is called primarily for its side effect of printing a formatted summary to the console. The invisible return allows for piping operations while displaying the summary.

See Also

run_builtin_analysis for generating analysis results, quick_check for quick validation, compare_analyses for comparing two result sets

Examples

# Basic summary
results <- run_builtin_analysis(scale = "county", year = 2016)
summarize_results(results)

# Detailed summary with integrated classifications
results <- run_builtin_analysis(
  scale = "huc8",
  year = 2012,
  include_wwtp = TRUE
)
summarize_results(results, detailed = TRUE)

## Not run: 
  # This requires magrittr - never auto-run
  library(magrittr)
  results <- run_builtin_analysis(scale = "huc2", year = 2015) %>%
    summarize_results() %>%
    export_for_gis(output_dir = tempdir())

## End(Not run)

Test OSF Connection

Description

Test downloading a small dataset to verify OSF connectivity

Usage

test_osf_connection(verbose = TRUE)

Arguments

Value

Logical. TRUE if test successful

Validate Required Columns

Description

Validate Required Columns

Usage

validate_columns(data, required_cols, data_type = "data")

Arguments

Value

Logical. TRUE if all columns present, stops with error otherwise

Aggregate WWTP Data by Spatial Boundaries

Description

Aggregate WWTP loads by spatial units (counties, HUC8s, etc.)

Usage

wwtp_aggregate_by_boundaries(wwtp_sf, boundaries, nutrient, boundary_id_col)

Arguments

Value

Data frame with aggregated loads by spatial unit

Classify WWTP Sources by Load Size

Description

Classify WWTP facilities into size categories based on annual nutrient loads

Usage

wwtp_classify_sources(wwtp_data, nutrient)

Arguments

Value

Data frame with source_class column added

Examples

# Load WWTP data first
wwtp_data <- load_builtin_wwtp("nitrogen", 2016)

# Classify nitrogen sources
classified_data <- wwtp_classify_sources(wwtp_data, "nitrogen")
table(classified_data$source_class)

Clean WWTP Data

Description

Clean and validate WWTP data for analysis

Clean and validate WWTP data for analysis

Usage

wwtp_clean_data(wwtp_data, nutrient)

wwtp_clean_data(wwtp_data, nutrient)

Arguments

Value

Data frame with cleaned WWTP data

Data frame with cleaned WWTP data

Examples

## Not run: 
# Clean user-loaded data will not run. They need to be supplied by users
clean_data <- wwtp_clean_data(raw_wwtp_data, "nitrogen")

# Clean OSF data (usually already clean, available 2007-2016)
osf_data <- load_builtin_wwtp("phosphorus", 2012)
clean_data <- wwtp_clean_data(osf_data, "phosphorus")

## End(Not run)

# Load and clean WWTP data
raw_wwtp_data <- load_builtin_wwtp("nitrogen", 2016)
clean_data <- wwtp_clean_data(raw_wwtp_data, "nitrogen")

Filter WWTP Data for Positive Loads

Description

Filter WWTP data to include only facilities with positive nutrient loads

Usage

wwtp_filter_positive_loads(wwtp_data, nutrient)

Arguments

Value

Data frame with facilities having positive loads

Complete WWTP Processing Pipeline

Description

Run complete WWTP processing pipeline for both nutrients

Usage

wwtp_process_complete(
  nitrogen_path = NULL,
  phosphorus_path = NULL,
  column_mapping = NULL,
  skip_rows = 0,
  header_row = 1,
  load_units = "kg",
  verbose = TRUE
)

Arguments

Value

List with processed nitrogen and phosphorus WWTP data

Examples

# Process built-in OSF data (2016 default)
wwtp_results_builtin <- wwtp_process_complete(
  nitrogen_path = NULL,     # Use built-in data
  phosphorus_path = NULL,   # Use built-in data
  verbose = TRUE
)

# Process custom user data
# wwtp_results_custom <- wwtp_process_complete(
#   nitrogen_path = "nitrogen_2020.csv",
#   phosphorus_path = "phosphorus_2020.csv",
#   load_units = "lbs"
# )

# Mixed: OSF for one nutrient, custom for another
# wwtp_results_mixed <- wwtp_process_complete(
#   nitrogen_path = NULL,           # Use OSF built-in
#   phosphorus_path = "custom_P.csv" # Use custom
# )

Convert WWTP Data to Spatial Format

Description

Convert WWTP data frame to sf spatial object

Usage

wwtp_to_spatial(wwtp_data, crs = 4326)

Arguments

Value

sf object with WWTP facilities as point geometries

Examples

# Load and convert to spatial format
wwtp_data <- load_builtin_wwtp("nitrogen", 2016)
wwtp_clean_data <- wwtp_clean_data(wwtp_data, "nitrogen")
wwtp_sf <- wwtp_to_spatial(wwtp_clean_data)

# Convert and transform to analysis CRS (without using pipe operator)
wwtp_sf <- wwtp_to_spatial(wwtp_clean_data)
wwtp_sf_transformed <- sf::st_transform(wwtp_sf, 5070)  # Albers Equal Area Conic