--- title: "Getting Started with manureshed" author: "Olatunde D. Akanbi" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Getting Started with manureshed} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 8, fig.height = 6 ) ``` ```{r setup} 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. ## Quick Start - Complete Analysis The easiest way to get started is with `quick_analysis()`: ```{r quick_start, eval=FALSE} # 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 ```{r check_data, eval=FALSE} # See what data is available check_builtin_data() # Download all data (optional, ~40MB) download_all_data() ``` ### 2. Basic Agricultural Analysis ```{r basic_analysis, eval=FALSE} # 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 ```{r with_wwtp, eval=FALSE} # 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 ```{r access_results, eval=FALSE} # 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 ```{r maps, eval=FALSE} # Basic nitrogen map n_map <- map_agricultural_classification( data = results$agricultural, nutrient = "nitrogen", classification_col = "N_class", title = "Nitrogen Classifications" ) # Save the map save_plot(n_map, "nitrogen_map.png", width = 10, height = 8) ``` ### WWTP Maps ```{r wwtp_maps, eval=FALSE} # Map WWTP facilities facility_map <- map_wwtp_points( results$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 ```{r single_year, eval=FALSE} # 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 ```{r multiple_years, eval=FALSE} # 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: ```{r custom_wwtp, eval=FALSE} # 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 ```{r state_analysis, eval=FALSE} # 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 ```{r individual_data, eval=FALSE} # 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 ```{r memory, eval=FALSE} # For large analyses, clear cache if needed clear_data_cache() # Check package health health_check() ``` ### Quality Checks ```{r quality, eval=FALSE} # 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 ```{r help, eval=FALSE} # 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.