# Using the Interactive Dashboard

## Overview

The manureshed interactive dashboard provides a user-friendly interface for exploring nutrient balance data without writing R code. This is ideal for:

- Policy makers and decision makers
- Extension agents and educators
- Students learning about nutrient management
- Quick exploratory analysis
- Presentations and demonstrations

## Installation Requirements

The dashboard requires several additional packages beyond the core manureshed package:

    # Install dashboard dependencies
    install.packages(c(
      "shiny",
      "shinydashboard",
      "leaflet",
      "plotly",
      "DT"
    ))

These packages are optional - manureshed works fine without them if you only need the programmatic interface. They’re only needed for the interactive dashboard.

## Launching the Dashboard

Once the dependencies are installed, launch the dashboard with:

    library(manureshed)

    # Launch the dashboard
    launch_dashboard()

The dashboard will open in your default web browser. By default, it uses a random available port. You can specify a port:

    # Launch on specific port
    launch_dashboard(port = 3838)

## Dashboard Interface

### Main Components

The dashboard has four main sections:

1. Sidebar: Analysis parameters and controls
2. Map Tab: Interactive spatial visualization
3. Statistics Tab: Summary metrics and plots
4. Data Table Tab: Detailed results table
5. Help Tab: Documentation and information

### Sidebar Controls

#### Analysis Parameters

Spatial Scale: - County: ~3,000 units, good for policy analysis - HUC8 Watershed: ~2,000 units, good for watershed management - HUC2 Region: 18 units, good for regional overview

Year: - Slider from 2007-2016 - 2007-2016: WWTP data available - Earlier years: Agricultural data only

Nutrient: - Nitrogen - Phosphorus

Include Wastewater Treatment Plants: - Checked: Shows integrated agricultural + WWTP analysis - Unchecked: Shows agricultural-only analysis

Advanced Options (click “Show advanced options”): - Cropland Threshold: Minimum cropland area for inclusion (default: 1,234 acres)

#### Action Buttons

Run Analysis: - Click to execute the analysis with selected parameters - Shows progress indicator - Displays notification when complete

Download Data: - Exports results as CSV file - Includes all analysis columns

## Using the Map Tab

### Interactive Map Features

The map tab displays a color-coded interactive map of nutrient classifications:

Color Legend: - Red: Nutrient sources (surplus) - Blue: Sinks with deficit (no fertilizer use) - Purple: Sinks with deficit (despite fertilizer use) - Green: Within watershed/county (balanced with WWTP) - Light blue: Excluded (below cropland threshold)

Interaction: - Zoom: Scroll wheel or +/- buttons - Pan: Click and drag - Click: See detailed information popup - Hover: Highlight individual units

Popup Information: When you click a map area, you’ll see: - Unit ID (FIPS code, HUC8, or HUC2) - Classification - Surplus/deficit amount (kg) - Cropland area (acres)

### Map Navigation Tips

Finding Specific Areas: 1. Zoom in to your region of interest 2. Use the search function in your browser (Ctrl+F / Cmd+F) 3. Click on areas to identify them

Comparing Regions: 1. Run analysis with WWTP included 2. Note colors and patterns 3. Uncheck WWTP box 4. Run analysis again 5. Compare how classifications change

## Using the Statistics Tab

### Value Boxes

Four key metrics displayed at the top:

1. Sources: Red box - nutrient surplus areas
2. Sink (Deficit): Blue box - deficit without fertilizer
3. Sink (Fertilizer): Purple box - deficit despite fertilizer
4. Within Watershed/County: Green box - balanced (WWTP included)
5. Excluded: Light blue box - below threshold

### Classification Distribution

Pie Chart: - Shows proportions of each classification - Interactive: Hover to see exact counts - Click legend items to show/hide categories

### Surplus/Deficit Distribution

Histogram: - Shows distribution of surplus (positive) and deficit (negative) values - Interactive: Hover to see bin counts - Zoom by clicking and dragging

### Summary Statistics

Text output showing: - Analysis parameters used - Total units analyzed - Classification counts - Statistical summary of surplus/deficit values

## Using the Data Table Tab

### Interactive Table Features

The data table provides detailed results with:

Search: - Global search box (top right) - Column-specific search (top of each column)

Sorting: - Click column headers to sort - Click again to reverse sort - Multi-column sort: Shift+click

Filtering: - Use column search boxes - Type partial matches - Filter by multiple columns

Pagination: - Choose rows per page (10, 25, 50, 100) - Navigate between pages

### Useful Filters

Find All Sources:

    In classification column filter: type "Source"

Find High Surplus Areas:

    Sort by surplus column (descending)
    Look at top entries

Find Specific State:

    In ID column filter (for county scale): type state FIPS code
    Example: "19" for Iowa

## Help Tab

The Help tab provides: - About manureshed information - How to use instructions - Classification definitions - Package version - Link to documentation

## Workflow Examples

### Example 1: Basic State Analysis

Goal: Analyze Iowa watersheds in 2016

Steps: 1. Launch dashboard: `launch_dashboard()` 2. Set Scale: HUC8 Watershed 3. Set Year: 2016 4. Set Nutrient: Nitrogen 5. Check: Include Wastewater Treatment Plants 6. Click: Run Analysis 7. Wait for completion notification 8. Explore results in Map tab 9. Click Download Data to save results

### Example 2: Compare Agricultural vs. Integrated

Goal: See how WWTP changes classifications

Steps: 1. Set parameters (e.g., HUC8, 2016, Nitrogen) 2. Check: Include WWTP 3. Click: Run Analysis 4. Note the Statistics tab value boxes 5. Uncheck: Include WWTP 6. Observe how value boxes change 7. Compare the differences

What to Look For: - How many sources remain? - How many sinks become “Within Watershed”? - How does total surplus/deficit change?

### Example 3: Year-over-Year Exploration

Goal: Explore temporal trends

Steps: 1. Set Scale: County 2. Set Nutrient: Nitrogen 3. Set Year: 2010 4. Check: Include WWTP 5. Run Analysis 6. Note key statistics 7. Change Year: 2013 8. Run Analysis again 9. Compare with 2010 results 10. Repeat for 2016

Document Your Findings: - Screenshot the Statistics tab for each year - Download data files for each year - Use scenario comparison function for formal analysis

### Example 4: Threshold Sensitivity

Goal: See how threshold affects excluded areas

Steps: 1. Click: Show advanced options 2. Set Threshold: 500 acres 3. Run Analysis 4. Note number in “Excluded” box 5. Set Threshold: 1234 acres (default) 6. Run Analysis 7. Note change in “Excluded” 8. Set Threshold: 2000 acres 9. Run Analysis 10. Compare all three

### Example 5: Create Presentation Materials

Goal: Generate maps for a presentation

Steps: 1. Set desired parameters 2. Run Analysis 3. Map tab: Take screenshot (Print Screen / Cmd+Shift+4) 4. Statistics tab: Take screenshot of charts 5. Arrange screenshots in presentation software

Tips: - Zoom map to desired extent before screenshot - Use full-screen browser mode for cleaner screenshots - Consider running multiple scenarios

## Downloading Results

### Data Download

Click “Download Data” button to export a CSV file containing:

Columns Include: - Unit ID (FIPS, HUC8, or HUC2) - Cropland area - Surplus/deficit values - Classifications - If WWTP included: combined classifications and surplus

File Name Format:

    manureshed_[scale]_[year]_[nutrient].csv

Example: `manureshed_huc8_2016_nitrogen.csv`

### Using Downloaded Data

Import into spreadsheet software:

    # In Excel or Google Sheets:
    # File > Import > CSV
    # Select the downloaded file

Further analysis in R:

    # Read downloaded data
    data <- read.csv("manureshed_huc8_2016_nitrogen.csv")

    # Filter to sources only
    sources <- data[data$N_class == "Source", ]

    # Calculate statistics
    mean(sources$N_surplus)

## Performance Tips

### For Faster Analysis

Choose Appropriate Scale: - HUC2: Fastest (~18 units) - HUC8: Moderate (~2,000 units) - County: Slowest (~3,000 units)

Processing Times (approximate): - County: 30-60 seconds - HUC8: 20-40 seconds - HUC2: 5-10 seconds

Note: First run for a year is slower (downloads data), subsequent runs are faster (uses cached data).

### Browser Performance

Recommended Browsers: - Chrome (best performance) - Firefox (good performance) - Safari (good on Mac) - Edge (good on Windows)

If Dashboard is Slow: - Close unused browser tabs - Try a different browser - Restart R session - Clear browser cache

## Troubleshooting

### Dashboard Won’t Launch

Error: “Package ‘shiny’ is required”

Solution:

    install.packages(c("shiny", "shinydashboard", "leaflet", "plotly", "DT"))

Error: “Could not find dashboard files”

Solution:

    devtools::install()  # Reinstall package

### Analysis Fails

Error during analysis:

Possible causes: - Network issue (downloading data) - Invalid year selection - Insufficient memory

Solutions: 1. Check internet connection 2. Try a different year 3. Use smaller scale (HUC2) 4. Restart R session

### Map Not Displaying

Map shows base layer only, no colored regions:

Possible causes: - Analysis not run yet - Analysis failed silently

Solutions: 1. Click “Run Analysis” again 2. Check browser console (F12) for errors 3. Try different parameters

### Value Boxes Show Zero

Green box shows 0:

This is normal when: - Agricultural-only analysis (no “Within Watershed” category) - Area genuinely has no balanced units

To see non-zero values: - Check “Include WWTP” - Run Analysis

## Comparison with Programmatic Interface

### When to Use Dashboard

Dashboard is better for: - Quick exploration - Demonstrations - Non-R users - Visual presentations - Teaching

### When to Use R Code

R code is better for: - Batch processing multiple years - Custom analyses - Reproducible research - Integration with other analyses - Automated workflows

### Hybrid Approach

Use both together:

    # 1. Explore with dashboard
    launch_dashboard()
    # Identify interesting patterns

    # 2. Reproduce in code for publication
    results <- run_builtin_analysis(
      scale = "huc8",
      year = 2016,
      nutrients = "nitrogen",
      include_wwtp = TRUE
    )

    # 3. Do advanced analysis
    comparison <- compare_scenarios(...)

## Sharing Results

### For Colleagues Without R

1. Take screenshots of dashboard
2. Download data CSV files
3. Email files with brief explanation

### For R Users

Share the code instead:

    # Colleagues can reproduce with:
    library(manureshed)
    results <- run_builtin_analysis(
      scale = "huc8",
      year = 2016,
      nutrients = "nitrogen",
      include_wwtp = TRUE
    )

### For Presentations

1. Use dashboard in presentation mode
2. Navigate to pre-analyzed results
3. Use zoom to highlight areas of interest
4. Switch between tabs to show different views

## Accessibility Features

The dashboard includes: - Keyboard navigation support - Screen reader compatibility - High contrast color schemes - Resizable text - Alternative text for graphics

## Privacy and Security

Data Privacy: - All analysis runs locally on your computer - No data sent to external servers - Downloads are saved to your local machine

Security: - Dashboard runs on localhost (your computer only) - Not accessible from internet - No authentication required (local use only)

## Advanced: Deploying for Teams

If you want to share the dashboard with your team:

### Option 1: Shinyapps.io

Deploy to cloud hosting:

    library(rsconnect)

    # Configure account (one time)
    setAccountInfo(name="your-account", token="...", secret="...")

    # Deploy
    deployApp(
      appDir = system.file("shiny", "dashboard", package = "manureshed"),
      appName = "manureshed-dashboard"
    )

### Option 2: RStudio Connect

Deploy to institutional server:

    # Follow RStudio Connect deployment guide
    # Requires RStudio Connect server

### Option 3: Shiny Server

Install on shared server: - Requires system administration access - Best for long-term team use - See Shiny Server documentation

## Getting Help

Within Dashboard: - Click “Help” tab for basic information

Package Documentation:

    ?launch_dashboard
    ?run_builtin_analysis

Online Resources: - Package website - GitHub repository - Vignettes: `browseVignettes("manureshed")`

Report Issues: - Use GitHub issues for bug reports - Include error messages and screenshots - Describe steps to reproduce

## Related Vignettes

- `vignette("getting-started")` - Basic package usage
- `vignette("scenario-comparison")` - Compare multiple scenarios
- `vignette("advanced-features")` - Advanced analysis techniques
- `vignette("data-integration")` - Using custom data

## Summary

The manureshed interactive dashboard provides:

Pros: - No R coding required - Visual and intuitive - Great for exploration - Easy to demonstrate - Interactive maps and plots

Cons: - Requires additional packages - Limited to one analysis at a time - Not suitable for batch processing - Less flexible than coding

Best For: - Initial data exploration - Presentations and teaching - Non-programmer collaborators - Quick “what if” scenarios - Visual communication

For reproducible research and complex workflows, use the programmatic interface alongside the dashboard.