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Scenario Comparison Analysis

Overview

The compare_scenarios() function allows you to compare multiple analysis scenarios side-by-side. This is useful for:

Basic Usage

Simple Comparison: Agricultural vs. WWTP

The most common use case is comparing agricultural nutrient balances with and without wastewater treatment plant data.

library(manureshed)

# Run analysis without WWTP
base_scenario <- run_builtin_analysis(
  scale = "huc8",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = FALSE
)

# Run analysis with WWTP
wwtp_scenario <- run_builtin_analysis(
  scale = "huc8",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

# Compare the two scenarios
comparison <- compare_scenarios(list(
  "Agricultural Only" = base_scenario,
  "Agricultural + WWTP" = wwtp_scenario
))

Understanding the Results

The comparison returns three main components:

1. Comparison Data

A data frame with metrics for each scenario:

# View the comparison data
print(comparison$comparison_data)

Key metrics include: - n_sources: Number of nutrient source areas - n_sinks: Number of nutrient sink areas - n_balanced / n_within_watershed: Balanced areas - n_excluded: Excluded areas (below cropland threshold) - total_surplus_kg: Total nutrient surplus - total_deficit_kg: Total nutrient deficit

2. Summary Statistics

Statistical comparison showing differences between scenarios:

# View summary
print(comparison$summary)

# See specific differences
print(comparison$summary$differences)

The summary shows: - Base scenario name - Delta (change) in key metrics - Percent change from base scenario

3. Visualization Plots

Three plots are automatically generated:

# Bar chart comparing classification counts
comparison$plots$bar_chart

# Surplus/deficit comparison
comparison$plots$surplus_deficit

# Percent change from base scenario
comparison$plots$percent_change

Advanced Examples

Multiple Scenarios

Compare more than two scenarios:

# Create three different scenarios
conservative <- run_builtin_analysis(
  scale = "huc8",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = FALSE,
  cropland_threshold = 2000  # More restrictive
)

moderate <- run_builtin_analysis(
  scale = "huc8",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE,
  cropland_threshold = 1234  # Default
)

liberal <- run_builtin_analysis(
  scale = "huc8",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE,
  cropland_threshold = 500   # Less restrictive
)

# Compare all three
multi_comparison <- compare_scenarios(list(
  "Conservative (No WWTP, High Threshold)" = conservative,
  "Moderate (WWTP, Default Threshold)" = moderate,
  "Liberal (WWTP, Low Threshold)" = liberal
))

# View results
print(multi_comparison$comparison_data)
multi_comparison$plots$bar_chart

Year-over-Year Comparison

Compare the same parameters across different years:

# Analyze multiple years
year_2010 <- run_builtin_analysis(
  scale = "county",
  year = 2010,
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

year_2013 <- run_builtin_analysis(
  scale = "county",
  year = 2013,
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

year_2016 <- run_builtin_analysis(
  scale = "county",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

# Compare temporal trends
temporal <- compare_scenarios(list(
  "2010" = year_2010,
  "2013" = year_2013,
  "2016" = year_2016
))

# See how classifications changed over time
temporal$plots$bar_chart
temporal$plots$percent_change

Scale Comparison

Compare the same year at different spatial scales:

county_scale <- run_builtin_analysis(
  scale = "county",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

huc8_scale <- run_builtin_analysis(
  scale = "huc8",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

huc2_scale <- run_builtin_analysis(
  scale = "huc2",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

# Compare scales
scale_comp <- compare_scenarios(list(
  "County (n=~3000)" = county_scale,
  "HUC8 (n=~2000)" = huc8_scale,
  "HUC2 (n=18)" = huc2_scale
))

print(scale_comp$comparison_data)

Saving Results

Save Plots

Save comparison plots to files:

# Create output directory
output_dir <- "scenario_comparison_results"
dir.create(output_dir, showWarnings = FALSE)

# Save all plots
save_plot(
  comparison$plots$bar_chart,
  file.path(output_dir, "classification_comparison.png"),
  width = 12, height = 8
)

save_plot(
  comparison$plots$surplus_deficit,
  file.path(output_dir, "surplus_deficit_comparison.png"),
  width = 12, height = 8
)

save_plot(
  comparison$plots$percent_change,
  file.path(output_dir, "percent_change.png"),
  width = 12, height = 8
)

Save Data

Export comparison data to CSV:

# Save comparison data
write.csv(
  comparison$comparison_data,
  file.path(output_dir, "scenario_comparison_data.csv"),
  row.names = FALSE
)

# Save summary statistics
write.csv(
  comparison$summary$differences,
  file.path(output_dir, "scenario_differences.csv"),
  row.names = FALSE
)

Auto-save During Comparison

Have plots automatically saved:

# Comparison with automatic plot saving
comparison <- compare_scenarios(
  scenario_list = list(
    "Base" = base_scenario,
    "WWTP" = wwtp_scenario
  ),
  create_plots = TRUE,
  output_dir = "my_comparison_results"  # Plots saved here
)

Interpreting Results

Understanding Deltas

Positive vs. negative changes:

# Extract differences
diffs <- comparison$summary$differences

# Interpret changes
if (diffs$delta_sources > 0) {
  cat("Adding WWTP increased sources by", diffs$delta_sources, "units\n")
} else if (diffs$delta_sources < 0) {
  cat("Adding WWTP decreased sources by", abs(diffs$delta_sources), "units\n")
}

# Percent change
cat("This represents a", round(diffs$pct_change_sources, 1), "% change\n")

Key Metrics to Watch

Classification Changes: - delta_sources: Change in nutrient source areas - delta_sinks: Change in nutrient sink areas

Magnitude Changes: - delta_surplus: Change in total surplus (kg) - delta_deficit: Change in total deficit (kg)

Negative values mean the second scenario has fewer/less than the base scenario.

Use Cases

Management Analysis

Evaluate the impact of adding WWTP nutrient recovery:

# Current state (no WWTP recovery)
current <- run_builtin_analysis(
  scale = "huc8",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = FALSE
)

# With WWTP recovery management
with_policy <- run_builtin_analysis(
  scale = "huc8",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

# Compare
policy_impact <- compare_scenarios(list(
  "Current (No WWTP)" = current,
  "With WWTP Recovery" = with_policy
))

# Key question: How many sink areas could benefit?
sinks_helped <- policy_impact$summary$differences$delta_sinks
cat("WWTP recovery could help", abs(sinks_helped), "deficit areas\n")

Sensitivity Analysis

Test sensitivity to cropland threshold:

thresholds <- c(500, 1000, 1234, 1500, 2000)
results <- list()

for (thresh in thresholds) {
  results[[paste0("Threshold_", thresh)]] <- run_builtin_analysis(
    scale = "huc8",
    year = 2016,
    nutrients = "nitrogen",
    include_wwtp = TRUE,
    cropland_threshold = thresh
  )
}

# Compare all thresholds
sensitivity <- compare_scenarios(results)

# See how excluded areas change
excluded_counts <- sensitivity$comparison_data$n_excluded
names(excluded_counts) <- names(results)
print(excluded_counts)

Regional Comparison

Compare different states or regions:

# Iowa
iowa <- run_state_analysis(
  state = "IA",
  scale = "county",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

# Nebraska
nebraska <- run_state_analysis(
  state = "NE",
  scale = "county",
  year = 2016,
  nutrients = "nitrogen",
  include_wwtp = TRUE
)

# Compare states
state_comp <- compare_scenarios(list(
  "Iowa" = iowa,
  "Nebraska" = nebraska
))

state_comp$plots$bar_chart

Tips and Best Practices

1. Keep Comparisons Meaningful

Compare scenarios that differ in one key aspect for clearest interpretation:

# GOOD: Only WWTP inclusion changes
compare_scenarios(list(
  "No WWTP" = run_builtin_analysis(year=2016, include_wwtp=FALSE),
  "With WWTP" = run_builtin_analysis(year=2016, include_wwtp=TRUE)
))

# LESS CLEAR: Multiple things change at once
compare_scenarios(list(
  "Base" = run_builtin_analysis(year=2016, scale="county", include_wwtp=FALSE),
  "Alternative" = run_builtin_analysis(year=2015, scale="huc8", include_wwtp=TRUE)
))

2. Name Scenarios Clearly

Use descriptive names that explain what differs:

# GOOD names
compare_scenarios(list(
  "2016 Agricultural Only" = scenario1,
  "2016 Agricultural + WWTP" = scenario2
))

# Less clear names
compare_scenarios(list(
  "Scenario 1" = scenario1,
  "Scenario 2" = scenario2
))

3. Document Your Analysis

Save parameters with results:

# Create a metadata file
metadata <- data.frame(
  scenario = c("Base", "WWTP"),
  year = c(2016, 2016),
  scale = c("huc8", "huc8"),
  include_wwtp = c(FALSE, TRUE),
  cropland_threshold = c(1234, 1234)
)

write.csv(metadata, "scenario_metadata.csv", row.names = FALSE)

4. Consider Statistical Significance

For year-over-year comparisons, consider whether changes are meaningful:

# Small changes might not be meaningful
diffs <- comparison$summary$differences

if (abs(diffs$pct_change_sources) < 5) {
  cat("Change is less than 5% - may not be substantial\n")
}

Troubleshooting

Different Scales

If comparing scenarios with different scales, metrics won’t be directly comparable:

# This comparison has limited value
compare_scenarios(list(
  "County" = county_results,  # ~3000 units
  "HUC2" = huc2_results       # ~18 units
))

# Better: Compare percentages instead
# Use the comparison data to calculate percentages yourself

Missing Data

If scenarios have different nutrients:

# One has nitrogen, other has phosphorus - won't compare well
# Make sure both scenarios analyze the same nutrient

See Also

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.