The soilquality R package provides comprehensive tools for calculating Soil Quality Index (SQI) using scientifically validated methods. The package implements Principal Component Analysis (PCA) for Minimum Data Set (MDS) selection and Analytic Hierarchy Process (AHP) for expert-based weighting, transforming complex soil property data into standardized quality metrics.

• Automated MDS Selection: PCA-based dimensionality reduction to identify key soil indicators
• Expert Weighting System: AHP methodology with consistency ratio validation
• Flexible Scoring Functions: Multiple normalization methods for different soil properties
  • Higher-is-better (e.g., organic matter, nutrients)
  • Lower-is-better (e.g., bulk density, electrical conductivity)
  • Optimal range (e.g., pH)
  • Custom threshold-based scoring
• Pre-defined Property Sets: Standard collections for common soil analyses (basic, standard, comprehensive, physical, chemical, fertility)
• Standard Scoring Rules: Automatic rule assignment based on property patterns
• Comprehensive Visualization: Multiple plot types for results interpretation
• Interactive Shiny Application: GUI for non-programmers
• Complete Documentation: Vignettes, examples, and function references

# Install pak if needed
if (!requireNamespace("pak", quietly = TRUE)) {
  install.packages("pak")
}

# Install soilquality
pak::pak("ccarbajal16/soilquality")

# Install devtools if needed
if (!requireNamespace("devtools", quietly = TRUE)) {
  install.packages("devtools")
}

# Install soilquality
devtools::install_github("ccarbajal16/soilquality")

# Coming soon
install.packages("soilquality")

library(soilquality)

# Load example data
data(soil_ucayali)

# Compute SQI with automatic property detection
result <- compute_sqi_properties(
  data = soil_ucayali,
  properties = c("Sand", "Silt", "Clay", "pH", "OM", "P", "K")
)

# View results summary
print(result)

# Visualize SQI distribution
plot(result, type = "distribution")

# Create comprehensive report
plot_sqi_report(result)

# Use standard property set
result <- compute_sqi_properties(
  data = soil_ucayali,
  properties = soil_property_sets$standard
)

# Define custom scoring for specific properties
custom_rules <- list(
  pH = optimum_range(optimal = 6.5, tolerance = 1),
  OM = higher_better(),
  BD = lower_better(),
  P = threshold_scoring(
    thresholds = c(0, 10, 20, 50),
    scores = c(0, 0.5, 0.8, 1.0)
  )
)

result <- compute_sqi_properties(
  data = soil_ucayali,
  properties = c("pH", "OM", "BD", "P"),
  scoring_rules = custom_rules
)

# Create AHP matrix interactively
indicators <- c("pH", "OM", "P", "K")
ahp_matrix <- create_ahp_matrix(indicators, mode = "interactive")

# Use computed AHP weights in SQI calculation
result <- compute_sqi_properties(
  data = soil_ucayali,
  properties = ahp_matrix$indicators,
  pairwise_matrix = ahp_matrix$weights
)

# Start Shiny app for GUI-based analysis
run_sqi_app()

• read_soil_csv() - Import soil data from CSV files
• standardize_numeric() - Z-score standardization of numeric columns
• to_numeric() - Safe type conversion

• pca_select_mds() - PCA-based minimum data set selection

• ahp_weights() - Calculate weights from pairwise comparison matrix
• create_ahp_matrix() - Interactive or programmatic matrix creation
• ratio_to_saaty() - Convert importance ratios to Saaty scale

• score_higher_better() - Normalize properties where higher is better
• score_lower_better() - Normalize properties where lower is better
• score_optimum() - Normalize properties with optimal range
• score_threshold() - Custom piecewise scoring
• score_indicators() - Apply scoring to multiple indicators

• higher_better() - Constructor for higher-is-better rules
• lower_better() - Constructor for lower-is-better rules
• optimum_range() - Constructor for optimal range rules
• threshold_scoring() - Constructor for threshold-based rules

• soil_property_sets - Pre-defined property collections
• standard_scoring_rules() - Automatic rule assignment

• compute_sqi() - File-based workflow
• compute_sqi_df() - In-memory workflow
• compute_sqi_properties() - Enhanced workflow with property selection

• plot.sqi_result() - S3 plot method with multiple types
• plot_sqi_report() - Multi-panel visualization report

• run_sqi_app() - Launch Shiny application

Comprehensive documentation is available through:

• Vignettes:

  • Introduction to soilquality - Basic workflow and concepts
  • Advanced Usage - Custom scoring and property selection
  • AHP Matrices Guide - Creating and interpreting pairwise comparisons

• Function Reference: Access help for any function with ?function_name

• Package Website: https://ccarbajal16.github.io/soilquality

If you use this package in your research, please cite it as:

citation("soilquality")

Or use:

Carbajal, Carlos (2025). soilquality: Soil Quality Index Calculation with PCA and AHP. R package version 1.0.0. https://github.com/ccarbajal16/soilquality

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue for:

• Bug reports
• Feature requests
• Documentation improvements
• Code contributions

Please report issues at: https://github.com/ccarbajal16/soilquality/issues

For questions and discussions, use the GitHub Discussions page.

This package is licensed under the MIT License. See the LICENSE file for details.

This package implements methodologies from soil quality assessment literature, including PCA-based MDS selection and AHP weighting approaches widely used in soil science research.