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Orchestrates a complete correlation analysis pipeline for Eimeria infection and broiler performance data, from data loading through visualization and results export.

Purpose

This is the main entry point function that coordinates a comprehensive statistical analysis workflow. It loads CSV data, identifies Eimeria infection variables and performance metrics, calculates correlations (both overall and grouped), generates visualizations (heatmaps, scatter plots), produces analytical conclusions, and exports results to multiple CSV and PNG files. Designed for veterinary/agricultural research analyzing the relationship between parasitic infection levels and poultry performance metrics.

Source Code

def main():
    """Main execution function"""
    
    print("="*80)
    print("EIMERIA INFECTION AND BROILER PERFORMANCE CORRELATION ANALYSIS")
    print("="*80)
    
    # Load data
    df = load_data('data.csv')
    
    # Explore data
    categorical_vars, numerical_vars = explore_data(df)
    
    # Identify variables
    eimeria_vars, performance_vars, grouping_vars = identify_variables(df, numerical_vars)
    
    if len(eimeria_vars) == 0 or len(performance_vars) == 0:
        print("\nWARNING: Could not identify Eimeria or performance variables.")
        print("Please ensure your dataset contains appropriate variable names.")
        return
    
    # Overall correlation analysis
    overall_results = calculate_correlations(df, eimeria_vars, performance_vars)
    
    # Grouped correlation analysis
    grouped_results = pd.DataFrame()
    if len(grouping_vars) > 0:
        grouped_results = grouped_correlation_analysis(df, eimeria_vars, 
                                                      performance_vars, grouping_vars)
    
    # Create visualizations
    print("\n" + "="*80)
    print("CREATING VISUALIZATIONS")
    print("="*80)
    
    create_correlation_heatmap(df, eimeria_vars, performance_vars)
    create_scatter_plots(df, eimeria_vars, performance_vars, grouping_vars)
    
    if len(grouped_results) > 0:
        create_grouped_correlation_plot(grouped_results)
    
    # Generate conclusions
    conclusions = generate_conclusions(overall_results, grouped_results, 
                                      eimeria_vars, performance_vars)
    
    # Export results
    export_results(overall_results, grouped_results, conclusions)
    
    print("\n" + "="*80)
    print("ANALYSIS COMPLETE")
    print("="*80)
    print("\nGenerated files:")
    print("  - overall_correlations.csv")
    print("  - grouped_correlations.csv")
    print("  - significant_correlations.csv")
    print("  - correlation_heatmap.png")
    print("  - scatter plots (multiple)")
    print("  - grouped_correlations.png")

Return Value

Returns None. The function produces side effects including console output, generated visualization files (PNG), and exported data files (CSV). If Eimeria or performance variables cannot be identified in the dataset, the function prints a warning and returns early without generating outputs.

Dependencies

• pandas
• numpy
• matplotlib
• seaborn
• scipy

Required Imports

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from scipy.stats import pearsonr
from scipy.stats import spearmanr
import warnings

Usage Example

# Ensure 'data.csv' exists in the current directory with appropriate columns
# The CSV should contain Eimeria-related columns (e.g., 'eimeria_count', 'oocyst_level')
# and performance columns (e.g., 'weight_gain', 'feed_conversion_ratio')

# Simply call the main function to run the entire analysis pipeline
main()

# Expected output:
# - Console output showing analysis progress
# - overall_correlations.csv: correlation coefficients for all data
# - grouped_correlations.csv: correlations by grouping variables
# - significant_correlations.csv: statistically significant correlations only
# - correlation_heatmap.png: visual heatmap of correlations
# - Multiple scatter plot PNG files
# - grouped_correlations.png: grouped correlation visualization

Best Practices

• Ensure the input CSV file 'data.csv' exists before calling this function
• Verify that column names in the dataset follow naming conventions that allow automatic identification of Eimeria and performance variables
• Ensure sufficient disk space and write permissions for output files
• Review console output for warnings about missing variable identification
• All helper functions (load_data, explore_data, etc.) must be properly defined in the same module
• Consider wrapping the function call in a try-except block to handle potential file I/O errors
• The function assumes a specific data structure and variable naming convention - review the identify_variables() function to understand expected column names
• Generated files will overwrite existing files with the same names in the current directory

Tags

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