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Performs statistical analysis to determine the correlation between antibiotic use frequency and vaccination modes (in-ovo vs non-in-ovo), generating visualizations and saving results to files.

Purpose

This function serves as a complete data analysis pipeline that: (1) loads antibiotic treatment data from a CSV file, (2) validates required columns exist, (3) calculates Pearson correlation between two vaccination modes, (4) creates a scatter plot visualization, (5) saves correlation metrics to a CSV file, and (6) writes statistical conclusions to a text file. It's designed for analyzing the relationship between antibiotic treatment frequencies in different vaccination contexts.

Source Code

def main():
    print("Starting statistical analysis...")
    print(f"Query: Conclude on the correlation between antibiotic use frequency and vaccination modes (in-ovo true or false). Use a single plot to illustrate this correlation.")
    
    # Load data
    try:
        df = pd.read_csv('input_data.csv')
        print(f"Data loaded successfully: {df.shape}")
    except Exception as e:
        print(f"Error loading data: {e}")
        return
    
    # Data validation
    required_columns = ['DWTreatmentId_False', 'DWTreatmentId_True']
    for col in required_columns:
        if col not in df.columns:
            print(f"Error: Missing required column '{col}' in the dataset.")
            return
    
    # Calculate correlation
    try:
        correlation, p_value = pearsonr(df['DWTreatmentId_False'], df['DWTreatmentId_True'])
        print(f"Correlation calculated: {correlation}, p-value: {p_value}")
    except Exception as e:
        print(f"Error calculating correlation: {e}")
        return
    
    # Plotting
    try:
        plt.figure(figsize=(10, 6))
        sns.scatterplot(x='DWTreatmentId_False', y='DWTreatmentId_True', data=df)
        plt.title('Correlation between Antibiotic Use Frequency and Vaccination Modes')
        plt.xlabel('Antibiotic Use Frequency (Not In-Ovo)')
        plt.ylabel('Antibiotic Use Frequency (In-Ovo)')
        plt.grid(True)
        plt.savefig('plot_01_correlation_antibiotic_vaccination.png')
        plt.close()
        print("Plot saved as 'plot_01_correlation_antibiotic_vaccination.png'")
    except Exception as e:
        print(f"Error generating plot: {e}")
        return
    
    # Save correlation result to a CSV file
    try:
        correlation_data = pd.DataFrame({
            'Metric': ['Correlation', 'P-Value'],
            'Value': [correlation, p_value]
        })
        correlation_data.to_csv('table_01_correlation_results.csv', index=False)
        print("Correlation results saved as 'table_01_correlation_results.csv'")
    except Exception as e:
        print(f"Error saving correlation results: {e}")
        return
    
    # Write conclusions
    try:
        with open('conclusions.txt', 'w') as f:
            f.write("Conclusions on the correlation between antibiotic use frequency and vaccination modes:\n")
            f.write(f"Pearson correlation coefficient: {correlation:.4f}\n")
            f.write(f"P-value: {p_value:.4f}\n")
            if p_value < 0.05:
                f.write("The correlation is statistically significant at the 0.05 significance level.\n")
            else:
                f.write("The correlation is not statistically significant at the 0.05 significance level.\n")
        print("Conclusions written to 'conclusions.txt'")
    except Exception as e:
        print(f"Error writing conclusions: {e}")
        return
    
    print("Analysis completed successfully!")

Return Value

This function returns None implicitly. It performs side effects by creating three output files: 'plot_01_correlation_antibiotic_vaccination.png' (scatter plot), 'table_01_correlation_results.csv' (correlation metrics), and 'conclusions.txt' (statistical interpretation). The function may return early (None) if errors occur during data loading, validation, or processing.

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.stats import pearsonr

Usage Example

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

# Prepare sample input data
data = {
    'DWTreatmentId_False': [10, 15, 20, 25, 30],
    'DWTreatmentId_True': [12, 18, 22, 28, 35]
}
df = pd.DataFrame(data)
df.to_csv('input_data.csv', index=False)

# Run the analysis
main()

# Output files created:
# - plot_01_correlation_antibiotic_vaccination.png
# - table_01_correlation_results.csv
# - conclusions.txt

Best Practices

• Ensure 'input_data.csv' exists and contains the required columns before calling this function
• The function uses comprehensive error handling with try-except blocks for each major operation
• All print statements provide progress tracking and error diagnostics
• The function follows early return pattern on errors to prevent cascading failures
• Output files are automatically named with descriptive prefixes (plot_01, table_01)
• Statistical significance is evaluated at the 0.05 level by default
• The function closes matplotlib figures after saving to prevent memory leaks
• Consider wrapping this function call in a try-except block for production use
• Verify write permissions in the working directory before execution
• The correlation assumes linear relationship between variables; check data distribution first

Tags

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