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Performs comprehensive exploratory data analysis on a broiler chicken performance dataset, analyzing the correlation between Eimeria infection and performance measures (weight gain, feed conversion ratio, mortality rate) across different treatments and challenge regimens.

Purpose

This function serves as a complete data analysis pipeline for veterinary/agricultural research data. It loads a CSV dataset, validates required columns, generates descriptive statistics, performs correlation analysis between Eimeria infection and performance metrics, and creates multiple visualizations (histograms, boxplots) to explore relationships between infection status, treatments, challenge regimens, and performance outcomes. The function is designed for exploratory data analysis in poultry health research.

Source Code

def main():
    # Load the dataset
    file_path = 'your_dataset.csv'  # Replace with the path to your dataset
    data = load_dataset(file_path)
    
    if data is None:
        return

    # Display the first few rows of the dataset
    print("Dataset Preview:")
    print(data.head())

    # Check if required columns exist
    required_columns = ['weight_gain', 'feed_conversion_ratio', 'mortality_rate', 'eimeria_infection', 'treatment', 'challenge_regimen']
    missing_columns = [col for col in required_columns if col not in data.columns]
    if missing_columns:
        print(f"Error: Missing columns in the dataset: {missing_columns}")
        return

    # Descriptive statistics for performance measures
    print("\nDescriptive Statistics:")
    performance_measures = ['weight_gain', 'feed_conversion_ratio', 'mortality_rate']
    print(data[performance_measures].describe())

    # Check for missing values
    print("\nMissing Values:")
    print(data.isnull().sum())

    # Visualize the distribution of performance measures
    for measure in performance_measures:
        plt.figure(figsize=(8, 4))
        sns.histplot(data[measure].dropna(), kde=True)
        plt.title(f'Distribution of {measure}')
        plt.xlabel(measure)
        plt.ylabel('Frequency')
        plt.show()

    # Correlation analysis between Eimeria infection and performance measures
    correlations = {}
    for measure in performance_measures:
        if data['eimeria_infection'].isnull().any() or data[measure].isnull().any():
            print(f"Warning: Missing data for correlation analysis with {measure}.")
            continue
        corr, p_value = pearsonr(data['eimeria_infection'], data[measure])
        correlations[measure] = {'correlation': corr, 'p_value': p_value}

    # Display correlation results
    print("\nCorrelation Analysis:")
    for measure, stats in correlations.items():
        print(f"{measure}: Correlation = {stats['correlation']:.2f}, p-value = {stats['p_value']:.4f}")

    # Visualize the relationship between Eimeria infection and performance measures
    for measure in performance_measures:
        plt.figure(figsize=(8, 4))
        sns.boxplot(x='eimeria_infection', y=measure, data=data)
        plt.title(f'{measure} by Eimeria Infection Status')
        plt.xlabel('Eimeria Infection (0 = No, 1 = Yes)')
        plt.ylabel(measure)
        plt.show()

    # Grouping by treatment and challenge regimen
    grouped_data = data.groupby(['treatment', 'challenge_regimen'])

    # Descriptive statistics by group
    print("\nDescriptive Statistics by Treatment and Challenge Regimen:")
    for name, group in grouped_data:
        print(f"\nGroup: {name}")
        print(group[performance_measures].describe())

    # Visualize performance measures by treatment and challenge regimen
    for measure in performance_measures:
        plt.figure(figsize=(12, 6))
        sns.boxplot(x='treatment', y=measure, hue='challenge_regimen', data=data)
        plt.title(f'{measure} by Treatment and Challenge Regimen')
        plt.xlabel('Treatment')
        plt.ylabel(measure)
        plt.legend(title='Challenge Regimen')
        plt.show()

    # Conclusion
    print("\nConclusion:")
    print("The analysis provides descriptive statistics and visualizations to explore the correlation between Eimeria infection and performance measures in broilers. Further statistical tests may be required to draw definitive conclusions.")

Return Value

Returns None. The function performs side effects including printing analysis results to console and displaying multiple matplotlib/seaborn visualizations. It may return early (None) if the dataset fails to load or if required columns are missing.

Dependencies

• pandas
• numpy
• seaborn
• matplotlib
• scipy
• os

Required Imports

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

Usage Example

# Ensure load_dataset function is defined
def load_dataset(file_path):
    try:
        return pd.read_csv(file_path)
    except Exception as e:
        print(f'Error loading dataset: {e}')
        return None

# Prepare your dataset CSV with required columns:
# weight_gain, feed_conversion_ratio, mortality_rate, eimeria_infection, treatment, challenge_regimen

# Update the file_path variable in the function or modify the CSV filename
# Then call the function
main()

# The function will:
# 1. Load 'your_dataset.csv'
# 2. Display dataset preview and statistics
# 3. Show distribution plots for performance measures
# 4. Perform correlation analysis with Eimeria infection
# 5. Generate boxplots comparing groups by treatment and challenge regimen

Best Practices

• Ensure the CSV file path is updated from 'your_dataset.csv' to your actual dataset location before running
• The load_dataset() function must be defined or imported before calling main()
• Dataset must contain all required columns: 'weight_gain', 'feed_conversion_ratio', 'mortality_rate', 'eimeria_infection', 'treatment', 'challenge_regimen'
• The 'eimeria_infection' column should be binary (0 = No, 1 = Yes) for proper visualization
• Run in an environment that supports matplotlib plot display (Jupyter notebook, IDE with plot support, or with appropriate backend configured)
• Consider adding plt.close() calls after plt.show() to prevent memory issues with multiple plots
• For large datasets, consider adding data sampling or limiting the number of visualizations
• The function performs multiple statistical tests; consider adjusting for multiple comparisons if using results for publication
• Missing values are reported but not automatically handled; consider preprocessing data before analysis

Tags

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