• Visualize the distribution of chicken weights using scatter plots, bar charts, and histograms.
• Identify the most popular feed types and their impact on chicken weights.
• Compare average weights across feed types using boxplots and summary statistics.
• Provide actionable insights on which feed types yield the best growth results.

The dataset consists of:

• weight: Chicken weights in grams.
• feed: Types of feed given to chickens (e.g., casein, sunflower, horsebean). 

This project utilized various tools and techniques in R to explore and visualize the dataset effectively:

• Data Exploration: Functions like data() and nrow() were used to inspect the dataset, while basic plots (plot()) were employed to visualize initial patterns in chicken weights.
• Custom Visualizations: Enhanced scatter plots with custom colors and shapes using:
  
  • my_colors and my_shapes vectors for feed-based differentiation.
  • Added legends to improve interpretability with legend().
• Bar Charts: Used barplot() to display the distribution of weights by feed type, incorporating custom colors for better visualization.
• Histograms: Visualized the distribution of chicken weights with hist(), highlighting frequency trends across weight ranges.
• Boxplots: Compared weight distributions across feed types using boxplot() with features like notch = TRUE and varwidth = TRUE for better visual insights into median and variability.
• Statistical Analysis: Calculated mean weights for each feed type using tapply() and visualized them with a grouped bar chart (barplot()).
• Data Cleaning: Ensured consistent analysis by:
  
  • Mapping colors and shapes to feed types.
  • Sorting chickens by weight for better grouping and analysis.
  • Using na.rm = TRUE in calculations to handle any potential missing values.

• Most chickens weighed between 300–350 grams.
• Very few chickens exceeded 400 grams.

• Casein and sunflower were the most commonly used feed types.

• Chickens fed with casein showed the highest average weight, while those fed with linseed had the lowest.

• Boxplots revealed significant variability in weight for certain feed types, indicating inconsistent results.

• Assigned specific colors and shapes to feed types for better visualization clarity.

• Ordered chickens by weight to ensure consistent insights.

• Although the dataset had no missing values, precautions were included with na.rm = TRUE.

Understanding how feed types impact chicken growth allows for data-driven decisions in optimizing poultry farming. This project demonstrates how R can transform raw data into actionable insights through statistical analysis and visualization.