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examples/basic_analysis.R

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+# Australian Health and Geographic Data (AHGD) - R Example
+# 
+# This example demonstrates how to load and analyse the AHGD dataset using R
+
+library(arrow)      # For Parquet files
+library(readr)     # For CSV files
+library(jsonlite)  # For JSON files
+library(dplyr)     # For data manipulation
+library(ggplot2)   # For visualisation
+
+# Load dataset (Parquet recommended for performance)
+load_ahgd_data <- function(format = "parquet") {
+  if (format == "parquet") {
+    data <- arrow::read_parquet("ahgd_data.parquet")
+  } else if (format == "csv") {
+    data <- readr::read_csv("ahgd_data.csv")
+  } else if (format == "json") {
+    json_data <- jsonlite::fromJSON("ahgd_data.json")
+    data <- as.data.frame(json_data$data)
+  } else {
+    stop("Unsupported format. Use 'parquet', 'csv', or 'json'")
+  }
+  
+  return(data)
+}
+
+# Basic analysis
+analyse_ahgd <- function() {
+  # Load data
+  df <- load_ahgd_data("parquet")
+  
+  cat("Dataset dimensions:", dim(df), "\n")
+  cat("Column names:", paste(names(df), collapse = ", "), "\n\n")
+  
+  # Summary statistics for numeric columns
+  numeric_cols <- sapply(df, is.numeric)
+  if (any(numeric_cols)) {
+    cat("Summary statistics:\n")
+    print(summary(df[, numeric_cols]))
+  }
+  
+  # State-level health indicators
+  if ("state_name" %in% names(df) && "life_expectancy_years" %in% names(df)) {
+    state_summary <- df %>%
+      group_by(state_name) %>%
+      summarise(
+        avg_life_expectancy = mean(life_expectancy_years, na.rm = TRUE),
+        avg_smoking = mean(smoking_prevalence_percent, na.rm = TRUE),
+        avg_obesity = mean(obesity_prevalence_percent, na.rm = TRUE),
+        .groups = 'drop'
+      )
+    
+    cat("\nHealth indicators by state:\n")
+    print(state_summary)
+  }
+  
+  return(df)
+}
+
+# Create visualisations
+create_plots <- function(df) {
+  # Life expectancy distribution
+  p1 <- ggplot(df, aes(x = life_expectancy_years)) +
+    geom_histogram(bins = 20, fill = "skyblue", alpha = 0.7) +
+    labs(title = "Distribution of Life Expectancy",
+         x = "Life Expectancy (Years)",
+         y = "Count") +
+    theme_minimal()
+  
+  # Smoking vs Life Expectancy
+  if (all(c("smoking_prevalence_percent", "life_expectancy_years") %in% names(df))) {
+    p2 <- ggplot(df, aes(x = smoking_prevalence_percent, y = life_expectancy_years)) +
+      geom_point(alpha = 0.6, color = "darkblue") +
+      geom_smooth(method = "lm", se = TRUE, color = "red") +
+      labs(title = "Smoking Prevalence vs Life Expectancy",
+           x = "Smoking Prevalence (%)",
+           y = "Life Expectancy (Years)") +
+      theme_minimal()
+    
+    # Save plots
+    ggsave("life_expectancy_distribution.png", p1, width = 8, height = 6, dpi = 300)
+    ggsave("smoking_vs_life_expectancy.png", p2, width = 8, height = 6, dpi = 300)
+  }
+}
+
+# Run analysis
+main <- function() {
+  cat("Loading Australian Health and Geographic Data...\n")
+  data <- analyse_ahgd()
+  
+  cat("Creating visualisations...\n")
+  create_plots(data)
+  
+  cat("Analysis complete!\n")
+}
+
+# Execute if run directly
+if (sys.nframe() == 0) {
+  main()
+}

examples/basic_analysis.py

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+"""
+Example: Basic analysis of Australian Health and Geographic Data
+
+This example demonstrates how to load and analyse the AHGD dataset
+using Python and common data science libraries.
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def load_dataset(format_type='parquet'):
+    """Load AHGD dataset in specified format."""
+    if format_type == 'parquet':
+        return pd.read_parquet('ahgd_data.parquet')
+    elif format_type == 'csv':
+        return pd.read_csv('ahgd_data.csv')
+    elif format_type == 'json':
+        import json
+        with open('ahgd_data.json', 'r') as f:
+            data = json.load(f)
+        return pd.DataFrame(data['data'])
+    else:
+        raise ValueError(f"Unsupported format: {format_type}")
+
+def basic_analysis():
+    """Perform basic statistical analysis."""
+    # Load data
+    df = load_dataset('parquet')
+    
+    print(f"Dataset shape: {df.shape}")
+    print(f"Columns: {list(df.columns)}")
+    
+    # Summary statistics
+    numeric_cols = df.select_dtypes(include=['float64', 'int64']).columns
+    print("\nSummary Statistics:")
+    print(df[numeric_cols].describe())
+    
+    # State-level aggregations
+    if 'state_name' in df.columns and 'life_expectancy_years' in df.columns:
+        state_health = df.groupby('state_name').agg({
+            'life_expectancy_years': 'mean',
+            'smoking_prevalence_percent': 'mean',
+            'obesity_prevalence_percent': 'mean'
+        }).round(2)
+        
+        print("\nHealth Indicators by State:")
+        print(state_health)
+    
+    return df
+
+def create_visualisations(df):
+    """Create basic visualisations."""
+    plt.style.use('seaborn-v0_8')
+    
+    # Life expectancy distribution
+    plt.figure(figsize=(10, 6))
+    plt.subplot(2, 2, 1)
+    df['life_expectancy_years'].hist(bins=20, alpha=0.7)
+    plt.title('Distribution of Life Expectancy')
+    plt.xlabel('Years')
+    
+    # Health indicators correlation
+    if all(col in df.columns for col in ['life_expectancy_years', 'smoking_prevalence_percent']):
+        plt.subplot(2, 2, 2)
+        plt.scatter(df['smoking_prevalence_percent'], df['life_expectancy_years'], alpha=0.6)
+        plt.xlabel('Smoking Prevalence (%)')
+        plt.ylabel('Life Expectancy (Years)')
+        plt.title('Smoking vs Life Expectancy')
+    
+    plt.tight_layout()
+    plt.savefig('ahgd_analysis.png', dpi=300, bbox_inches='tight')
+    plt.show()
+
+if __name__ == "__main__":
+    # Run basic analysis
+    data = basic_analysis()
+    
+    # Create visualisations
+    create_visualisations(data)
+    
+    print("\nAnalysis complete! Check ahgd_analysis.png for visualisations.")