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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()
}
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