# HistorySaid Global Economic Dataset — R Quick Start
# Requires: readr, dplyr, ggplot2

library(readr)
library(dplyr)
library(ggplot2)

# Load the unified dataset
df <- read_csv("data/unified/all_indicators.csv", show_col_types = FALSE)

cat(sprintf("Dataset: %s rows, %d countries, %d indicators\n",
            format(nrow(df), big.mark = ","),
            n_distinct(df$country_code),
            n_distinct(df$indicator_id)))
cat(sprintf("Year range: %d–%d\n\n", min(df$year), max(df$year)))

# --- Filter by country and indicator ---
usa_gdp <- df %>%
  filter(country_code == "USA", indicator_id == "wb.NY.GDP.MKTP.CD", !is.na(value)) %>%
  arrange(year)

cat("USA GDP (last 10 years):\n")
print(tail(usa_gdp %>% select(year, value), 10))

# --- Compare countries ---
countries <- c("USA", "CHN", "DEU", "JPN", "IND")
gdp_compare <- df %>%
  filter(indicator_id == "wb.NY.GDP.MKTP.CD",
         country_code %in% countries,
         year == 2022) %>%
  arrange(desc(value))

cat("\nGDP comparison (2022):\n")
print(gdp_compare %>% select(country_name, value))

# --- Plot time series ---
plot_data <- df %>%
  filter(indicator_id == "wb.NY.GDP.MKTP.CD",
         country_code %in% countries,
         !is.na(value)) %>%
  mutate(value_t = value / 1e12)

p <- ggplot(plot_data, aes(x = year, y = value_t, color = country_name)) +
  geom_line(linewidth = 0.8) +
  labs(title = "GDP (trillions USD)",
       x = "Year", y = "Trillions USD", color = "Country") +
  theme_minimal()

ggsave("gdp_comparison.png", p, width = 10, height = 5, dpi = 150)
cat("Plot saved to gdp_comparison.png\n")