Code Along 4

symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG")

prices <- tq_get(
  x = symbols,
  get = "stock.prices",
  from = "2012-01-01",
  to = "2017-01-01"
)

asset_returns_tbl <- prices %>%
  group_by(symbol) %>%
  tq_transmute(
    select     = adjusted,
    mutate_fun = periodReturn,
    period     = "monthly",
    type       = "log"
  ) %>%
  set_names(c("asset", "date", "returns")) %>%
  ungroup()

asset_returns_tbl %>%
  ggplot(aes(x = returns)) +
  geom_histogram(aes(fill = asset), alpha = 0.3, bins = 30, show.legend = FALSE) +
  geom_density(aes(color = asset), show.legend = FALSE) +
  facet_wrap(~ asset, ncol = 1) +
  labs(
    title = "Distribution of Monthly Returns (2012–2016)",
    x = "Monthly Log Returns",
    y = "Frequency",
    caption = "SPY and IJS show higher peak returns than AGG or EEM"
  )