# Load packages
library(tidyverse)
library(tidyquant)

1 Import stock prices of your choice

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

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

2 Convert prices to returns by quarterly

asset_returns_tbl<- prices %>%
    
    group_by(symbol) %>%
    tq_transmute(select = adjusted, 
                 mutate_fun = periodReturn,
                 period     = "quarterly",
                 type       = "log"
                ) %>%

    set_names(c("asset", "date", "returns"))

3 Make plot

asset_returns_tbl %>%
  ggplot(aes(x = returns)) +
  geom_density(aes(color = asset), alpha = 1) +
  geom_histogram(aes(fill = asset), show.legend = FALSE, alpha = 0.3, binwidth = 0.001) +
  facet_wrap(~asset, ncol = 1) +
  labs(
    title   = "Distribution of Monthly Returns, 2012-2016",
    y       = "frequency",
    x       = "rate of returns",
    caption = "A typical monthly return is higher for SPY and IJS than for AGG, EEM, and EFA."
  )

4 Interpret the plot

Between 2012 and 2016, IJS (small-cap value ETF) had the highest typical monthly return, followed closely by SPY (S&P 500 ETF).

5 Change the global chunck options

Hide the code, messages, and warnings