# Load packages

# Core
library(tidyverse)
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library(tidyquant)
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Goal

Take raw prices of five individual stocks and transform them into monthly returns five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”

1 Import stock prices

# 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

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

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)+

    # labeling
    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.")