# Load packages

# Core
library(tidyverse)
library(tidyquant)

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") %>%
  ungroup() %>%
  
  set_names(c("asset", "date", "returns"))

asset_returns_tbl
## # A tibble: 300 × 3
##    asset date        returns
##    <chr> <date>        <dbl>
##  1 SPY   2012-01-31  0.0295 
##  2 SPY   2012-02-29  0.0425 
##  3 SPY   2012-03-30  0.0317 
##  4 SPY   2012-04-30 -0.00670
##  5 SPY   2012-05-31 -0.0619 
##  6 SPY   2012-06-29  0.0398 
##  7 SPY   2012-07-31  0.0118 
##  8 SPY   2012-08-31  0.0247 
##  9 SPY   2012-09-28  0.0250 
## 10 SPY   2012-10-31 -0.0184 
## # ℹ 290 more rows

3 Make plot

asset_returns_tbl %>%
  
  ggplot(aes(x = returns)) +
  geom_density(aes(col = asset), alpha = 1, show.legend = FALSE) +
  geom_histogram(aes(fill = asset), show.legend = FALSE, aplha = 0.3, binwidth = 0.01) +
  facet_wrap(~asset, ncol = 1) +
  
  # labeling
  labs(title = "Monthly returns since 2012-2016",
       x = "Frequency",
       y = "Rate of Returns",
       caption = "A typic monthly return is higher for SPV and IJS than for AGG, EEM, and EFA.")