# Load packages
library(tidyverse)
library(tidyquant)

1 Import stock prices of your choice

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_tb1 <- prices %>%
  
  group_by(symbol) %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "quarterly",
               type       = "log") %>%
  ungroup() %>% 
  set_names(c("asset", "date", "returns"))


asset_returns_tb1
## # A tibble: 100 × 3
##    asset date        returns
##    <chr> <date>        <dbl>
##  1 SPY   2012-03-30  0.104  
##  2 SPY   2012-06-29 -0.0289 
##  3 SPY   2012-09-28  0.0615 
##  4 SPY   2012-12-31 -0.00383
##  5 SPY   2013-03-28  0.0999 
##  6 SPY   2013-06-28  0.0289 
##  7 SPY   2013-09-30  0.0511 
##  8 SPY   2013-12-31  0.100  
##  9 SPY   2014-03-31  0.0169 
## 10 SPY   2014-06-30  0.0503 
## # ℹ 90 more rows

3 Make plot

asset_returns_tb1 %>%
         ggplot(aes(x = returns)) +
         geom_density(aes(color = asset), show.legend = FALSE, alpha = 1) + 
         geom_histogram(aes(fill = asset), show.legend = FALSE, alpha = 0.3,bindwidth = 0.01 ) + 
         facet_wrap(~asset, ncol = 1) +
  
         # labeling
         labs (title = "Distribution of Monthly Returns, 2012-2016",
               y     = "Frequency",
               x.    = "Rate of Returns",
               caption = "A typic monthly return is higher for SPY and IJS than for AGG, EEM, and EFA")

4 Interpret the plot

5 Change the global chunck options

Hide the code, messages, and warnings