# Load packages
library(tidyverse)
library(tidyquant)

1 Import stock prices of your choice

symbols <- c("AMZN", "AAPL", "TSLA")

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

asset_returns_tbl
## # A tibble: 60 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 AMZN  2012-03-30  0.123 
##  2 AMZN  2012-06-29  0.120 
##  3 AMZN  2012-09-28  0.108 
##  4 AMZN  2012-12-31 -0.0137
##  5 AMZN  2013-03-28  0.0604
##  6 AMZN  2013-06-28  0.0412
##  7 AMZN  2013-09-30  0.119 
##  8 AMZN  2013-12-31  0.243 
##  9 AMZN  2014-03-31 -0.170 
## 10 AMZN  2014-06-30 -0.0351
## # ℹ 50 more rows

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 = 0.01) +
    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")

## 4 Interpret the plot

5 Change the global chunck options

Hide the code, messages, and warnings