# Load packages
library(tidyverse)
library(tidyquant)

1 Import stock prices of your choice

# Choose stocks
symbols <- c("AAPL", "DIS", "GE", "NKE", "SBUX")

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: 100 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 AAPL  2012-03-30  0.377 
##  2 AAPL  2012-06-29 -0.0263
##  3 AAPL  2012-09-28  0.137 
##  4 AAPL  2012-12-31 -0.221 
##  5 AAPL  2013-03-28 -0.178 
##  6 AAPL  2013-06-28 -0.103 
##  7 AAPL  2013-09-30  0.191 
##  8 AAPL  2013-12-31  0.169 
##  9 AAPL  2014-03-31 -0.0383
## 10 AAPL  2014-06-30  0.198 
## # ℹ 90 more rows

3 Make plot

asset_returns_tbl %>%
    
    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, binwidth = 0.01) +
    facet_wrap(~asset, ncol = 1) +
    
    # labelig
    labs(title = "Distribution of Monthly Returns, 2012-2016",
         y     = "frequency",
         x     = "rate of returns",
         capition = "")

4 Interpret the plot

All of the stocks that I had were pretty volatile and spaced out, they were not safe stocks.

5 Change the global chunck options

Hide the code, messages, and warnings