# Load packages
library(tidyverse)
library(tidyquant)

1 Import stock prices of your choice

# Choose stocks
symbols <- c("TSLA", "DELL", "AAPL", "SOXL", "SMCI")

prices <- tq_get(x    = symbols, 
                 get  = "stock.prices", 
                 from = "2012-01-01", 
                 to = "2016-01-01")

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: 64 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 TSLA  2012-03-30  0.282 
##  2 TSLA  2012-06-29 -0.174 
##  3 TSLA  2012-09-28 -0.0664
##  4 TSLA  2012-12-31  0.146 
##  5 TSLA  2013-03-28  0.112 
##  6 TSLA  2013-06-28  1.04  
##  7 TSLA  2013-09-30  0.588 
##  8 TSLA  2013-12-31 -0.251 
##  9 TSLA  2014-03-31  0.326 
## 10 TSLA  2014-06-30  0.141 
## # ℹ 54 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 = 1, binwidth = 0.1)  +
    facet_wrap(~asset, ncol = 1) +
    
    # Labeling
    labs(title = "Distribution of Monthly Returns, 2012-2016",
         y = "Frequency",
         x = "Rate of Returns")

4 Interpret the plot

AAPL has the highest typical quarterly return

5 Change the global chunck options

Hide the code, messages, and warnings