# Load packages
library(tidyverse)
library(tidyquant)

1 Import stock prices of your choice

# Choose stocks
symbols <- c("MSFT", "NVDA", "JPM")

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

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: 144 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 MSFT  2012-03-30  0.193 
##  2 MSFT  2012-06-29 -0.0466
##  3 MSFT  2012-09-28 -0.0209
##  4 MSFT  2012-12-31 -0.0999
##  5 MSFT  2013-03-28  0.0770
##  6 MSFT  2013-06-28  0.195 
##  7 MSFT  2013-09-30 -0.0301
##  8 MSFT  2013-12-31  0.125 
##  9 MSFT  2014-03-31  0.0989
## 10 MSFT  2014-06-30  0.0242
## # ℹ 134 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) +
    
    # labeling
    labs(title = "Distribution of Monthly Returns, 2012-Present",
         y     = "frequency",
         x     = "rate of returns",
         capition = "")

4 Interpret the plot

JPM and MSFT both are clustered close together making them less volatile, and NVDA was more spaced out making it a more volatile stock. JPM and MSFT both seem like good investments.

5 Change the global chunck options

Hide the code, messages, and warnings