# Load packages
library(tidyverse)
library(tidyquant)

1 Import stock prices of your choice

symbols <- c("AAPL", "BBWI", "CCEP", "DKS", "DLTR")

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)

4 Interpret the plot

what does the plot tell you? which stock do you expect higher monthly returns? which stock has highest typical monthly return?

The wider the data is spread on individual stocks in the histogram/density graph. The riskier the stock is on a quarterly basis. The closer the data is to the center of the graph the less volatile the stock is but less rewarding as well. Based on the information I believe that CCEP will outperform the other 4 stocks I chose. closely followed by BBWI. Typically CCEP has the highest return.

5 Change the global chunck options

Hide the code, messages, and warnings