# Load packages
library(tidyverse)
library(tidyquant)

1 Import stock prices of your choice

# choose stocks
symbols <- c("HD", "TSLA", "AMC", "WAL", "MSFT")

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: 93 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 HD    2012-03-30  0.183 
##  2 HD    2012-06-29  0.0578
##  3 HD    2012-09-28  0.136 
##  4 HD    2012-12-31  0.0287
##  5 HD    2013-03-28  0.126 
##  6 HD    2013-06-28  0.109 
##  7 HD    2013-09-30 -0.0159
##  8 HD    2013-12-31  0.0870
##  9 HD    2014-03-31 -0.0340
## 10 HD    2014-06-30  0.0287
## # ℹ 83 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.legned = 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 typic monthly return is higher for HD than for MSFT, TSLA, WAL, and AMC.")

4 Interpret the plot

The stock that will make the strongest return and most consistent monthly return would be the Home Depot stock (HD).