# Load packages
library(tidyverse)
library(tidyquant)

1 Import stock prices of your choice

# Choose stocks 
symbols <- c("TSLA", "MFST", "AAPL", "NKE", "GOOGL")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices", 
                 from = "2025-01-01", 
                 to   = "2026-01-01")

2 Convert prices to returns by quarterly

asset_returns_tbl <- prices %>%
    
    group_by(symbol) %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 type       = "log") %>%
    ungroup() %>%
    
    set_names(c("asset", "date", "return"))

asset_returns_tbl
## # A tibble: 48 × 3
##    asset date        return
##    <chr> <date>       <dbl>
##  1 TSLA  2025-01-31  0.0646
##  2 TSLA  2025-02-28 -0.323 
##  3 TSLA  2025-03-31 -0.123 
##  4 TSLA  2025-04-30  0.0850
##  5 TSLA  2025-05-30  0.205 
##  6 TSLA  2025-06-30 -0.0868
##  7 TSLA  2025-07-31 -0.0300
##  8 TSLA  2025-08-29  0.0798
##  9 TSLA  2025-09-30  0.287 
## 10 TSLA  2025-10-31  0.0263
## # ℹ 38 more rows

3 Make plot

asset_returns_tbl %>%
    
    ggplot(aes(x = return))  +
    geom_density(aes(color = asset), alpha = 1) +
    geom_histogram(aes(fill = asset), show.legend = FALSE, alpha = 0.3, birwidth = 0.001) +
    facet_wrap(~asset, ncol = 1)

# labeling
    labs(title = "Distribution of monthly returns, 2025-2026",
         y     = "Frequency",
         x     = "Rate of returns",
         caption = "A typical monthly return is higher for TSLA and MSFT than for AAPL, NKE, and GOOGL")
## <ggplot2::labels> List of 4
##  $ y      : chr "Frequency"
##  $ x      : chr "Rate of returns"
##  $ title  : chr "Distribution of monthly returns, 2025-2026"
##  $ caption: chr "A typical monthly return is higher for TSLA and MSFT than for AAPL, NKE, and GOOGL"

4 Interpret the plot

The plot demonstrates that Tesla has the highest and the lowest typical return rate.

5 Change the global chunck options

Hide the code, messages, and warnings