Introduction

In this Apply it to your data assignment, I applied lessons from Chapters 1–3 of R for Data Science to financial market data. I decided to select three securities: Apple (AAPL), Microsoft (MSFT), and the S&P 500 ETF (SPY). These represent two major tech firms and a broad market benchmark.The goal is to compare stock prices, returns, and volatility using visualization and data transformation.

Load Packages

# Install if missing:
# install.packages(c("tidyquant","dplyr","ggplot2","rmarkdown","knitr"))

library(tidyquant)
library(dplyr)
library(ggplot2)

Data Import

tickers <- c("AAPL", "MSFT", "SPY")

stock_data <- tq_get(tickers,
                     from = "2024-01-01",
                     to   = Sys.Date(),
                     get  = "stock.prices")

head(stock_data)

## # A tibble: 6 × 8
##   symbol date        open  high   low close   volume adjusted
##   <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
## 1 AAPL   2024-01-02  187.  188.  184.  186. 82488700     184.
## 2 AAPL   2024-01-03  184.  186.  183.  184. 58414500     183.
## 3 AAPL   2024-01-04  182.  183.  181.  182. 71983600     180.
## 4 AAPL   2024-01-05  182.  183.  180.  181. 62379700     180.
## 5 AAPL   2024-01-08  182.  186.  182.  186. 59144500     184.
## 6 AAPL   2024-01-09  184.  185.  183.  185. 42841800     184.

Data Wrangling

returns <- stock_data %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
              mutate_fun = periodReturn,
              period = "monthly",
              col_rename = "monthly_returns")

head(returns)

## # A tibble: 6 × 3
## # Groups:   symbol [1]
##   symbol date       monthly_returns
##   <chr>  <date>               <dbl>
## 1 AAPL   2024-01-31        -0.00668
## 2 AAPL   2024-02-29        -0.0185 
## 3 AAPL   2024-03-28        -0.0513 
## 4 AAPL   2024-04-30        -0.00671
## 5 AAPL   2024-05-31         0.130  
## 6 AAPL   2024-06-28         0.0956

summary_stats <- returns %>%
  group_by(symbol) %>%
  summarize(mean_return = mean(monthly_returns, na.rm = TRUE),
            sd_return   = sd(monthly_returns, na.rm = TRUE))

summary_stats

## # A tibble: 3 × 3
##   symbol mean_return sd_return
##   <chr>        <dbl>     <dbl>
## 1 AAPL        0.0131    0.0578
## 2 MSFT        0.0166    0.0623
## 3 SPY         0.0167    0.0323

Data Visualization

Stock Price Trends

ggplot(stock_data, aes(x = date, y = adjusted, color = symbol)) +
  geom_line(size = 1) +
  labs(title = "Stock Prices (AAPL, MSFT, SPY)",
       x = "Date", y = "Adjusted Closing Price",
       color = "Symbol") +
       theme_minimal()

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Monthly Returns

ggplot(returns, aes(x = date, y = monthly_returns, fill = symbol)) +
  geom_col(position = "dodge") +
  labs(title = "Monthly Returns by Stock",
       x = "Date", y = "Return",
       fill = "Symbol") +
  theme_minimal()

Conclusion

From this analysis, I took that AAPL and MSFT had higher monthly returns but also had a greater volatility compared to SPY. The ETF SPY offered more stable performance with lower fluctuations I noticed.This demonstrates the trade-off between risk and return which means individual growth stocks may provide higher gains but with higher uncertainty, while diversified funds provide more steady results.

References

Wickham, H., & Grolemund, G. (2017). R for Data Science. O’Reilly Media.
Data sourced from Yahoo Finance via the tidyquant package.

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Apply it to Your Data 2

Cameron Whitty

2025-09-10