Stock Price Analysis (2016-2024)

Introduction

This analysis visualizes the stock price performance of eight technology companies from 2016 to 2024. We examine both normalized price trends and individual stock trajectories.

Load Libraries

library(tidyverse)
library(quantmod)
library(gridExtra)

Data Collection

# Define stock symbols
stocks <- c("AAPL", "AMD", "MU", "NIO", "NVDA", "PRPL", "SNAP", "TSLA")

# Set date range
start_date <- "2016-01-01"
end_date <- "2024-12-31"

# Function to get stock data
get_stock_data <- function(symbol) {
  tryCatch({
    data <- getSymbols(symbol, src = "yahoo", from = start_date, 
                       to = end_date, auto.assign = FALSE)
    df <- data.frame(Date = index(data), 
                     Close = as.numeric(Cl(data)),
                     Stock = symbol)
    return(df)
  }, error = function(e) {
    message(paste("Error downloading", symbol))
    return(NULL)
  })
}

# Get data for all stocks
stock_data_list <- lapply(stocks, get_stock_data)
stock_data <- bind_rows(stock_data_list)

# Normalize prices (base = $1 at start)
stock_data <- stock_data %>%
  group_by(Stock) %>%
  mutate(Normalized_Price = Close / first(Close)) %>%
  ungroup()

Normalized Stock Prices (2016-2024)

This visualization shows how each stock performed relative to its starting price in 2016. All stocks begin at a normalized value of $1.

# Define colors for each stock
stock_colors <- c("AAPL" = "#d62728", "AMD" = "#1f77b4", "MU" = "#2ca02c", 
                  "NIO" = "#9467bd", "NVDA" = "#ff7f0e", "PRPL" = "#bcbd22",
                  "SNAP" = "#8c564b", "TSLA" = "#e377c2")

# Plot 1: Normalized Stock Prices
ggplot(stock_data, aes(x = Date, y = Normalized_Price, color = Stock)) +
  geom_line(linewidth = 0.5) +
  scale_color_manual(values = stock_colors) +
  scale_y_continuous(breaks = seq(0, 200, 50), limits = c(0, 200)) +
  scale_x_date(breaks = as.Date(c("2016-01-01", "2018-01-01", "2020-01-01", 
                                   "2022-01-01", "2024-01-01")),
               labels = c("2016", "2018", "2020", "2022", "2024")) +
  labs(title = "Normalized Stock Prices (2016-2024)",
       x = "Date",
       y = "Normalized price (Base = $1)",
       color = "Stock") +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
    panel.grid.major = element_line(color = "gray90"),
    panel.grid.minor = element_blank(),
    legend.position = "right",
    axis.text = element_text(size = 10),
    axis.title = element_text(size = 12)
  )

Key Observations

• NVDA (Nvidia) shows the most dramatic growth, reaching over 170x its 2016 price
• AMD demonstrates strong performance with significant volatility
• TSLA (Tesla) shows moderate growth with high volatility
• Other stocks show more modest returns over the period

Individual Stock Performance

This section displays the actual closing prices for each stock, allowing for detailed examination of individual price movements.

# Filter data up to 2023 for the second plot
stock_data_2023 <- stock_data %>%
  filter(Date <= as.Date("2023-12-31"))

# Add TWTR (Twitter) data if available
twtr_data <- tryCatch({
  data <- getSymbols("TWTR", src = "yahoo", from = start_date, 
                     to = "2022-10-27", auto.assign = FALSE)
  df <- data.frame(Date = index(data), 
                   Close = as.numeric(Cl(data)),
                   Stock = "TWTR")
  df
}, error = function(e) {
  message("TWTR data not available (delisted in 2022)")
  NULL
})

# Combine data
plot2_data <- stock_data_2023

if (!is.null(twtr_data)) {
  plot2_data <- bind_rows(plot2_data, twtr_data)
}

# Create faceted plot
ggplot(plot2_data, aes(x = Date, y = Close)) +
  geom_line(color = "#1f4788", linewidth = 0.6) +
  facet_wrap(~ Stock, scales = "free_y", ncol = 3) +
  scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
  labs(x = "Date", y = "Closing prices") +
  theme_minimal() +
  theme(
    strip.background = element_rect(fill = "gray85", color = NA),
    strip.text = element_text(size = 12, face = "bold", color = "black"),
    panel.grid.major = element_line(color = "gray90"),
    panel.grid.minor = element_blank(),
    panel.background = element_rect(fill = "gray95", color = NA),
    axis.text = element_text(size = 9),
    axis.title = element_text(size = 11)
  )

Summary Statistics

# Calculate key statistics
summary_stats <- stock_data %>%
  group_by(Stock) %>%
  summarise(
    `Start Price` = round(first(Close), 2),
    `End Price` = round(last(Close), 2),
    `Total Return (%)` = round(((last(Close) / first(Close)) - 1) * 100, 2),
    `Max Price` = round(max(Close), 2),
    `Min Price` = round(min(Close), 2)
  ) %>%
  arrange(desc(`Total Return (%)`))

knitr::kable(summary_stats)

Stock	Start Price	End Price	Total Return (%)	Max Price	Min Price
NVDA	0.81	137.49	16889.81	148.88	0.63
AMD	2.77	122.44	4320.22	211.38	1.80
TSLA	14.89	417.41	2702.54	479.86	9.58
AAPL	26.34	252.20	857.57	259.02	22.58
MU	14.33	85.31	495.32	153.45	9.56
NIO	6.60	4.38	-33.64	62.84	1.32
SNAP	24.48	10.86	-55.64	83.11	4.99
PRPL	9.66	0.83	-91.36	40.05	0.56

Conclusion

This analysis reveals significant variation in stock performance across the technology sector from 2016 to 2024. NVDA emerged as the clear winner with exceptional returns, while other stocks showed varying degrees of growth and volatility.

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Data Source: Yahoo Finance via quantmod package

Analysis Date: 2025-12-01