DATA 4310 Mini Project 1 — Bitcoin Price Trends

1. Warm-up: Cars Dataset Median (10 pts)

The built-in cars dataset contains the speed and stopping distances of cars.
Here I compute the median of the first column (speed):

# Load cars dataset
data(cars)

# Compute median speed
median_speed <- median(cars[, 1])
median_speed

## [1] 15

Answer: The median of the first column is 15.

2. Extracting Bitcoin Data via API (20 pts)

I will retrieve daily BTC–USD price data for the past 100 days from CryptoCompare.
The data is in JSON format, so I will use jsonlite::fromJSON().

# Load required package
library(jsonlite)

# Construct the URL for 100 days of BTC-USD daily data
url <- "https://min-api.cryptocompare.com/data/v2/histoday?fsym=BTC&tsym=USD&limit=100"

# Retrieve JSON data
btc_raw <- fromJSON(url)

# Inspect structure
str(btc_raw, max.level = 2)

## List of 6
##  $ Response  : chr "Success"
##  $ Message   : chr ""
##  $ HasWarning: logi FALSE
##  $ Type      : int 100
##  $ RateLimit : Named list()
##  $ Data      :List of 4
##   ..$ Aggregated: logi FALSE
##   ..$ TimeFrom  : int 1751155200
##   ..$ TimeTo    : int 1759795200
##   ..$ Data      :'data.frame':   101 obs. of  9 variables:

The price data is stored under btc_raw$Data$Data.
I’ll extract it into a clean data frame and compute the maximum daily close price.

# Extract the data sublist
btc_df <- btc_raw$Data$Data

# Convert timestamp to Date format
btc_df$time <- as.POSIXct(btc_df$time, origin = "1970-01-01")
btc_df$date <- as.Date(btc_df$time)

# Look at first few rows
head(btc_df)

##                  time     high      low     open volumefrom   volumeto    close
## 1 2025-06-28 20:00:00 108538.4 107233.6 107346.4    6094.35  657403121 108391.9
## 2 2025-06-29 20:00:00 108815.7 106756.4 108391.9   14220.51 1530756559 107169.8
## 3 2025-06-30 20:00:00 107574.1 105295.0 107169.8   15730.48 1673173145 105724.2
## 4 2025-07-01 20:00:00 109818.5 105143.1 105724.2   19611.45 2124516059 108886.6
## 5 2025-07-02 20:00:00 110584.4 108579.6 108886.6   16647.63 1825182142 109639.0
## 6 2025-07-03 20:00:00 109810.0 107283.9 109639.0   10440.09 1130647601 108027.7
##   conversionType conversionSymbol       date
## 1         direct                  2025-06-29
## 2         direct                  2025-06-30
## 3         direct                  2025-07-01
## 4         direct                  2025-07-02
## 5         direct                  2025-07-03
## 6         direct                  2025-07-04

Now compute the maximum closing price:

max_close <- max(btc_df$close, na.rm = TRUE)
max_close

## [1] 124723

Answer: The maximum daily close price for BTC over the past 100 days is 1.24723^{5} USD.

3. Mini Project — Bitcoin Price Trends (70 pts)

Title & Topic (10 pts)

Project Title: Bitcoin Price Trends — Last 100 Days
I’ll explore daily BTC price behavior using data from CryptoCompare.

Research Questions (15 pts)

What is the maximum, minimum, and average closing price of Bitcoin over the past 100 days?
How volatile has Bitcoin been during this period (measured by standard deviation of daily returns)?
What are the short-term and long-term trends in Bitcoin closing prices?
Are there noticeable spikes or drops in price, and on what dates did they occur?

Data Extraction (25 pts)

I have already extracted 100-day BTC data using the CryptoCompare API in Q2.
I will reuse btc_df and clean it for analysis.

# Keep relevant columns
btc_clean <- btc_df[, c("date", "open", "high", "low", "close", "volumefrom", "volumeto")]

# Check structure
str(btc_clean)

## 'data.frame':    101 obs. of  7 variables:
##  $ date      : Date, format: "2025-06-29" "2025-06-30" ...
##  $ open      : num  107346 108392 107170 105724 108887 ...
##  $ high      : num  108538 108816 107574 109818 110584 ...
##  $ low       : num  107234 106756 105295 105143 108580 ...
##  $ close     : num  108392 107170 105724 108887 109639 ...
##  $ volumefrom: num  6094 14221 15730 19611 16648 ...
##  $ volumeto  : num  6.57e+08 1.53e+09 1.67e+09 2.12e+09 1.83e+09 ...

# Sort by date just to be sure
btc_clean <- btc_clean[order(btc_clean$date), ]

Data Description & Transformation (20 pts)

I’ll calculate basic statistics and daily returns for further analysis.

# Basic descriptive statistics
summary_stats <- data.frame(
  Min_Close = min(btc_clean$close),
  Max_Close = max(btc_clean$close),
  Mean_Close = mean(btc_clean$close),
  Median_Close = median(btc_clean$close),
  SD_Close = sd(btc_clean$close)
)
summary_stats

##   Min_Close Max_Close Mean_Close Median_Close SD_Close
## 1  105724.2    124723   114879.6     115401.1 4187.038

Daily Returns & Volatility

# Compute daily returns
btc_clean$return <- c(NA, diff(log(btc_clean$close)))

# Volatility (std dev of returns)
volatility <- sd(na.omit(btc_clean$return))
volatility

## [1] 0.01559527

Visualization

library(ggplot2)

# Closing price trend
ggplot(btc_clean, aes(x = date, y = close)) +
  geom_line(color = "steelblue", size = 1) +
  labs(
    title = "Bitcoin Closing Price — Last 100 Days",
    x = "Date",
    y = "BTC Close (USD)"
  ) +
  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.

# Daily returns plot
ggplot(btc_clean, aes(x = date, y = return)) +
  geom_line(color = "darkred", size = 0.8) +
  labs(
    title = "Bitcoin Daily Log Returns",
    x = "Date",
    y = "Log Return"
  ) +
  theme_minimal()

## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).

Summary

The max BTC close was 1.24723^{5} USD.
The min BTC close was 1.0572418^{5} USD.
Average closing price: 1.1487958^{5} USD.
Volatility: 0.0156 (std dev of log returns).
Visualizations show both price trends and volatility patterns clearly.