# Install necessary packages
if(!require(tidyverse)) install.packages("tidyverse")
if(!require(caret)) install.packages("caret")

# Load Libraries
library(tidyverse)
library(caret)

# Download data
url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/00526/data.zip"
dest_file <- "data/data.zip"
if(!dir.exists("data"))dir.create("data")
if(!file.exists(dest_file))download.file(url, destfile = dest_file)

# Unzip
if(!file.exists("data/BitcoinHeistData.csv"))unzip(dest_file, "BitcoinHeistData.csv", exdir="data")

# Import data from CSV
ransomware <- read_csv("data/BitcoinHeistData.csv")

# Turn labels into factors, bw is a binary factor for ransomware/non-ransomware
ransomware <- ransomware %>% mutate(label=as.factor(label), bw=as.factor(ifelse(label=="white", "white", "black")))

# Validation set made from 50% of BitcoinHeist data, reduce later if possible. Binary outcomes (bw)
test_index <- createDataPartition(y = ransomware$bw, times = 1, p = .5, list = FALSE)

workset <- ransomware[-test_index,]
validation <- ransomware[test_index,]

# Split the working set into a training set and a test set @ 50%, reduce later if possible. Binary outcomes (bw)
test_index <- createDataPartition(y = workset$bw, times = 1, p = .5, list = FALSE)

train_set <- workset[-test_index,]
test_set <- workset[test_index,]

# Clean up environment
rm(dest_file, url)