Day: October 21, 2024

library(RSQLite)
library(DBI)
library(ggplot2)

# Define the path to your SQLite database
db_path <- "/home/jbyungrokim/CSV/CSV.db"

# Connect to the SQLite database
conn <- dbConnect(RSQLite::SQLite(), dbname = db_path)

# Adjust the query to calculate the sum for each item in October 2024
query <- "
SELECT 
    Item, 
    SUM(Pay_Amount) AS Total_Spent
FROM 
    '지출-액수_2024_10_21'
WHERE 
    strftime('%Y-%m', '20' || substr(Date_Transaction, 7, 2) || '-' || substr(Date_Transaction, 1, 2) || '-' || substr(Date_Transaction, 4, 2)) = '2024-10'
GROUP BY 
    Item
ORDER BY 
    Total_Spent DESC;
"

# Execute the query and store the result in a data frame
October_2024_sum <- dbGetQuery(conn, query)

# Print the result
print("Sum of Pay_Amount by Item for October 2024:")
print(October_2024_sum)

# Save the result as a CSV file
csv_file_path <- "/home/jbyungrokim/Downloads/October_2024_sum.csv"
write.csv(October_2024_sum, file = csv_file_path, row.names = FALSE)
print(paste("Data has been written to", csv_file_path))

# Create a bar graph using ggplot2
ggplot(October_2024_sum, aes(x = reorder(Item, -Total_Spent), y = Total_Spent)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  theme_minimal() +
  labs(title = "Total Spending by Item for October 2024",
       x = "Item",
       y = "Total Spent ($)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Close the connection to the SQLite database
dbDisconnect(conn)