# 1. Create vectors for each variable
StudentID <- c("S001", "S002", "S003")       # Nominal / ID
Name <- c("Alice", "Budi", "Citra")          # Nominal / Name
Age <- c(20, 21, 19)                         # Numeric / Continuous
CreditsCompleted <- c(45, 60, 30)            # Numeric / Discrete

# Nominal
Major <- c("Data Sains", "Mathematics", "Statistics")  

# Ordinal
YearLevel <- factor(c("Sophomore", "Junior", "Freshman"),
                    levels = c("Freshman","Sophomore","Junior","Senior"),
                    ordered = TRUE)          

# 2. Combine all vectors into a data frame
students <- data.frame(
  StudentID, Name, Age, CreditsCompleted, Major, YearLevel,
  stringsAsFactors = FALSE
)

# 3. Display the data frame
print(students)

# Install knitr package if not already installed
# install.packages("knitr")
library(knitr)

# Create a data frame for Data Types
variables_info <- data.frame(
  No = 1:5,
  Variable = c(
    "Number of vehicles passing through the toll road each day",
    "Student height in cm",
    "Employee gender (Male / Female)",
    "Customer satisfaction level: Low, Medium, High",
    "Respondent's favorite color: Red, Blue, Green"
  ),
  DataType = c(
    "Quantitative",
    "Quantitative",
    "Quantitative",
    "Qualitative",
    "Qualitative"
  ),
  Subtype = c(
    "Diskrete",
    "Continuous",
    "Nominal",
    "Ordinal",
    "Nominal"
  ),
  stringsAsFactors = FALSE
)

# Display the data frame as a neat table
kable(variables_info, 
      caption = "Table of Variables and Data Types")

# Install DT package if not already installed
# install.packages("DT") 
library(DT)

# Create a data frame for data sources 
data_sources <- data.frame(
  No = 1:4,
  DataSource = c(
    "Daily sales transaction data of the company",
    "Weather reports from BMKG",
    "Product reviews on social media",
    "Warehouse inventory reports"
  ),
  Internal_External = c(
    "Internal",
    "Eksternal",
    "Eksternal",
    "Internal"
  ),
  Structured_Unstructured = c(
    "Structured",
    "Structured",
    "Unstructured",
    "Structured"
  ),
  stringsAsFactors = FALSE
)

# Display the data frame as a neat table
datatable(data_sources, 
          caption = "Table of Data Sources",
          rownames = FALSE) # hides the index column

# Create Data Frame  Transaction
library(DT)
library(knitr)

transactions <- data.frame(
  No = 1:10,
  Date = as.Date(c("2025-10-01", "2025-10-01","2025-10-02", "2025-10-02", "2025-10-03",                       "2025-10-03", "2025-10-04", "2025-10-04", "2025-10-05", "2025-10-05")),
  Quantity = c(2, 5, 1, 3, 4, 2, 6, 1, 3, 5),
  Price = c(1000, 20, 1000, 30, 50, 1000, 25, 1000, 40, 10),
  Product = c("Laptop", "Mouse", "Laptop", "Keyboard", "Mouse", "Laptop", "Keyboard",                    "Laptop", "Mouse", "Keyboard"),
  CustomerTier = factor(c("High", "Medium", "Low", "Medium",                                                         "Medium", "High", "Low", "High", "Low", "Medium"),
  levels = c("Low", "Medium", "High"), ordered = TRUE)
)

transactions$Total <- transactions$Quantity * transactions$Price

str(transactions)

datatable(transactions,
          rownames = FALSE,
          caption = htmltools::tags$strong("Transactions Table")
)

   #create data frame of category variable
library(DT)

categoryVariable <- data.frame(
      Category = c("Date", "Quantity", "Price", "Product",                              "CustomersTier"),
      Variable = c("Categorical (Date)", 
                   "Numeric (Discrete)", 
                   "Numeric(Continuous)", 
                   "Categorical (Nominal)", 
                   "Categorical (Ordinal)"),
      stringsAsFactors = FALSE)
                  
str(categoryVariable)

datatable(categoryVariable,
          caption = htmltools::tags$strong("Identification                                               Table"))

 # create data frame of total quantity
library(DT)

totalQuantity <- aggregate(Quantity ~ Product, data = transactions, sum)

str(totalQuantity)

datatable(totalQuantity,
          rownames = FALSE,
          caption = htmltools::tags$strong("Total Quantity                                                Table")
)

 # create data frame of total reveneu
library(DT)

totalRevenue <- aggregate(Total ~ Product, data = transactions, mean)

str(totalRevenue)

datatable(totalRevenue,
          rownames = FALSE,
          caption = htmltools::tags$strong("Total Reveneu                                                Table")
)

 # create data frame of avarange price
avarangePrice <- aggregate(Price ~ Product, data = transactions, mean)

str(avarangePrice)

datatable(avarangePrice,
          rownames = FALSE,
          caption = htmltools::tags$strong("Avarange Price                                               Table")
)

 # create barplot of date
quantityColors <- c("#a6cba9", "#ecd59f", "#a0ced9")

barplot(totalQuantity$Quantity,
        names.arg = totalQuantity$Product,
        main = "Total Quantity Sold per Product",
        xlab = "Product",
        ylab = "Total Quantity Sold",
        col = quantityColors,
        border = "black")

pie_colors <- c("#cdb4db", "#eba7ac", "#a4c0d6")

revenueTier <- aggregate(Total ~ CustomerTier, data = transactions, sum)

pie(revenueTier$Total, 
    labels = paste(revenueTier$CustomerTier, "-", revenueTier$Total),
    main = "Total Revenue by Customer Tier",
    col = pie_colors)