Data Exploration

Exercises ~ Week 2

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1 Exercise 1

The following table shows sample information for three students. Each observation represents a single student and includes details such as their unique student ID, name, age, total credits completed, major field of study, and year level.

This dataset demonstrates a mixture of variable types:

  • Nominal: StudentID, Name, Major
  • Numeric: Age (continuous), CreditsCompleted (discrete)
  • Ordinal: YearLevel (Freshman → Senior)
StudentID Name Age CreditsCompleted Major YearLevel
S001 Alice 20 45 Data Sains Sophomore
S002 Budi 21 60 Mathematics Junior
S003 Citra 19 30 Statistics Freshman 
# 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)
##   StudentID  Name Age CreditsCompleted       Major YearLevel
## 1      S001 Alice  20               45  Data Sains Sophomore
## 2      S002  Budi  21               60 Mathematics    Junior
## 3      S003 Citra  19               30  Statistics  Freshman

2 Exercise 2

Identify Data Types: Determine the type of data for each of the following variables:

# 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(
    "Numeric",
    "numeric",
    "Categorical",
    "Categorical",
    "Categorical"
  ),
  Subtype = c(
    "Continous",
    "continous",
    "Nominal",
    "Ordinal",
    "Nominal"
  ),
  stringsAsFactors = FALSE
)

# Display the data frame as a neat table
kable(variables_info, 
      caption = "Table of Variables and Data Types")
Table of Variables and Data Types
No Variable DataType Subtype
1 Number of vehicles passing through the toll road each day Numeric Continous
2 Student height in cm numeric continous
3 Employee gender (Male / Female) Categorical Nominal
4 Customer satisfaction level: Low, Medium, High Categorical Ordinal
5 Respondent’s favorite color: Red, Blue, Green Categorical Nominal

3 Exercise 3

Classify Data Sources: Determine whether the following data comes from internal or external sources, and whether it is structured or unstructured:

# 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",
    "External",
    "External",
    "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

4 Exercise 4

Dataset Structure: Consider the following transaction table:

Date Qty Price Product CustomerTier
2025-10-01 2 1000 Laptop High
2025-10-01 5 20 Mouse Medium
2025-10-02 1 1000 Laptop Low
2025-10-02 3 30 Keyboard Medium
2025-10-03 4 50 Mouse Medium
2025-10-03 2 1000 Laptop High
2025-10-04 6 25 Keyboard Low
2025-10-04 1 1000 Laptop High
2025-10-05 3 40 Mouse Low
2025-10-05 5 10 Keyboard Medium

Your Assignment Instructions: Creating a Transactions Table above in R

  1. Create a data frame in R called transactions containing the data above.

  2. Identify which variables are numeric and which are categorical

  3. Calculate total revenue for each transaction by multiplying Qty × Price and add it as a new column Total.

  4. Compute summary statistics:

    • Total quantity sold for each product
    • Total revenue per product
    • Average price per product

  5. Visualize the data:

    • Create a barplot showing total quantity sold per product.
    • Create a pie chart showing the proportion of total revenue per customer tier.

  6. Optional Challenge:

    • Find which date had the highest total revenue.
    • Create a stacked bar chart showing quantity sold per product by customer tier.

Hints: Use data.frame(), aggregate(), barplot(), pie(), and basic arithmetic operations in R.

library(DT)

#transaction
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")
Qty <- 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 <- c("High", "Medium", "Low", "Medium", "Medium", "High", 
                  "Low", "High", "Low", "Medium")

# data frame 
transactions <- data.frame(Date, Qty, Price, Product, CustomerTier)

# Display the data frame 
print(transactions)
##          Date Qty Price  Product CustomerTier
## 1  2025-10-01   2  1000   Laptop         High
## 2  2025-10-01   5    20    Mouse       Medium
## 3  2025-10-02   1  1000   Laptop          Low
## 4  2025-10-02   3    30 Keyboard       Medium
## 5  2025-10-03   4    50    Mouse       Medium
## 6  2025-10-03   2  1000   Laptop         High
## 7  2025-10-04   6    25 Keyboard          Low
## 8  2025-10-04   1  1000   Laptop         High
## 9  2025-10-05   3    40    Mouse          Low
## 10 2025-10-05   5    10 Keyboard       Medium
# 3 Total Reveneu
transactions$total <- transactions$Qty * transactions$Price

# view the data frame 
print(transactions)
##          Date Qty Price  Product CustomerTier total
## 1  2025-10-01   2  1000   Laptop         High  2000
## 2  2025-10-01   5    20    Mouse       Medium   100
## 3  2025-10-02   1  1000   Laptop          Low  1000
## 4  2025-10-02   3    30 Keyboard       Medium    90
## 5  2025-10-03   4    50    Mouse       Medium   200
## 6  2025-10-03   2  1000   Laptop         High  2000
## 7  2025-10-04   6    25 Keyboard          Low   150
## 8  2025-10-04   1  1000   Laptop         High  1000
## 9  2025-10-05   3    40    Mouse          Low   120
## 10 2025-10-05   5    10 Keyboard       Medium    50
qty_summary <- aggregate(Qty ~ Product, data = transactions, sum) 
print(qty_summary)
##    Product Qty
## 1 Keyboard  14
## 2   Laptop   6
## 3    Mouse  12
total_summary <- aggregate(total ~ Product, data = transactions, sum) 
print(total_summary)
##    Product total
## 1 Keyboard   290
## 2   Laptop  6000
## 3    Mouse   420
Price_summary <- aggregate(Price ~ Product, data = transactions, mean)
print(Price_summary)
##    Product      Price
## 1 Keyboard   21.66667
## 2   Laptop 1000.00000
## 3    Mouse   36.66667
# Calculate total quantity per product
qty_per_product <- aggregate(Qty ~ Product, data = transactions, sum)

# Create a bar plot 
barplot (qty_per_product$Qty,
         names.arg = qty_per_product$Product, 
         col = "orange", 
         main = "Total Quantity Sold per Product",
         xlab = "Product", ylab = "Quantity Sold")

# Calculate total revenue per CustomerTier
revenue_tier <- aggregate(total ~ CustomerTier, data = transactions, sum) 

# create pie chart 
pie(revenue_tier$tota, 
    labels = revenue_tier$CustomerTier, 
    main = "Proportion of Total Revenue per Customer Tier",
    col = c("red", "orange", "pink"))

# Hitung total revenue per tanggal
revenue_per_date <- aggregate(total ~ Date, data = transactions, sum)

# Lihat hasilnya
print(revenue_per_date)
##         Date total
## 1 2025-10-01  2100
## 2 2025-10-02  1090
## 3 2025-10-03  2200
## 4 2025-10-04  1150
## 5 2025-10-05   170
# Cari tanggal dengan total revenue paling tingi menggunakan max
max_date <- revenue_per_date[which.max(revenue_per_date$total), ]

#lihat hasilnya
print(max_date)
##         Date total
## 3 2025-10-03  2200
library(ggplot2)

# Bikin stacked bar chart
ggplot(transactions, aes(x = reorder(Product, Qty), y = Qty, fill = CustomerTier)) +
  geom_bar(stat = "identity") +
  labs(title = "Quantity Sold per Product by Customer Tier",
       x = "Product",
       y = "Quantity Sold") +
  theme_minimal()

5 Exercise 5

Create Your Own Data Frame:

Objective: Create a data frame in R with 30 rows containing a mix of data types: continuous, discrete, nominal, and ordinal.

5.1 Instructions

  1. Open RStudio or the R console.

  2. Create a vector for each column in your data frame:

    • Date: 30 dates (can be sequential or random within a month/year)
    • Continuous: numeric values that can take decimal values (e.g., height, weight, temperature)
    • Discrete: numeric values that can only take whole numbers (e.g., number of items, number of vehicles)
    • Nominal: categorical values with no order (e.g., color, gender, city)
    • Ordinal: categorical values with a defined order (e.g., Low, Medium, High; Beginner, Intermediate, Expert)

  3. Combine all vectors into a data frame called my_data.

  4. Check your data frame using head() or View() to ensure it has 30 rows and the columns are correct.

  5. Optional tasks:

    • Summarize each column using summary()
    • Count the frequency of each category for Nominal and Ordinal columns using table()

5.2 Hints

  • Use seq.Date() or as.Date() to generate the Date column.
  • Use runif() or rnorm() for continuous numeric data.
  • Use sample() for discrete, nominal, and ordinal data.
  • Ensure the ordinal vector is created with factor(..., levels = c("Low","Medium","High"), ordered = TRUE) (or similar).
library(DT)

# Data Financial Frame

# column 1: Transaction Date
Date <- seq.Date(from = as.Date("2025-10-01"), by = "day", length.out = 30)

# Column 2: Transaction value (Continuous) — In rupiah
TransactionValue <- c(
  1250000, 980000, 1500000, 2100000, 1850000, 750000, 2400000, 1300000, 1780000, 2220000,
  1950000, 1650000, 1420000, 2550000, 3100000, 2750000, 1980000, 880000, 1750000, 2200000,
  1900000, 2600000, 2450000, 1350000, 1500000, 900000, 1200000, 2750000, 3200000, 2100000
)

# Column 3: Number of items purchased (Discrete)
Items <- c(
  2, 1, 3, 5, 4, 2, 6, 3, 5, 4,
  2, 3, 1, 7, 8, 5, 3, 2, 4, 6,
  5, 7, 8, 3, 4, 1, 2, 7, 9, 5
)

# Column 4: Transaction type (Nominal)
TransactionType <- c(
  "Pembelian Tunai", "Transfer Bank", "Kartu Kredit", "Pembelian Tunai", "E-Wallet",
  "Transfer Bank", "Kartu Kredit", "E-Wallet", "Pembelian Tunai", "E-Wallet",
  "Kartu Kredit", "Pembelian Tunai", "Transfer Bank", "E-Wallet", "Kartu Kredit",
  "Pembelian Tunai", "E-Wallet", "Transfer Bank", "Kartu Kredit", "Pembelian Tunai",
  "E-Wallet", "Transfer Bank", "Kartu Kredit", "Pembelian Tunai", "E-Wallet",
  "Kartu Kredit", "Transfer Bank", "Pembelian Tunai", "E-Wallet", "Transfer Bank"
)

# Kolom 5: Customer Tier (Ordinal)
CustomerTier <- factor(
  c(
    "Sedang", "Rendah", "Sedang", "Tinggi", "Sedang", "Rendah", "Tinggi", "Sedang", "Tinggi", "Sedang",
    "Rendah", "Tinggi", "Sedang", "Tinggi", "Tinggi", "Sedang", "Rendah", "Sedang", "Tinggi", "Sedang",
    "Rendah", "Tinggi", "Tinggi", "Rendah", "Sedang", "Rendah", "Sedang", "Tinggi", "Tinggi", "Sedang"
  ),
  levels = c("Rendah", "Sedang", "Tinggi"),
  ordered = TRUE
)

# Combine into a data frame
data_financial <- data.frame(Date, TransactionValue, Items, TransactionType, CustomerTier)

# Display the data frame as a table
print(data_financial)
##          Date TransactionValue Items TransactionType CustomerTier
## 1  2025-10-01          1250000     2 Pembelian Tunai       Sedang
## 2  2025-10-02           980000     1   Transfer Bank       Rendah
## 3  2025-10-03          1500000     3    Kartu Kredit       Sedang
## 4  2025-10-04          2100000     5 Pembelian Tunai       Tinggi
## 5  2025-10-05          1850000     4        E-Wallet       Sedang
## 6  2025-10-06           750000     2   Transfer Bank       Rendah
## 7  2025-10-07          2400000     6    Kartu Kredit       Tinggi
## 8  2025-10-08          1300000     3        E-Wallet       Sedang
## 9  2025-10-09          1780000     5 Pembelian Tunai       Tinggi
## 10 2025-10-10          2220000     4        E-Wallet       Sedang
## 11 2025-10-11          1950000     2    Kartu Kredit       Rendah
## 12 2025-10-12          1650000     3 Pembelian Tunai       Tinggi
## 13 2025-10-13          1420000     1   Transfer Bank       Sedang
## 14 2025-10-14          2550000     7        E-Wallet       Tinggi
## 15 2025-10-15          3100000     8    Kartu Kredit       Tinggi
## 16 2025-10-16          2750000     5 Pembelian Tunai       Sedang
## 17 2025-10-17          1980000     3        E-Wallet       Rendah
## 18 2025-10-18           880000     2   Transfer Bank       Sedang
## 19 2025-10-19          1750000     4    Kartu Kredit       Tinggi
## 20 2025-10-20          2200000     6 Pembelian Tunai       Sedang
## 21 2025-10-21          1900000     5        E-Wallet       Rendah
## 22 2025-10-22          2600000     7   Transfer Bank       Tinggi
## 23 2025-10-23          2450000     8    Kartu Kredit       Tinggi
## 24 2025-10-24          1350000     3 Pembelian Tunai       Rendah
## 25 2025-10-25          1500000     4        E-Wallet       Sedang
## 26 2025-10-26           900000     1    Kartu Kredit       Rendah
## 27 2025-10-27          1200000     2   Transfer Bank       Sedang
## 28 2025-10-28          2750000     7 Pembelian Tunai       Tinggi
## 29 2025-10-29          3200000     9        E-Wallet       Tinggi
## 30 2025-10-30          2100000     5   Transfer Bank       Sedang
# Create a second table for variable type
variables_info <- data.frame(
  No = 1:4,
  variable = c(
    "Transaction Value",
    "Items",
    "Transaction Type",
    "Customer Tier"
),
  DataType = c(
      "Continuous",
      "Discrete",
      "Nominal",
      "Ordinal"
    ),
    stringsAsFactors = FALSE
  )

library(knitr)

kable(
  variables_info,
  caption = "Table of Variables and Data Types"
)
Table of Variables and Data Types
No variable DataType
1 Transaction Value Continuous
2 Items Discrete
3 Transaction Type Nominal
4 Customer Tier Ordinal
---
title: "Data Exploration"       # Main title of the document
subtitle: "Exercises ~ Week 2"  # Subtitle or topic for week 2
author: "kelompok 3 - Naifah Edria Arta(52250056), Frizzy Lithmensyah(52250062), Lulu Najla Salsabila(52250069), Naila Syahrani Putri(52250070), Ni. MD Aurora Sekarningrum(52250072)"
date:  "`r format(Sys.Date(), '%B %d, %Y')`" # Auto displays the current date
output:                         # Output section defines the format and layout 
  rmdformats::readthedown:      # https://github.com/juba/rmdformats
    self_contained: true        # Embeds all resources (CSS, JS, images) 
    thumbnails: true            # Displays image thumbnails in the doc
    lightbox: true              # Enables click to enlarge images
    gallery: true               # Groups images into an interactive gallery
    number_sections: true       # Automatically numbers all sections
    lib_dir: libs               # Directory where JavaScript/CSS libraries
    df_print: "paged"           # Displays data frames as interactive paged 
    code_folding: "show"        # Allows folding/unfolding R code blocks 
    code_download: yes          # Adds a button to download all R code
---


<img id="Foto" src="https://github.com/dsciencelabs/images/blob/master/Logo_Dsciencelabs_v1.png?raw=true" alt="Logo" style="width:200px; display: block; margin: auto;">

---

## Exercise 1

The following table shows sample information for three students. Each observation represents a single student and includes details such as their unique student ID, name, age, total credits completed, major field of study, and year level.  

This dataset demonstrates a mixture of variable types:  

- **Nominal:** StudentID, Name, Major  
- **Numeric:** Age (continuous), CreditsCompleted (discrete)  
- **Ordinal:** YearLevel (Freshman → Senior)  

| StudentID | Name   | Age | CreditsCompleted | Major            | YearLevel |
|-----------|--------|-----|-----------------|-----------------|-----------|
| S001      | Alice  | 20  | 45              | Data Sains      | Sophomore |
| S002      | Budi   | 21  | 60              | Mathematics     | Junior    |
| S003      | Citra  | 19  | 30              | Statistics      | Freshman  |

```{r}
# 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)
```


## Exercise 2

**Identify Data Types:** Determine the type of data for each of the following variables:

```{r}
# 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(
    "Numeric",
    "numeric",
    "Categorical",
    "Categorical",
    "Categorical"
  ),
  Subtype = c(
    "Continous",
    "continous",
    "Nominal",
    "Ordinal",
    "Nominal"
  ),
  stringsAsFactors = FALSE
)

# Display the data frame as a neat table
kable(variables_info, 
      caption = "Table of Variables and Data Types")
```
---

## Exercise 3

**Classify Data Sources:** Determine whether the following data comes from **internal** or **external sources**, and whether it is **structured** or **unstructured**:

```{r}
# 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",
    "External",
    "External",
    "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
```

---

## Exercise 4

**Dataset Structure:** Consider the following transaction table:

| Date       | Qty | Price | Product  | CustomerTier |
|------------|-----|-------|----------|--------------|
| 2025-10-01 | 2   | 1000  | Laptop   | High         |
| 2025-10-01 | 5   | 20    | Mouse    | Medium       |
| 2025-10-02 | 1   | 1000  | Laptop   | Low          |
| 2025-10-02 | 3   | 30    | Keyboard | Medium       |
| 2025-10-03 | 4   | 50    | Mouse    | Medium       |
| 2025-10-03 | 2   | 1000  | Laptop   | High         |
| 2025-10-04 | 6   | 25    | Keyboard | Low          |
| 2025-10-04 | 1   | 1000  | Laptop   | High         |
| 2025-10-05 | 3   | 40    | Mouse    | Low          |
| 2025-10-05 | 5   | 10    | Keyboard | Medium       |


**Your Assignment Instructions:** Creating a Transactions Table above in R

1. **Create a data frame** in R called `transactions` containing the data above.

2. Identify which variables are numeric and which are categorical

3. **Calculate total revenue** for each transaction by multiplying `Qty × Price` and add it as a new column `Total`.

4. **Compute summary statistics**:
   - Total quantity sold for each product
   - Total revenue per product
   - Average price per product

5. **Visualize the data**:
   - Create a **barplot** showing total quantity sold per product.
   - Create a **pie chart** showing the proportion of total revenue per customer tier.

6. **Optional Challenge**:
   - Find which **date** had the highest total revenue.
   - Create a **stacked bar chart** showing quantity sold per product by customer tier.

**Hints:** Use `data.frame()`, `aggregate()`, `barplot()`, `pie()`, and basic arithmetic operations in R.


``` {r}
library(DT)

#transaction
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")
Qty <- 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 <- c("High", "Medium", "Low", "Medium", "Medium", "High", 
                  "Low", "High", "Low", "Medium")

# data frame 
transactions <- data.frame(Date, Qty, Price, Product, CustomerTier)

# Display the data frame 
print(transactions)

# 3 Total Reveneu
transactions$total <- transactions$Qty * transactions$Price

# view the data frame 
print(transactions)

qty_summary <- aggregate(Qty ~ Product, data = transactions, sum) 
print(qty_summary)
total_summary <- aggregate(total ~ Product, data = transactions, sum) 
print(total_summary)
Price_summary <- aggregate(Price ~ Product, data = transactions, mean)
print(Price_summary)

# Calculate total quantity per product
qty_per_product <- aggregate(Qty ~ Product, data = transactions, sum)

# Create a bar plot 
barplot (qty_per_product$Qty,
         names.arg = qty_per_product$Product, 
         col = "orange", 
         main = "Total Quantity Sold per Product",
         xlab = "Product", ylab = "Quantity Sold")

# Calculate total revenue per CustomerTier
revenue_tier <- aggregate(total ~ CustomerTier, data = transactions, sum) 

# create pie chart 
pie(revenue_tier$tota, 
    labels = revenue_tier$CustomerTier, 
    main = "Proportion of Total Revenue per Customer Tier",
    col = c("red", "orange", "pink"))



# Hitung total revenue per tanggal
revenue_per_date <- aggregate(total ~ Date, data = transactions, sum)

# Lihat hasilnya
print(revenue_per_date)

# Cari tanggal dengan total revenue paling tingi menggunakan max
max_date <- revenue_per_date[which.max(revenue_per_date$total), ]

#lihat hasilnya
print(max_date)

library(ggplot2)

# Bikin stacked bar chart
ggplot(transactions, aes(x = reorder(Product, Qty), y = Qty, fill = CustomerTier)) +
  geom_bar(stat = "identity") +
  labs(title = "Quantity Sold per Product by Customer Tier",
       x = "Product",
       y = "Quantity Sold") +
  theme_minimal()
```



## Exercise 5

**Create Your Own Data Frame:**

**Objective:** Create a data frame in R with **30 rows** containing a mix of data types: continuous, discrete, nominal, and ordinal.  

### Instructions

1. **Open RStudio** or the R console.  

2. **Create a vector for each column** in your data frame:  

   - **Date**: 30 dates (can be sequential or random within a month/year)  
   - **Continuous**: numeric values that can take decimal values (e.g., height, weight, temperature)  
   - **Discrete**: numeric values that can only take whole numbers (e.g., number of items, number of vehicles)  
   - **Nominal**: categorical values with **no order** (e.g., color, gender, city)  
   - **Ordinal**: categorical values with a **defined order** (e.g., Low, Medium, High; Beginner, Intermediate, Expert)  

3. **Combine all vectors into a data frame** called `my_data`.  

4. **Check your data frame** using `head()` or `View()` to ensure it has **30 rows** and the columns are correct.  

5. **Optional tasks**:  
   - Summarize each column using `summary()`  
   - Count the frequency of each category for **Nominal** and **Ordinal** columns using `table()`  

### Hints

- Use `seq.Date()` or `as.Date()` to generate the Date column.  
- Use `runif()` or `rnorm()` for continuous numeric data.  
- Use `sample()` for discrete, nominal, and ordinal data.  
- Ensure the **ordinal vector** is created with `factor(..., levels = c("Low","Medium","High"), ordered = TRUE)` (or similar).  
``` {r}
library(DT)

# Data Financial Frame

# column 1: Transaction Date
Date <- seq.Date(from = as.Date("2025-10-01"), by = "day", length.out = 30)

# Column 2: Transaction value (Continuous) — In rupiah
TransactionValue <- c(
  1250000, 980000, 1500000, 2100000, 1850000, 750000, 2400000, 1300000, 1780000, 2220000,
  1950000, 1650000, 1420000, 2550000, 3100000, 2750000, 1980000, 880000, 1750000, 2200000,
  1900000, 2600000, 2450000, 1350000, 1500000, 900000, 1200000, 2750000, 3200000, 2100000
)

# Column 3: Number of items purchased (Discrete)
Items <- c(
  2, 1, 3, 5, 4, 2, 6, 3, 5, 4,
  2, 3, 1, 7, 8, 5, 3, 2, 4, 6,
  5, 7, 8, 3, 4, 1, 2, 7, 9, 5
)

# Column 4: Transaction type (Nominal)
TransactionType <- c(
  "Pembelian Tunai", "Transfer Bank", "Kartu Kredit", "Pembelian Tunai", "E-Wallet",
  "Transfer Bank", "Kartu Kredit", "E-Wallet", "Pembelian Tunai", "E-Wallet",
  "Kartu Kredit", "Pembelian Tunai", "Transfer Bank", "E-Wallet", "Kartu Kredit",
  "Pembelian Tunai", "E-Wallet", "Transfer Bank", "Kartu Kredit", "Pembelian Tunai",
  "E-Wallet", "Transfer Bank", "Kartu Kredit", "Pembelian Tunai", "E-Wallet",
  "Kartu Kredit", "Transfer Bank", "Pembelian Tunai", "E-Wallet", "Transfer Bank"
)

# Kolom 5: Customer Tier (Ordinal)
CustomerTier <- factor(
  c(
    "Sedang", "Rendah", "Sedang", "Tinggi", "Sedang", "Rendah", "Tinggi", "Sedang", "Tinggi", "Sedang",
    "Rendah", "Tinggi", "Sedang", "Tinggi", "Tinggi", "Sedang", "Rendah", "Sedang", "Tinggi", "Sedang",
    "Rendah", "Tinggi", "Tinggi", "Rendah", "Sedang", "Rendah", "Sedang", "Tinggi", "Tinggi", "Sedang"
  ),
  levels = c("Rendah", "Sedang", "Tinggi"),
  ordered = TRUE
)

# Combine into a data frame
data_financial <- data.frame(Date, TransactionValue, Items, TransactionType, CustomerTier)

# Display the data frame as a table
print(data_financial)

# Create a second table for variable type
variables_info <- data.frame(
  No = 1:4,
  variable = c(
    "Transaction Value",
    "Items",
    "Transaction Type",
    "Customer Tier"
),
  DataType = c(
      "Continuous",
      "Discrete",
      "Nominal",
      "Ordinal"
    ),
    stringsAsFactors = FALSE
  )

library(knitr)

kable(
  variables_info,
  caption = "Table of Variables and Data Types"
)
```


