Data Exploration

Exercises ~ Week 3

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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(
    "numerik",
    "numerik",
    "kategorikal",
    "kategorikal",
    "kategorikal"
  ),
  Subtype = c(
    "diskrit",
    "kontinu",
    "nomilal",
    "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 numerik diskrit
2 Student height in cm numerik kontinu
3 Employee gender (Male / Female) kategorikal nomilal
4 Customer satisfaction level: Low, Medium, High kategorikal ordinal
5 Respondent’s favorite color: Red, Blue, Green kategorikal 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(
    "terstruktur",
    "terstuktur",
    "tidak terstuktur",
    "terstruktur"
  ),
  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
# Install knitr package if not already installed
# install.packages("knitr")
library(knitr)

# Create a data frame for Transactions
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"
  )              # Numeric / Discrete
Qty = c (2, 5, 1, 3, 4, 2, 6, 1, 3, 5)   # Numeric / Discrete
Price = c (1000, 20, 1000, 30, 50, 1000, 25, 1000, 40, 10)   # Numeric / Discrete

# Nominal
Product = c (
  "Laptop",
  "Mouse",
  "Laptop",
  "Keyboard",
  "Mouse",
  "Laptop",
  "Keyboard",
  "Laptop",
  "Mouse",
  "Keyboard"
  )           # Categorical / Nominal

# Ordinal
CustomerTier = factor(c(
  "High",
  "Medium",
  "Low",
  "Medium",
  "Medium",
  "High",
  "Low",
  "High",
  "Low",
  "Medium"
  ),
levels = c("Low","Medium","High"),
ordered = TRUE) 

# 2. Combine all vectors into a data frame
transactions =  data.frame (Date, Qty, Price, Product, CustomerTier, stringsAsFactors = FALSE)

# 3. Display the data frame
library(knitr)
kable(transactions)
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 
# 2. Identify variable types
library(knitr)
variable_types <- data.frame(
  Variable = c(
    "Date",
    "Qty",
    "Price",
    "Product",
    "CustomerTier"
    ),
  Type = c(
    "Numeric (Discrete)",
    "Numeric (Discrete)",
    "Numeric (Discrete)",
    "Categorical (Nominal)",
    "Categorical (Ordinal)"
    )
)

# Display the data frame
kable(variable_types, caption = "Variable Types in Transactions Data")
Variable Types in Transactions Data
Variable Type
Date Numeric (Discrete)
Qty Numeric (Discrete)
Price Numeric (Discrete)
Product Categorical (Nominal)
CustomerTier Categorical (Ordinal) 
# 3. Calculate total revenue
transactions$total = transactions$Qty * transactions$Price
kable(transactions)
Date Qty Price Product CustomerTier total
2025-10-01 2 1000 Laptop High 2000
2025-10-01 5 20 Mouse Medium 100
2025-10-02 1 1000 Laptop Low 1000
2025-10-02 3 30 Keyboard Medium 90
2025-10-03 4 50 Mouse Medium 200
2025-10-03 2 1000 Laptop High 2000
2025-10-04 6 25 Keyboard Low 150
2025-10-04 1 1000 Laptop High 1000
2025-10-05 3 40 Mouse Low 120
2025-10-05 5 10 Keyboard Medium 50 
# 4. Compute summary statistic

# a. Total quantity sold for each product
total_Qty = aggregate(Qty ~ Product, data = transactions, sum)
kable(total_Qty, caption = "Total Quantity Sold per Product")
Total Quantity Sold per Product
Product Qty
Keyboard 14
Laptop 6
Mouse 12 
# b. total revenue per product
total_revenue = aggregate(total ~ Product, data = transactions, sum)
kable(total_revenue, caption = "Total Revenue per Product")
Total Revenue per Product
Product total
Keyboard 290
Laptop 6000
Mouse 420 
# c. Average price per product
avg_price = aggregate(Price ~ Product, data = transactions, mean)
kable(avg_price, caption = "Average Price per Product")
Average Price per Product
Product Price
Keyboard 21.66667
Laptop 1000.00000
Mouse 36.66667 
# 5. Visualize the data

# a. barplot showing total quantity sold per product
total_qty = tapply(transactions$Qty, transactions$Product, sum)
barplot(total_qty,
        main = "Total Quantity Sold per Product",
        xlab = "Product",
        ylab = "Total Quantity",
        col = "lightblue")

# b. pie chart showing the proportion of total revenue
total_revenue_tier = tapply(transactions$total, transactions$CustomerTier, sum)
pie(total_revenue_tier,
    main = "Proportion of Total Revenue per Customer Tier",
    col = rainbow(length(total_revenue_tier)))

# 6. Optional challenge

# a. Find which date had the highest total revenue
total_revenue_date <- aggregate(total ~ Date, data = transactions, sum)
total_revenue_date[which.max(total_revenue_date$total), ]
# b. stacked bar chart showing quantity sold per product by customer tier
qty_table = xtabs(Qty ~ Product + CustomerTier, data = transactions)
barplot(qty_table,
        main = "Quantity Sold per Product by Customer Tier",
        xlab = "Product",
        ylab = "Quantity",
        col = c("lightblue", "lightgreen", "pink"))

  1. 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.

  2. 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.

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).
# 1. Coffee Shop Data

# Date (30 hari di bulan September)
Date = seq(as.Date("2025-09-01"), as.Date("2025-09-30"), by = "day")

# Continuous: jumlah ml kopi terjual per hari (acak dari 1500–4000 ml)
Coffee_ml = runif(30, min = 1500, max = 4000)

# Discrete: jumlah cangkir kopi terjual per hari (acak 20–100)
Cups_Sold = sample(20:100, 30, replace = TRUE)

# Nominal: jenis minuman kopi
Drink_Type = sample(c("Americano", "Cappuccino", "Latte", "Espresso", "Mocha"), 30, replace = TRUE)

# Ordinal: tingkat kepuasan pelanggan
Customer_Satisfaction = factor(
  sample(c("Poor", "Fair", "Good", "Very Good", "Excellent"), 30, replace = TRUE),
  levels = c("Poor", "Fair", "Good", "Very Good", "Excellent"),
  ordered = TRUE)

# Combine all vectors into a data frame
my_data = data.frame(Date, Coffee_ml, Cups_Sold, Drink_Type, Customer_Satisfaction)
kable(my_data)
Date Coffee_ml Cups_Sold Drink_Type Customer_Satisfaction
2025-09-01 3064.949 26 Espresso Excellent
2025-09-02 1610.821 70 Mocha Fair
2025-09-03 1770.798 99 Cappuccino Very Good
2025-09-04 3094.826 92 Espresso Very Good
2025-09-05 1866.469 40 Mocha Excellent
2025-09-06 2054.750 64 Latte Fair
2025-09-07 2436.079 92 Latte Excellent
2025-09-08 2429.349 22 Americano Good
2025-09-09 3086.462 49 Americano Excellent
2025-09-10 2523.109 91 Cappuccino Excellent
2025-09-11 1800.869 63 Espresso Very Good
2025-09-12 1506.859 99 Cappuccino Poor
2025-09-13 2754.235 40 Mocha Very Good
2025-09-14 1774.955 50 Americano Very Good
2025-09-15 2336.350 34 Cappuccino Good
2025-09-16 3263.853 57 Mocha Good
2025-09-17 2013.145 72 Americano Very Good
2025-09-18 3642.700 31 Cappuccino Very Good
2025-09-19 2488.838 26 Latte Fair
2025-09-20 3859.804 78 Espresso Good
2025-09-21 3300.900 82 Mocha Good
2025-09-22 3284.224 22 Cappuccino Fair
2025-09-23 3597.779 91 Latte Fair
2025-09-24 2131.266 29 Latte Good
2025-09-25 2531.294 32 Latte Good
2025-09-26 1587.727 43 Mocha Excellent
2025-09-27 3192.204 88 Latte Fair
2025-09-28 2169.073 83 Latte Poor
2025-09-29 3391.213 44 Americano Excellent
2025-09-30 1531.602 59 Latte Poor 
# Summary data (opsional)
summary_data <- summary(my_data)
kable(summary_data)
Date Coffee_ml Cups_Sold Drink_Type Customer_Satisfaction
Min. :2025-09-01 Min. :1507 Min. :22.00 Length:30 Poor :3
1st Qu.:2025-09-08 1st Qu.:1903 1st Qu.:35.50 Class :character Fair :6
Median :2025-09-15 Median :2462 Median :58.00 Mode :character Good :7
Mean :2025-09-15 Mean :2537 Mean :58.93 NA Very Good:7
3rd Qu.:2025-09-22 3rd Qu.:3168 3rd Qu.:82.75 NA Excellent:7
Max. :2025-09-30 Max. :3860 Max. :99.00 NA NA 
# Frekuensi Kategori (nominal, ordinal)
library(knitr)
drink_freq <- table(my_data$Drink_Type)
satisfaction_freq <- table(my_data$Customer_Satisfaction)

kable(drink_freq, caption = "Frekuensi Jenis Minuman (Nominal)")
Frekuensi Jenis Minuman (Nominal)
Var1 Freq
Americano 5
Cappuccino 6
Espresso 4
Latte 9
Mocha 6 
kable(satisfaction_freq, caption = "Frekuensi Kepuasan Pelanggan (Ordinal)")
Frekuensi Kepuasan Pelanggan (Ordinal)
Var1 Freq
Poor 3
Fair 6
Good 7
Very Good 7
Excellent 7
---
title: "Data Exploration"       # Main title of the document
subtitle: "Exercises ~ Week 3"  # Subtitle or topic for week 2
author: 
- "Nakeisha Aulia Zahra" 
- "Angelique Kiyoshi Lakeisha BU" 
- "Khafizatun Nisa" 
- "Naychilla Adelia Zahrah" 
- "Veronica Maria LF Xavier" # Replace with your full name

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="C:/Users/USER/OneDrive/Desktop/Tugas Statistika R/Red Blue Modern Abstract Scrapbook Teamwork Moment Photo Collage.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(
    "numerik",
    "numerik",
    "kategorikal",
    "kategorikal",
    "kategorikal"
  ),
  Subtype = c(
    "diskrit",
    "kontinu",
    "nomilal",
    "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(
    "terstruktur",
    "terstuktur",
    "tidak terstuktur",
    "terstruktur"
  ),
  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
```{r}
# Install knitr package if not already installed
# install.packages("knitr")
library(knitr)

# Create a data frame for Transactions
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"
  )              # Numeric / Discrete
Qty = c (2, 5, 1, 3, 4, 2, 6, 1, 3, 5)   # Numeric / Discrete
Price = c (1000, 20, 1000, 30, 50, 1000, 25, 1000, 40, 10)   # Numeric / Discrete

# Nominal
Product = c (
  "Laptop",
  "Mouse",
  "Laptop",
  "Keyboard",
  "Mouse",
  "Laptop",
  "Keyboard",
  "Laptop",
  "Mouse",
  "Keyboard"
  )           # Categorical / Nominal

# Ordinal
CustomerTier = factor(c(
  "High",
  "Medium",
  "Low",
  "Medium",
  "Medium",
  "High",
  "Low",
  "High",
  "Low",
  "Medium"
  ),
levels = c("Low","Medium","High"),
ordered = TRUE) 

# 2. Combine all vectors into a data frame
transactions =  data.frame (Date, Qty, Price, Product, CustomerTier, stringsAsFactors = FALSE)

# 3. Display the data frame
library(knitr)
kable(transactions)
```

```{r}
# 2. Identify variable types
library(knitr)
variable_types <- data.frame(
  Variable = c(
    "Date",
    "Qty",
    "Price",
    "Product",
    "CustomerTier"
    ),
  Type = c(
    "Numeric (Discrete)",
    "Numeric (Discrete)",
    "Numeric (Discrete)",
    "Categorical (Nominal)",
    "Categorical (Ordinal)"
    )
)

# Display the data frame
kable(variable_types, caption = "Variable Types in Transactions Data")

```

```{r}
# 3. Calculate total revenue
transactions$total = transactions$Qty * transactions$Price
kable(transactions)
```


```{r}
# 4. Compute summary statistic

# a. Total quantity sold for each product
total_Qty = aggregate(Qty ~ Product, data = transactions, sum)
kable(total_Qty, caption = "Total Quantity Sold per Product")

# b. total revenue per product
total_revenue = aggregate(total ~ Product, data = transactions, sum)
kable(total_revenue, caption = "Total Revenue per Product")

# c. Average price per product
avg_price = aggregate(Price ~ Product, data = transactions, mean)
kable(avg_price, caption = "Average Price per Product")
```


```{r}
# 5. Visualize the data

# a. barplot showing total quantity sold per product
total_qty = tapply(transactions$Qty, transactions$Product, sum)
barplot(total_qty,
        main = "Total Quantity Sold per Product",
        xlab = "Product",
        ylab = "Total Quantity",
        col = "lightblue")

# b. pie chart showing the proportion of total revenue
total_revenue_tier = tapply(transactions$total, transactions$CustomerTier, sum)
pie(total_revenue_tier,
    main = "Proportion of Total Revenue per Customer Tier",
    col = rainbow(length(total_revenue_tier)))
```


```{r}
# 6. Optional challenge

# a. Find which date had the highest total revenue
total_revenue_date <- aggregate(total ~ Date, data = transactions, sum)
total_revenue_date[which.max(total_revenue_date$total), ]


# b. stacked bar chart showing quantity sold per product by customer tier
qty_table = xtabs(Qty ~ Product + CustomerTier, data = transactions)
barplot(qty_table,
        main = "Quantity Sold per Product by Customer Tier",
        xlab = "Product",
        ylab = "Quantity",
        col = c("lightblue", "lightgreen", "pink"))
```


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.


## 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}
# 1. Coffee Shop Data

# Date (30 hari di bulan September)
Date = seq(as.Date("2025-09-01"), as.Date("2025-09-30"), by = "day")

# Continuous: jumlah ml kopi terjual per hari (acak dari 1500–4000 ml)
Coffee_ml = runif(30, min = 1500, max = 4000)

# Discrete: jumlah cangkir kopi terjual per hari (acak 20–100)
Cups_Sold = sample(20:100, 30, replace = TRUE)

# Nominal: jenis minuman kopi
Drink_Type = sample(c("Americano", "Cappuccino", "Latte", "Espresso", "Mocha"), 30, replace = TRUE)

# Ordinal: tingkat kepuasan pelanggan
Customer_Satisfaction = factor(
  sample(c("Poor", "Fair", "Good", "Very Good", "Excellent"), 30, replace = TRUE),
  levels = c("Poor", "Fair", "Good", "Very Good", "Excellent"),
  ordered = TRUE)

# Combine all vectors into a data frame
my_data = data.frame(Date, Coffee_ml, Cups_Sold, Drink_Type, Customer_Satisfaction)
kable(my_data)

# Summary data (opsional)
summary_data <- summary(my_data)
kable(summary_data)

# Frekuensi Kategori (nominal, ordinal)
library(knitr)
drink_freq <- table(my_data$Drink_Type)
satisfaction_freq <- table(my_data$Customer_Satisfaction)

kable(drink_freq, caption = "Frekuensi Jenis Minuman (Nominal)")
kable(satisfaction_freq, caption = "Frekuensi Kepuasan Pelanggan (Ordinal)")

```
