Data Exploration

Exercises ~ Week 3

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(
    "Discrete",
    "Continuous",
    "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	Discrete
2	Student height in cm	Numeric	Continuous
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

Create a data frame in R called transactions containing the data above.
Identify which variables are numeric and which are categorical
Calculate total revenue for each transaction by multiplying Qty × Price and add it as a new column Total.
Compute summary statistics:
- Total quantity sold for each product
- Total revenue per product
- Average price per product
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.
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.

# Create a data frame for Transactions
library(knitr)
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 = factor(c(
  "High",
  "Medium",
  "Low",
  "Medium",
  "Medium",
  "High",
  "Low",
  "High",
  "Low",
  "Medium"
  ),
levels = c("Low","Medium","High"),
ordered = TRUE) 
transactions <-  data.frame (Date, Qty, Price, Product, CustomerTier, stringsAsFactors = FALSE)

# Display the data frame
library(knitr)
kable(transactions, caption = "Data Transactions")

Data 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"))

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

Open RStudio or the R console.
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)
Combine all vectors into a data frame called my_data.
Check your data frame using head() or View() to ensure it has 30 rows and the columns are correct.
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
library(knitr)
Date = seq(as.Date("2025-09-01"), as.Date("2025-09-30"), by = "day")    # Date
Coffee_ml = runif(30, min = 1500, max = 4000)             # Continuous / Volume Kopi
Cups_Sold = sample(20:100, 30, replace = TRUE)            # Discrete / Jumlah Kopi
Drink_Type = sample(c(
  "Americano",
  "Cappuccino",
  "Latte",
  "Espresso",
  "Mocha"), 30, replace = TRUE)                           # Nominal / Jenis Kopi
Customer_Satisfaction = factor(
  sample(c(
    "Poor",
    "Fair",
    "Good",
    "Very Good",
    "Excellent"), 30, replace = TRUE),
  levels = c("Poor", "Fair", "Good", "Very Good", "Excellent"),
  ordered = TRUE)                                         # Ordinal / Satisfaction

# 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	2119.589	82	Espresso	Poor
2025-09-02	1652.511	41	Americano	Fair
2025-09-03	3538.228	82	Americano	Very Good
2025-09-04	3596.254	75	Espresso	Fair
2025-09-05	2717.580	41	Mocha	Poor
2025-09-06	2622.993	100	Americano	Good
2025-09-07	3579.724	65	Cappuccino	Excellent
2025-09-08	2051.191	67	Americano	Poor
2025-09-09	3702.286	90	Latte	Very Good
2025-09-10	2436.549	74	Espresso	Poor
2025-09-11	1800.542	24	Espresso	Excellent
2025-09-12	2910.268	36	Cappuccino	Very Good
2025-09-13	2582.743	76	Espresso	Good
2025-09-14	1640.772	46	Mocha	Good
2025-09-15	3080.980	56	Mocha	Very Good
2025-09-16	2903.017	35	Espresso	Very Good
2025-09-17	2286.498	28	Americano	Fair
2025-09-18	3331.012	56	Espresso	Fair
2025-09-19	3655.258	83	Americano	Good
2025-09-20	2286.588	20	Americano	Excellent
2025-09-21	2555.764	63	Cappuccino	Poor
2025-09-22	3392.277	49	Espresso	Good
2025-09-23	3083.719	40	Espresso	Good
2025-09-24	3162.052	93	Mocha	Very Good
2025-09-25	3733.539	56	Mocha	Good
2025-09-26	3367.869	89	Espresso	Excellent
2025-09-27	3921.344	68	Mocha	Excellent
2025-09-28	2545.634	86	Mocha	Good
2025-09-29	2542.456	100	Americano	Fair
2025-09-30	2936.739	97	Americano	Very Good

# 2. 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. :1641	Min. : 20.00	Length:30	Poor :5
1st Qu.:2025-09-08	1st Qu.:2463	1st Qu.: 42.25	Class :character	Fair :5
Median :2025-09-15	Median :2907	Median : 66.00	Mode :character	Good :8
Mean :2025-09-15	Mean :2858	Mean : 63.93	NA	Very Good:7
3rd Qu.:2025-09-22	3rd Qu.:3386	3rd Qu.: 82.75	NA	Excellent:5
Max. :2025-09-30	Max. :3921	Max. :100.00	NA	NA

# 3. Frekuensi Kategori (nominal, ordinal)
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	9
Cappuccino	3
Espresso	10
Latte	1
Mocha	7

kable(satisfaction_freq, caption = "Frekuensi Kepuasan Pelanggan (Ordinal)")

Frekuensi Kepuasan Pelanggan (Ordinal)
Var1	Freq
Poor	5
Fair	5
Good	8
Very Good	7
Excellent	5

---
title: "Data Exploration"           # Main title of the document
subtitle: "Exercises ~ Week 3"      # Subtitle or topic for week 3
author:                             # Replace with your full name
- "Angelique Kiyoshi Lakeisha B.U"
- "Khafizatun Nisa" 
- "Naychilla Adelia Zahrah"
- "Nakeisha Aulia Zahra"
- "Veronica L F Xavier"
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="gurlspict.jpeg?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(
    "Discrete",
    "Continuous",
    "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}
# Create a data frame for Transactions
library(knitr)
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 = factor(c(
  "High",
  "Medium",
  "Low",
  "Medium",
  "Medium",
  "High",
  "Low",
  "High",
  "Low",
  "Medium"
  ),
levels = c("Low","Medium","High"),
ordered = TRUE) 
transactions <-  data.frame (Date, Qty, Price, Product, CustomerTier, stringsAsFactors = FALSE)

# Display the data frame
library(knitr)
kable(transactions, caption = "Data 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"))
      
```

## 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
library(knitr)
Date = seq(as.Date("2025-09-01"), as.Date("2025-09-30"), by = "day")    # Date
Coffee_ml = runif(30, min = 1500, max = 4000)             # Continuous / Volume Kopi
Cups_Sold = sample(20:100, 30, replace = TRUE)            # Discrete / Jumlah Kopi
Drink_Type = sample(c(
  "Americano",
  "Cappuccino",
  "Latte",
  "Espresso",
  "Mocha"), 30, replace = TRUE)                           # Nominal / Jenis Kopi
Customer_Satisfaction = factor(
  sample(c(
    "Poor",
    "Fair",
    "Good",
    "Very Good",
    "Excellent"), 30, replace = TRUE),
  levels = c("Poor", "Fair", "Good", "Very Good", "Excellent"),
  ordered = TRUE)                                         # Ordinal / Satisfaction

# Combine all vectors into a data frame
my_data = data.frame(Date, Coffee_ml, Cups_Sold, Drink_Type, Customer_Satisfaction)
kable(my_data)

# 2. Summary data (opsional)
summary_data = summary(my_data)
kable(summary_data)

# 3. Frekuensi Kategori (nominal, ordinal)
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)")

```

