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",
    "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	Discrete
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

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.

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.

# 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"))

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

# 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	3243.048	67	Espresso	Good
2025-09-02	2114.439	83	Espresso	Poor
2025-09-03	2893.236	85	Espresso	Excellent
2025-09-04	3023.221	67	Latte	Good
2025-09-05	2185.550	100	Latte	Poor
2025-09-06	1871.067	98	Espresso	Fair
2025-09-07	1509.224	45	Cappuccino	Fair
2025-09-08	2703.869	58	Cappuccino	Good
2025-09-09	1840.767	77	Latte	Poor
2025-09-10	2875.354	97	Latte	Fair
2025-09-11	1816.171	74	Mocha	Fair
2025-09-12	1656.236	88	Cappuccino	Good
2025-09-13	3003.934	86	Latte	Poor
2025-09-14	3980.280	78	Latte	Excellent
2025-09-15	1805.588	95	Mocha	Excellent
2025-09-16	3406.042	66	Latte	Poor
2025-09-17	3751.245	27	Americano	Good
2025-09-18	3262.385	72	Americano	Poor
2025-09-19	2131.619	94	Americano	Fair
2025-09-20	3426.469	92	Espresso	Excellent
2025-09-21	3402.881	99	Mocha	Good
2025-09-22	1712.093	59	Mocha	Fair
2025-09-23	3672.968	60	Americano	Good
2025-09-24	3100.494	29	Americano	Fair
2025-09-25	2625.981	41	Mocha	Excellent
2025-09-26	1730.989	76	Cappuccino	Fair
2025-09-27	3134.336	51	Cappuccino	Fair
2025-09-28	3026.545	40	Latte	Very Good
2025-09-29	3222.764	23	Espresso	Very Good
2025-09-30	3555.928	30	Latte	Excellent

# 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. :1509	Min. : 23.00	Length:30	Poor :6
1st Qu.:2025-09-08	1st Qu.:1932	1st Qu.: 52.75	Class :character	Fair :9
Median :2025-09-15	Median :2949	Median : 73.00	Mode :character	Good :7
Mean :2025-09-15	Mean :2723	Mean : 68.57	NA	Very Good:2
3rd Qu.:2025-09-22	3rd Qu.:3258	3rd Qu.: 87.50	NA	Excellent:6
Max. :2025-09-30	Max. :3980	Max. :100.00	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	5
Espresso	6
Latte	9
Mocha	5

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

Frekuensi Kepuasan Pelanggan (Ordinal)
Var1	Freq
Poor	6
Fair	9
Good	7
Very Good	2
Excellent	6

---
title: "Data Exploration"       # Main title of the document
subtitle: "Exercises ~ Week 3"  # Subtitle or topic for week 2
author:
- "Khafizatun Nisa"  
- "Angelique Kiyoshi BU"
- "Naychilla Adelia Zahra"
- "Nakeisha Aulia Zahra"
- "Veronica Maria Lucia Ferreira 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="foto kelompok.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",
    "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.


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}
# 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"))
      
```

## 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)")

```
