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(
    "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

  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)
#Exercise 4: Create Transactions Data Frame
# 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")
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")
# Combine all colums into a data frame
transactions <- data.frame(Date, Qty ,Price ,Product ,CustomerTier)
#Show data frame
#Add a new column for total
transactions <- transform (transactions, Total = Qty * Price)
View (transactions)

str (transactions)
## 'data.frame':    10 obs. of  6 variables:
##  $ Date        : chr  "2025-10-01" "2025-10-01" "2025-10-02" "2025-10-02" ...
##  $ Qty         : num  2 5 1 3 4 2 6 1 3 5
##  $ Price       : num  1000 20 1000 30 50 1000 25 1000 40 10
##  $ Product     : chr  "Laptop" "Mouse" "Laptop" "Keyboard" ...
##  $ CustomerTier: chr  "High" "Medium" "Low" "Medium" ...
##  $ Total       : num  2000 100 1000 90 200 2000 150 1000 120 50
# Total quantity per product
aggregate (Qty ~ Product, data = 
             transactions, FUN = sum)
# Total revenue per product
aggregate (Total ~ Product, data = 
             transactions, FUN = sum)
# Total price per product
aggregate (Price ~ Product, data = 
             transactions, FUN = mean)
# Total quantity per product
qty_per_product <- aggregate (Qty ~ Product, transactions, sum)

# Bar chart of quantity sold
barplot (qty_per_product$Qty, 
         names.arg = qty_per_product$Product,
         main = "Quantity Sold by Product",
         xlab = "Product",
         ylab = "Quantity",
         col = "red")

# Revenue per custom tier
revenue_per_tier <- aggregate (Total ~ CustomerTier, transactions, sum)

# Pie chart of revenue share 
pie (revenue_per_tier$Total,
     labels = revenue_per_tier$CustomerTier,
     main = "Revenue Share by Customer Tier",
     col = rainbow(3))

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).
# create data frame about purchasing drinks at cafe statis

#Dates from September 1 to September 30 
date = seq.Date (from = as.Date ("2030-09-01"), to =
              as.Date ("2030-09-30"), by = "day")

#Number of drinks ordered each day
set.seed(123)
Number_of_Drinks <- sample (1:15, 30,
                            replace = TRUE)

#Total purchase amount 
Total_Purchase <- round (Number_of_Drinks * runif (30, min = 15000, max = 25000), 0)

#Type of drinks
Drink_Type = sample (c("Matcha", "Coffe", 
                  "Chocolate","Tea", 
                  "Milkshake", "Latte"),
                30, replace = TRUE)

# Customer satisfaction level
satisfaction <- ifelse (Total_Purchase < 50000, "Not satisfied",
                ifelse (Total_Purchase < 100000,"Satisfied", 
                "Very satisfied"))

#
q <- quantile(Total_Purchase, probs = c(0, 1/3, 2/3, 1))
satisfaction <- cut(Total_Purchase,
                    breaks = q,
                    labels = c("Not satisfied", "Satisfied", "Very satisfied"),
                    include.lowest = TRUE)


#combine all columns into on data frame named my_data
my_data <- data.frame(date, 
                      Total_Purchase, Number_of_Drinks, 
                      Drink_Type, satisfaction)

# 7. Show first few rows
head(my_data)
# 8. Print full data frame
print(my_data)
##          date Total_Purchase Number_of_Drinks Drink_Type   satisfaction
## 1  2030-09-01         360345               15        Tea Very satisfied
## 2  2030-09-02         328606               15  Milkshake Very satisfied
## 3  2030-09-03          68864                3      Coffe  Not satisfied
## 4  2030-09-04         213446               14     Matcha Very satisfied
## 5  2030-09-05          59334                3     Matcha  Not satisfied
## 6  2030-09-06         225846               10  Chocolate Very satisfied
## 7  2030-09-07          34328                2     Matcha  Not satisfied
## 8  2030-09-08         109091                6      Latte  Not satisfied
## 9  2030-09-09         190479               11  Milkshake      Satisfied
## 10 2030-09-10          82140                5     Matcha  Not satisfied
## 11 2030-09-11          76582                4      Coffe  Not satisfied
## 12 2030-09-12         267921               14        Tea Very satisfied
## 13 2030-09-13         112131                6        Tea  Not satisfied
## 14 2030-09-14         148720                9      Latte      Satisfied
## 15 2030-09-15         163881               10      Latte      Satisfied
## 16 2030-09-16         190634               11  Chocolate      Satisfied
## 17 2030-09-17          98298                5      Latte  Not satisfied
## 18 2030-09-18          52979                3      Latte  Not satisfied
## 19 2030-09-19         259361               11     Matcha Very satisfied
## 20 2030-09-20         139125                9      Latte      Satisfied
## 21 2030-09-21         233064               12      Coffe Very satisfied
## 22 2030-09-22         206903                9     Matcha Very satisfied
## 23 2030-09-23         145971                9      Coffe      Satisfied
## 24 2030-09-24         267923               13        Tea Very satisfied
## 25 2030-09-25          51196                3  Milkshake  Not satisfied
## 26 2030-09-26         130203                8  Milkshake      Satisfied
## 27 2030-09-27         225331               10      Latte Very satisfied
## 28 2030-09-28         167653                7  Chocolate      Satisfied
## 29 2030-09-29         187446               10     Matcha      Satisfied
## 30 2030-09-30         194860                9        Tea      Satisfied
# 9. Check number of rows
nrow(my_data)
## [1] 30
# 10. Display summary of each column
summary(my_data)
##       date            Total_Purchase   Number_of_Drinks  Drink_Type       
##  Min.   :2030-09-01   Min.   : 34328   Min.   : 2.000   Length:30         
##  1st Qu.:2030-09-08   1st Qu.:100996   1st Qu.: 5.250   Class :character  
##  Median :2030-09-15   Median :165767   Median : 9.000   Mode  :character  
##  Mean   :2030-09-15   Mean   :166422   Mean   : 8.533                     
##  3rd Qu.:2030-09-22   3rd Qu.:222360   3rd Qu.:11.000                     
##  Max.   :2030-09-30   Max.   :360345   Max.   :15.000                     
##          satisfaction
##  Not satisfied :10   
##  Satisfied     :10   
##  Very satisfied:10   
##                      
##                      
## 
# 11. Make a formatted table using knitr
library(knitr)
kable(my_data, caption = "Table: Drink Purchase Data (30 Days)")
Table: Drink Purchase Data (30 Days)
date Total_Purchase Number_of_Drinks Drink_Type satisfaction
2030-09-01 360345 15 Tea Very satisfied
2030-09-02 328606 15 Milkshake Very satisfied
2030-09-03 68864 3 Coffe Not satisfied
2030-09-04 213446 14 Matcha Very satisfied
2030-09-05 59334 3 Matcha Not satisfied
2030-09-06 225846 10 Chocolate Very satisfied
2030-09-07 34328 2 Matcha Not satisfied
2030-09-08 109091 6 Latte Not satisfied
2030-09-09 190479 11 Milkshake Satisfied
2030-09-10 82140 5 Matcha Not satisfied
2030-09-11 76582 4 Coffe Not satisfied
2030-09-12 267921 14 Tea Very satisfied
2030-09-13 112131 6 Tea Not satisfied
2030-09-14 148720 9 Latte Satisfied
2030-09-15 163881 10 Latte Satisfied
2030-09-16 190634 11 Chocolate Satisfied
2030-09-17 98298 5 Latte Not satisfied
2030-09-18 52979 3 Latte Not satisfied
2030-09-19 259361 11 Matcha Very satisfied
2030-09-20 139125 9 Latte Satisfied
2030-09-21 233064 12 Coffe Very satisfied
2030-09-22 206903 9 Matcha Very satisfied
2030-09-23 145971 9 Coffe Satisfied
2030-09-24 267923 13 Tea Very satisfied
2030-09-25 51196 3 Milkshake Not satisfied
2030-09-26 130203 8 Milkshake Satisfied
2030-09-27 225331 10 Latte Very satisfied
2030-09-28 167653 7 Chocolate Satisfied
2030-09-29 187446 10 Matcha Satisfied
2030-09-30 194860 9 Tea Satisfied 
library(knitr)

# Count how many times each drink type appears

drink_table <- as.data.frame(table(my_data$Drink_Type))
colnames(drink_table) <- c("Drink Type", "Frequency")

# Count satisfaction levels

satisfaction_table <- as.data.frame(table(my_data$satisfaction))
colnames(satisfaction_table) <- c("Satisfaction Level", "Frequency")

# show table
knitr::kable(drink_table, caption = "Table: Frequency of Each Drink Type")
Table: Frequency of Each Drink Type
Drink Type Frequency
Chocolate 3
Coffe 4
Latte 7
Matcha 7
Milkshake 4
Tea 5 
knitr::kable(satisfaction_table, caption = "Table: Frequency of Each Satisfaction Level")
Table: Frequency of Each Satisfaction Level
Satisfaction Level Frequency
Not satisfied 10
Satisfied 10
Very satisfied 10
---
title: "Data Exploration"       # Main title of the document
subtitle: "Exercises ~ Week 2"  # Subtitle or topic for week 2
author: 
- "Muhammad Nabil Khairil Anam"
- "Carol Dupino pereira"
- "Ignasius Rabi Blolong"
- "Raihania Syah Putri"
- "Dameria Adelina Mini Simarmata"
                                # 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 src="https://github.com/dsciencelabs/images/blob/master/Logo_Dsciencelabs_v1.png?raw=true" alt="Logo" id="Foto" 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}
library(DT)
#Exercise 4: Create Transactions Data Frame
# 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")
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")
# Combine all colums into a data frame
transactions <- data.frame(Date, Qty ,Price ,Product ,CustomerTier)
#Show data frame
#Add a new column for total
transactions <- transform (transactions, Total = Qty * Price)
View (transactions)

str (transactions)

# Total quantity per product
aggregate (Qty ~ Product, data = 
             transactions, FUN = sum)

# Total revenue per product
aggregate (Total ~ Product, data = 
             transactions, FUN = sum)

# Total price per product
aggregate (Price ~ Product, data = 
             transactions, FUN = mean)

# Total quantity per product
qty_per_product <- aggregate (Qty ~ Product, transactions, sum)

# Bar chart of quantity sold
barplot (qty_per_product$Qty, 
         names.arg = qty_per_product$Product,
         main = "Quantity Sold by Product",
         xlab = "Product",
         ylab = "Quantity",
         col = "red")


# Revenue per custom tier
revenue_per_tier <- aggregate (Total ~ CustomerTier, transactions, sum)

# Pie chart of revenue share 
pie (revenue_per_tier$Total,
     labels = revenue_per_tier$CustomerTier,
     main = "Revenue Share by Customer Tier",
     col = rainbow(3))

```

## 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, echo = TRUE, include = TRUE, warning = FALSE}

# create data frame about purchasing drinks at cafe statis

#Dates from September 1 to September 30 
date = seq.Date (from = as.Date ("2030-09-01"), to =
              as.Date ("2030-09-30"), by = "day")

#Number of drinks ordered each day
set.seed(123)
Number_of_Drinks <- sample (1:15, 30,
                            replace = TRUE)

#Total purchase amount 
Total_Purchase <- round (Number_of_Drinks * runif (30, min = 15000, max = 25000), 0)

#Type of drinks
Drink_Type = sample (c("Matcha", "Coffe", 
                  "Chocolate","Tea", 
                  "Milkshake", "Latte"),
                30, replace = TRUE)

# Customer satisfaction level
satisfaction <- ifelse (Total_Purchase < 50000, "Not satisfied",
                ifelse (Total_Purchase < 100000,"Satisfied", 
                "Very satisfied"))

#
q <- quantile(Total_Purchase, probs = c(0, 1/3, 2/3, 1))
satisfaction <- cut(Total_Purchase,
                    breaks = q,
                    labels = c("Not satisfied", "Satisfied", "Very satisfied"),
                    include.lowest = TRUE)


#combine all columns into on data frame named my_data
my_data <- data.frame(date, 
                      Total_Purchase, Number_of_Drinks, 
                      Drink_Type, satisfaction)

# 7. Show first few rows
head(my_data)

# 8. Print full data frame
print(my_data)

# 9. Check number of rows
nrow(my_data)

# 10. Display summary of each column
summary(my_data)

# 11. Make a formatted table using knitr
library(knitr)
kable(my_data, caption = "Table: Drink Purchase Data (30 Days)")

library(knitr)

# Count how many times each drink type appears

drink_table <- as.data.frame(table(my_data$Drink_Type))
colnames(drink_table) <- c("Drink Type", "Frequency")

# Count satisfaction levels

satisfaction_table <- as.data.frame(table(my_data$satisfaction))
colnames(satisfaction_table) <- c("Satisfaction Level", "Frequency")

# show table
knitr::kable(drink_table, caption = "Table: Frequency of Each Drink Type")
knitr::kable(satisfaction_table, caption = "Table: Frequency of Each Satisfaction Level")