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(
    "Numerics",
    "Numerics",
    "Catagorical",
    "Catagorical",
    "Catagorical"
  ),
  Subtype = c(
    "Discreate",
    "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	Numerics	Discreate
2	Student height in cm	Numerics	Continuous
3	Employee gender (Male / Female)	Catagorical	Nominal
4	Customer satisfaction level: Low, Medium, High	Catagorical	Ordinal
5	Respondent’s favorite color: Red, Blue, Green	Catagorical	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.

4.1 Data Variable

Data Frame

library(knitr)
library(reshape2)

#Data Frame

Date = as.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")
  CostumerTier = c("High", "Medium", "Low", "Medium", "Medium",
                   "High", "Low", "High", "Low", "Medium")
  
  #transactions data frame
transactions <- data.frame(Date, Qty, Price, 
                           Product, CostumerTier)
kable(transactions,
      caption="Data Frame")

Data Frame
Date	Qty	Price	Product	CostumerTier
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

Data Numeric Numeric or quantitative data are data expressed in numbers that represent counts or measurements. They provide information about how much or how many of something, allowing for mathematical operations such as addition, subtraction, averaging, and statistical analysis.

#Category and Numeric
Numeric <- data.frame(
  Qty, Price,
  stringsAsFactors = FALSE
 ) 


kable(Numeric,
      caption = "Data Numeric")

Data Numeric
Qty	Price
2	1000
5	20
1	1000
3	30
4	50
2	1000
6	25
1	1000
3	40
5	10

Data Category Categorical or qualitative data are data expressed in labels, names, or categories rather than numbers. They describe qualities, attributes, or classifications that cannot be meaningfully measured with arithmetic operations like addition or subtraction.

Category <- data.frame(Date, Product, CostumerTier)


kable(Category,
      caption = "Data Category")

Data Category
Date	Product	CostumerTier
2025-10-01	Laptop	High
2025-10-01	Mouse	Medium
2025-10-02	Laptop	Low
2025-10-02	Keyboard	Medium
2025-10-03	Mouse	Medium
2025-10-03	Laptop	High
2025-10-04	Keyboard	Low
2025-10-04	Laptop	High
2025-10-05	Mouse	Low
2025-10-05	Keyboard	Medium

Total Data Transaction

transactions$Total <- transactions$Qty * transactions$Price        
kable(transactions)

Date	Qty	Price	Product	CostumerTier	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

#Total quantity sold for each product
total_qty <- aggregate(Qty ~ Product, data = transactions, sum)

# Total revenue per product
total_revenue <- aggregate(Total ~ Product, data = transactions, sum)

# Average price per product
avg_price <- aggregate(Price ~ Product, data = transactions, mean)


kable(total_qty,
      caption = "Total Quantity")

Total Quantity
Product	Qty
Keyboard	14
Laptop	6
Mouse	12

kable(total_revenue,
      caption = "Total Revenue")

Total Revenue
Product	Total
Keyboard	290
Laptop	6000
Mouse	420

kable(avg_price,
      caption = "Average Price")

Average Price
Product	Price
Keyboard	21.66667
Laptop	1000.00000
Mouse	36.66667

barplot(total_qty$Qty,
        names.arg = total_qty$Qty,
        main = "Total Quantity Sold per Product",
        col = "navy",
        xlab = "Product",
        ylab = "total_qty",)

___

# Pie chart total revenue per customer tier and percentage

revenue_tier <- aggregate(Total ~ CostumerTier, data = transactions, sum)

revenue_tier$Percent <- round(100 * revenue_tier$Total / sum(revenue_tier$Total), 1)

labels <- paste(revenue_tier$CostumerTier, "-", revenue_tier$Percent, "%")

pie(revenue_tier$Total,
    labels = labels,
    main = "Total revenue pie chart per CostumerTier (%)",
    col = c("lightblue", "lightgreen", "pink"),
    xlab = "Costumer",
    ylab = "total_revenue")

Date Highest Total Revenue

revenue_date <- aggregate(Total ~ Date, data = transactions, sum)
revenue_date[which.max(revenue_date$Total),]

# Stacked bar chart: quantity sold per product by customer tier

qty_tier <- aggregate(Qty ~ Product + CostumerTier, data = transactions, sum)
qty_wide <- dcast(qty_tier, Product ~ CostumerTier, value.var = "Qty", fill = 0)
barplot(
  as.matrix(qty_wide[, -1]),
  beside = TRUE,
  legend = colnames(qty_wide)[-1],
  col = rainbow(ncol(qty_wide) - 1),
  main = "Quantity Sold per Product by Customer Tier",
  xlab = "Product",
  ylab = "Quantity",
  names.arg = qty_wide$Product
)

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).

#Excercise 5: Data Penggunaan KRL selama bulan September
library(knitr)
library(DT)

# Data simulasi
Tanggal <- seq.Date(from = as.Date("2025-09-01"), to = as.Date("2025-09-30"), by = "day")
Jumlah_Penumpang <- sample(4800:5500, 30, replace = TRUE)
Waktu_Tunggu <- round(runif(30, min = 6.0, max = 8.5), 1)
Jenis_Jalur <- sample(c("Merah", "Hijau", "Biru", "Kuning"), 30, replace = TRUE)

Tingkat_Kepadatan <- factor(
  sample(c("Rendah", "Sedang", "Tinggi", "Sangat Tinggi"), 30, replace = TRUE),
  levels = c("Rendah", "Sedang", "Tinggi", "Sangat Tinggi"),
  ordered = TRUE
)

#Data Frame
my_data <- data.frame(
  Tanggal,
  Jumlah_Penumpang,
  Waktu_Tunggu,
  Jenis_Jalur,
  Tingkat_Kepadatan,
  stringsAsFactors = FALSE
)

#Interactive table and color
datatable(
  my_data,
  caption = htmltools::tags$caption(
    style = 'caption-side: top; text-align: center; font-weight: bold; font-size: 16px; color: #2C3E50;',
    'Tabel 1. Data Simulasi Penggunaan KRL Selama Bulan September'
  ),
  options = list(
    pageLength = 10,
    autoWidth = TRUE,
    dom = 'Bfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
    initComplete = JS(
      "function(settings, json) {",
      "$(this.api().table().header()).css({'background-color': '#2C3E50', 'color': '#fff'});",
      "}"
    )
  ),
  rownames = FALSE,
  class = 'cell-border stripe hover compact'
) %>%
  formatStyle(
    'Jenis_Jalur',
    backgroundColor = styleEqual(
      c("Merah", "Hijau", "Biru", "Kuning"),
      c("#FF6B6B", "#6BCB77", "#4D96FF", "#FFD93D")
    ),
    color = "black",
    fontWeight = "bold"
  ) %>%
  formatStyle(
    'Tingkat_Kepadatan',
    backgroundColor = styleEqual(
      c("Rendah", "Sedang", "Tinggi", "Sangat Tinggi"),
      c("#DFFFD6", "#FFF3B0", "#FFD6A5", "#FFB5A7")
    ),
    color = "black",
    fontWeight = "bold"
  )

SUMMARY

my_data_summary <- summary(my_data)
kable(my_data_summary)

Tanggal	Jumlah_Penumpang	Waktu_Tunggu	Jenis_Jalur	Tingkat_Kepadatan
Min. :2025-09-01	Min. :4831	Min. :6.200	Length:30	Rendah :11
1st Qu.:2025-09-08	1st Qu.:4940	1st Qu.:7.025	Class :character	Sedang : 3
Median :2025-09-15	Median :5120	Median :7.250	Mode :character	Tinggi : 8
Mean :2025-09-15	Mean :5138	Mean :7.350	NA	Sangat Tinggi: 8
3rd Qu.:2025-09-22	3rd Qu.:5321	3rd Qu.:7.850	NA	NA
Max. :2025-09-30	Max. :5488	Max. :8.500	NA	NA

JENIS JALUR (NOMINAL)

table(my_data$Jenis_Jalur)

## 
##   Biru  Hijau Kuning  Merah 
##      6      5      8     11

TINGKAT KEPADATAN (ORDINAL)

table(my_data$Tingkat_Kepadatan)

## 
##        Rendah        Sedang        Tinggi Sangat Tinggi 
##            11             3             8             8

---
title: "Data Exploration"       # Main title of the document
subtitle: "Exercises ~ Week 3"  # Subtitle or topic for week 2
author: 
- "Arya Fharezi"
- "Christian Michael Juliano"
- "Dhefio Alim Muzakki"
- "Frenkhy Tonga Retang"
- "M Yustian Putra Muhadi"
- "Yosef Teofani Tamba"

# 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 (show)
    code_download: yes           # Adds a button to download all R code (yes)
---

<img id="Foto" src="FotoKelompok.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(
    "Numerics",
    "Numerics",
    "Catagorical",
    "Catagorical",
    "Catagorical"
  ),
  Subtype = c(
    "Discreate",
    "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.


### Data Variable 
**Data Frame**
```{r}
library(knitr)
library(reshape2)

#Data Frame

Date = as.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")
  CostumerTier = c("High", "Medium", "Low", "Medium", "Medium",
                   "High", "Low", "High", "Low", "Medium")
  
  #transactions data frame
transactions <- data.frame(Date, Qty, Price, 
                           Product, CostumerTier)
kable(transactions,
      caption="Data Frame")
```

**Data Numeric**
Numeric or quantitative data are data expressed in numbers that represent counts or measurements. They provide information about how much or how many of something, allowing for mathematical operations such as addition, subtraction, averaging, and statistical analysis.
```{r}

#Category and Numeric
Numeric <- data.frame(
  Qty, Price,
  stringsAsFactors = FALSE
 ) 


kable(Numeric,
      caption = "Data Numeric")
```


**Data Category**
Categorical or qualitative data are data expressed in labels, names, or categories rather than numbers. They describe qualities, attributes, or classifications that cannot be meaningfully measured with arithmetic operations like addition or subtraction.
```{r}
Category <- data.frame(Date, Product, CostumerTier)


kable(Category,
      caption = "Data Category")

```

**Total Data Transaction**
```{r}
transactions$Total <- transactions$Qty * transactions$Price        
kable(transactions)

```


```{r}


#Total quantity sold for each product
total_qty <- aggregate(Qty ~ Product, data = transactions, sum)

# Total revenue per product
total_revenue <- aggregate(Total ~ Product, data = transactions, sum)

# Average price per product
avg_price <- aggregate(Price ~ Product, data = transactions, mean)


kable(total_qty,
      caption = "Total Quantity")

kable(total_revenue,
      caption = "Total Revenue")

kable(avg_price,
      caption = "Average Price")

```

```{r}
barplot(total_qty$Qty,
        names.arg = total_qty$Qty,
        main = "Total Quantity Sold per Product",
        col = "navy",
        xlab = "Product",
        ylab = "total_qty",)
```
___

```{r}

# Pie chart total revenue per customer tier and percentage

revenue_tier <- aggregate(Total ~ CostumerTier, data = transactions, sum)

revenue_tier$Percent <- round(100 * revenue_tier$Total / sum(revenue_tier$Total), 1)

labels <- paste(revenue_tier$CostumerTier, "-", revenue_tier$Percent, "%")

pie(revenue_tier$Total,
    labels = labels,
    main = "Total revenue pie chart per CostumerTier (%)",
    col = c("lightblue", "lightgreen", "pink"),
    xlab = "Costumer",
    ylab = "total_revenue")
```

**Date Highest Total Revenue**
```{r}

revenue_date <- aggregate(Total ~ Date, data = transactions, sum)
revenue_date[which.max(revenue_date$Total),]

```

```{r}
# Stacked bar chart: quantity sold per product by customer tier

qty_tier <- aggregate(Qty ~ Product + CostumerTier, data = transactions, sum)
qty_wide <- dcast(qty_tier, Product ~ CostumerTier, value.var = "Qty", fill = 0)
barplot(
  as.matrix(qty_wide[, -1]),
  beside = TRUE,
  legend = colnames(qty_wide)[-1],
  col = rainbow(ncol(qty_wide) - 1),
  main = "Quantity Sold per Product by Customer Tier",
  xlab = "Product",
  ylab = "Quantity",
  names.arg = qty_wide$Product
)
```





## 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}
#Excercise 5: Data Penggunaan KRL selama bulan September
library(knitr)
library(DT)

# Data simulasi
Tanggal <- seq.Date(from = as.Date("2025-09-01"), to = as.Date("2025-09-30"), by = "day")
Jumlah_Penumpang <- sample(4800:5500, 30, replace = TRUE)
Waktu_Tunggu <- round(runif(30, min = 6.0, max = 8.5), 1)
Jenis_Jalur <- sample(c("Merah", "Hijau", "Biru", "Kuning"), 30, replace = TRUE)

Tingkat_Kepadatan <- factor(
  sample(c("Rendah", "Sedang", "Tinggi", "Sangat Tinggi"), 30, replace = TRUE),
  levels = c("Rendah", "Sedang", "Tinggi", "Sangat Tinggi"),
  ordered = TRUE
)

#Data Frame
my_data <- data.frame(
  Tanggal,
  Jumlah_Penumpang,
  Waktu_Tunggu,
  Jenis_Jalur,
  Tingkat_Kepadatan,
  stringsAsFactors = FALSE
)

#Interactive table and color
datatable(
  my_data,
  caption = htmltools::tags$caption(
    style = 'caption-side: top; text-align: center; font-weight: bold; font-size: 16px; color: #2C3E50;',
    'Tabel 1. Data Simulasi Penggunaan KRL Selama Bulan September'
  ),
  options = list(
    pageLength = 10,
    autoWidth = TRUE,
    dom = 'Bfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
    initComplete = JS(
      "function(settings, json) {",
      "$(this.api().table().header()).css({'background-color': '#2C3E50', 'color': '#fff'});",
      "}"
    )
  ),
  rownames = FALSE,
  class = 'cell-border stripe hover compact'
) %>%
  formatStyle(
    'Jenis_Jalur',
    backgroundColor = styleEqual(
      c("Merah", "Hijau", "Biru", "Kuning"),
      c("#FF6B6B", "#6BCB77", "#4D96FF", "#FFD93D")
    ),
    color = "black",
    fontWeight = "bold"
  ) %>%
  formatStyle(
    'Tingkat_Kepadatan',
    backgroundColor = styleEqual(
      c("Rendah", "Sedang", "Tinggi", "Sangat Tinggi"),
      c("#DFFFD6", "#FFF3B0", "#FFD6A5", "#FFB5A7")
    ),
    color = "black",
    fontWeight = "bold"
  )
```

**SUMMARY**
```{r}
my_data_summary <- summary(my_data)
kable(my_data_summary)

```
**JENIS JALUR (NOMINAL)**
```{r}
table(my_data$Jenis_Jalur)

```
**TINGKAT KEPADATAN (ORDINAL)**
```{R}
table(my_data$Tingkat_Kepadatan)

```