Data Exploration

Exercises ~ Week 3

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1 Exercise 1

The following table shows sample information for three students. Each observation represents a single student and includes details such as their unique student ID, name, age, total credits completed, major field of study, and year level.

This dataset demonstrates a mixture of variable types:

Nominal: StudentID, Name, Major
Numeric: Age (continuous), CreditsCompleted (discrete)
Ordinal: YearLevel (Freshman → Senior)

StudentID	Name	Age	CreditsCompleted	Major	YearLevel
S001	Alice	20	45	Data Sains	Sophomore
S002	Budi	21	60	Mathematics	Junior
S003	Citra	19	30	Statistics	Freshman

# 1. Create vectors for each variable
StudentID <- c("S001", "S002", "S003")       # Nominal / ID
Name <- c("Alice", "Budi", "Citra")          # Nominal / Name
Age <- c(20, 21, 19)                         # Numeric / Continuous
CreditsCompleted <- c(45, 60, 30)            # Numeric / Discrete

# Nominal
Major <- c("Data Sains", "Mathematics", "Statistics")  

# Ordinal
YearLevel <- factor(c("Sophomore", "Junior", "Freshman"),
                    levels = c("Freshman","Sophomore","Junior","Senior"),
                    ordered = TRUE)          

# 2. Combine all vectors into a data frame
students <- data.frame(
  StudentID, Name, Age, CreditsCompleted, Major, YearLevel,
  stringsAsFactors = FALSE
)

# 3. Display the data frame
print(students)

##   StudentID  Name Age CreditsCompleted       Major YearLevel
## 1      S001 Alice  20               45  Data Sains Sophomore
## 2      S002  Budi  21               60 Mathematics    Junior
## 3      S003 Citra  19               30  Statistics  Freshman

2 Exercise 2

Identify Data Types: Determine the type of data for each of the following variables:

# Install knitr package if not already installed
# install.packages("knitr")
library(knitr) #untuk tabel yang rapih 
variables_info <- data.frame( 
  No = 1:5, # data yang di tampilkan 1 hingga 5
  
  #c itu Combine. untuk membuat vector (data)
  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(
    "Continous",
    "Continous",
    "Nominal",
    "Ordinal",
    "Nominal"
  ),
  #Mengatur agar data teks (string) tidak otomatis diubah menjadi factor
  stringsAsFactors = FALSE 
)

# Display the data frame as a neat table
kable(variables_info, 
      caption = "Table of Variables and Data Types") #untuk caption (keterangan) yang muncul di atas tabel

Table of Variables and Data Types
No	Variable	DataType	Subtype
1	Number of vehicles passing through the toll road each day	Numeric	Continous
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) # package DT(Data Tables) untuk membuat tabel data yang rapih

data_sources <- data.frame(
  No = 1:4, # baris data yang muncul 4 data
  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"
  ),
  
  #Mengatur agar data teks (string) tidak otomatis diubah menjadi factor
  stringsAsFactors = FALSE 
)


datatable(data_sources, 
          caption = "Table of Data Sources", # keterangan untuk di atas tabel
          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.

library(DT)
NO = 1:10
transactions <- data.frame(
  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"),
  #di atas ini Vector (karena datanya tidak memiliki level (text (string)))
  
  # di bawah ini Factor (karena menyimpan data Ordinal(ada levelnya Low - Medium - High)) bukan text (string)
  CustomerTier = c("High", "Medium", "Low", "Medium", "Medium",
                   "High", "Low", "High", "Low", "Medium"),
  
  stringsAsFactors = FALSE #Mengatur agar data teks (string) tidak otomatis diubah menjadi factor
)

# Atur urutan CustomerTier (Low → Medium → High)
transactions$CustomerTier <- factor( # ($) ambil kolom tertentu dari data frame yang bernama transactions
  transactions$CustomerTier,
  levels = c("Low", "Medium", "High"), # untuk menentukan urutan kategori
  ordered = TRUE # memberitahu R bahwa kategori (levels) di dalam faktor tersebut memiliki urutan yang logis
                 # dengan argumen ordered = TRUE R jadi tahu kalau High lebih tinggi dari Medium dan seterusnya
)

# Tambahkan kolom Total = Qty × Price
transactions$Total <- transactions$Qty * transactions$Price 


# Tampilkan tabel interaktif dengan DT
datatable(
  transactions,
  caption = "Table: Transaction Data with Total Revenue",
  rownames = FALSE,
  )

transactions_summary <- aggregate(
  cbind(Qty, Total, Price) ~ Product, # Untuk menggabungkan isi data (value) pada tabel Qty, Total, Price
  data = transactions,
  
  # ini fungsi (function) untuk data di bawahnya (sum = penjumlahan untuk nilai total)
  # (mean = menghitung nilai rata rata)
  FUN = function(x) c(sum = sum(x), avg = mean(x)) 
)


# Bayangin punya lemari besar namanya transactions_summary, 
# di lemari itu ada  4 laci : 1. Product, 2. Qty, 3. Total, 4. Price
transactions_summary <- data.frame(
  Product = transactions_summary$Product,
  
  # Ambil map 'sum' dari laci 'Qty'
  Total_Qty = transactions_summary$Qty[, "sum"],
  
  # Ambil map 'sum' dari laci 'Total'
  Total_Revenue = transactions_summary$Total[, "sum"],
  
  # Ambil map 'avg' dari laci 'Price', lalu bulatkan ke 2 angka desimal
  Avg_Price = round(transactions_summary$Price[, "avg"], 2)
)


#Tampilkan Table Numeric
numeric_table <- transactions[, c("Date", "Qty", "Price", "Total")] # data yang hanya di ambil Date, Qty, Price, Total

datatable(
  numeric_table,
  caption = "Table 2: Numeric Table",
  rownames = FALSE,
  options = list(pageLength = 5) # hanya menampilkan 5 baris data
)

# Tampilkan Table Categorical
categorical_table <- transactions[, c("Date", "Product", "CustomerTier")] # data yang hanya di ambil Date, Product, CustomerTier

datatable(
  categorical_table,
  caption = "Table 3: Categorical Table",
  rownames = FALSE,
  options = list(pageLength = 5) # hanya menampilkan 5 baris data
)

# Tampilkan tabel Summary
datatable(
  transactions_summary,
  caption = "Table 4: Summary Statistics per Product",
  rownames = FALSE,
  options = list(pageLength = 3) # hanya menampilkan 3 baris data
)

# PEMBUATAN BARPLOT

# aggregate itu untuk Mengelompokkan data. jadi pengelompokan data transactions berdasarkan kolom Product, 
qty_per_product <- aggregate(Qty ~ Product, data = transactions, sum) # Qty ~ Product -> kelompokkan Qty menurut Product
# Buat barplot
barplot(
  qty_per_product$Qty,  # tinggi batang sesuai jumlah penjualan
  names.arg = qty_per_product$Product, # nama batang = nama produk
  col = "skyblue", # menampilkan bar warna menjadi biru (skyblue)
  main = "Total Quantity Sold per Product", # Judul barplot
  xlab = "Product", # menunjukkan data sumbu X (Horizontal)
  ylab = "Total Quantity Sold" # menunjukkan data sumbu Y (Vertikal)
  )

# Mengelompokkan data transactions berdasarkan CustomerTier, lalu menjumlahkan nilai Total untuk setiap level pelanggan.
revenue_per_tier <- aggregate(Total ~ CustomerTier, data = transactions, sum) # Total ~ CustomerTier -> kelompokkan Total menurut CustomerTier
# Buat pie chart
pie(
  revenue_per_tier$Total, # nilai total per tier
  labels = revenue_per_tier$CustomerTier, # label tiap potongan pie
  col = c("red", "yellow", "green"), # menampilkan bar warna merah, kuning, hijau.
  main = "Proportion of Total Revenue per Customer Tier" # Judul Pie chart
)

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).

# Install kableExtra package if not already installed
# install.packages("kableExtra")
library(knitr)
library(kableExtra) #library untuk double header di table

# 1. Kolom Date: 30 hari berturut-turut di bulan Oktober 2025
Date <- seq.Date(from = as.Date("2025-10-01"), by = "day", length.out = 30)

# 2. Kolom Weather Temperature
# Menggunakan runif() untuk menghasilkan angka desimal acak antara 15°C dan 35°C
Weather_Temperature <- runif(30, min = 15, max = 35)

# 3. Kolom Number of Green Areas
# Menggunakan sample() untuk menghasilkan angka bulat acak antara 1 - 20
Number_of_Green_Areas <- sample(1:20, 30, replace = TRUE) 

  # 4. Kolom City Name
Cities <- c("New York", "Los Angeles", "Chicago", "Houston", "Phoenix",
            "Philadelphia", "San Antonio", "San Diego", "Dallas", "San Jose")
City_Name <- sample(Cities, 30, replace = TRUE) # untuk memilih nama kota secara acak dari daftar

# 5. Kolom Crime Level
# Menggunakan factor() dengan urutan level dari rendah ke tinggi (Low < Medium < High)
Crime_Level <- factor(
  sample(c("Low", "Medium", "High"), 30, replace = TRUE),
  levels = c("Low", "Medium", "High"),
  ordered = TRUE
)

# Menggabungkan semua kolom menjadi satu data frame bernama my_data
my_data <- data.frame(
  Date = Date,
  Weather_Temperature = Weather_Temperature,
  Number_of_Green_Areas = Number_of_Green_Areas,
  City_Name = City_Name,
  Crime_Level = Crime_Level
)

View(my_data)#melihat semua data

#Untuk menampilkan Kolum Data dan Baris header table
kable(my_data,
        caption = "City Environment & Crime Level Dataset",
        col.names = c("Date", "Air Temperature (°C)", "Number of Green Areas", "City Name", "Crime Level"),
        align = "c") %>%
    add_header_above(c(" " = 1, "Continous" = 1, "Discrete" = 1, "Nominal" = 1, "Ordinal" = 1)
)

City Environment & Crime Level Dataset
	Continous	Discrete	Nominal	Ordinal
Date	Air Temperature (°C)	Number of Green Areas	City Name	Crime Level
2025-10-01	26.34752	19	San Antonio	Low
2025-10-02	22.10281	16	New York	High
2025-10-03	31.71580	9	Phoenix	Medium
2025-10-04	15.13374	15	Philadelphia	High
2025-10-05	25.78779	18	Los Angeles	Medium
2025-10-06	15.85605	17	San Antonio	High
2025-10-07	21.96234	20	Houston	Low
2025-10-08	25.08193	13	Los Angeles	Medium
2025-10-09	18.59004	2	New York	Low
2025-10-10	17.11901	3	San Diego	High
2025-10-11	16.50570	20	New York	High
2025-10-12	15.15755	17	San Antonio	Low
2025-10-13	29.76421	8	Dallas	Medium
2025-10-14	29.70380	18	Chicago	Medium
2025-10-15	30.81624	3	Los Angeles	Low
2025-10-16	17.30336	15	Philadelphia	Low
2025-10-17	21.18546	3	Chicago	Medium
2025-10-18	17.55258	7	San Diego	High
2025-10-19	18.58223	5	New York	High
2025-10-20	18.63405	6	San Jose	Low
2025-10-21	29.39202	5	Los Angeles	Low
2025-10-22	21.04285	6	Dallas	Medium
2025-10-23	29.68960	9	Dallas	High
2025-10-24	19.64592	6	San Jose	Low
2025-10-25	24.58204	1	Los Angeles	Low
2025-10-26	24.77269	9	Chicago	Low
2025-10-27	34.01621	10	Los Angeles	Low
2025-10-28	30.63755	20	Los Angeles	High
2025-10-29	16.86696	1	San Antonio	High
2025-10-30	17.22151	19	Chicago	High

---
title: "Data Exploration"       # Main title of the document
subtitle: "Exercises ~ Week 3"  # Subtitle or topic for week 2
author:
- "Refantanur Hunsul Haqib"
- "Cahaya Medina Semidang"
- "Adinda Maiza Ishfahani"
- "Chandra Rizal Alamsyah"
- "Fityanandra Athar Adyaksa"   # 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="https://raw.githubusercontent.com/YanDraa/Week3Statistika/main/FOTO_KELOMPOK.jpg" 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) #untuk tabel yang rapih 
variables_info <- data.frame( 
  No = 1:5, # data yang di tampilkan 1 hingga 5
  
  #c itu Combine. untuk membuat vector (data)
  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(
    "Continous",
    "Continous",
    "Nominal",
    "Ordinal",
    "Nominal"
  ),
  #Mengatur agar data teks (string) tidak otomatis diubah menjadi factor
  stringsAsFactors = FALSE 
)

# Display the data frame as a neat table
kable(variables_info, 
      caption = "Table of Variables and Data Types") #untuk caption (keterangan) yang muncul di atas tabel

```
---

## 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) # package DT(Data Tables) untuk membuat tabel data yang rapih

data_sources <- data.frame(
  No = 1:4, # baris data yang muncul 4 data
  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"
  ),
  
  #Mengatur agar data teks (string) tidak otomatis diubah menjadi factor
  stringsAsFactors = FALSE 
)


datatable(data_sources, 
          caption = "Table of Data Sources", # keterangan untuk di atas tabel
          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)
NO = 1:10
transactions <- data.frame(
  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"),
  #di atas ini Vector (karena datanya tidak memiliki level (text (string)))
  
  # di bawah ini Factor (karena menyimpan data Ordinal(ada levelnya Low - Medium - High)) bukan text (string)
  CustomerTier = c("High", "Medium", "Low", "Medium", "Medium",
                   "High", "Low", "High", "Low", "Medium"),
  
  stringsAsFactors = FALSE #Mengatur agar data teks (string) tidak otomatis diubah menjadi factor
)

# Atur urutan CustomerTier (Low → Medium → High)
transactions$CustomerTier <- factor( # ($) ambil kolom tertentu dari data frame yang bernama transactions
  transactions$CustomerTier,
  levels = c("Low", "Medium", "High"), # untuk menentukan urutan kategori
  ordered = TRUE # memberitahu R bahwa kategori (levels) di dalam faktor tersebut memiliki urutan yang logis
                 # dengan argumen ordered = TRUE R jadi tahu kalau High lebih tinggi dari Medium dan seterusnya
)

# Tambahkan kolom Total = Qty × Price
transactions$Total <- transactions$Qty * transactions$Price 


# Tampilkan tabel interaktif dengan DT
datatable(
  transactions,
  caption = "Table: Transaction Data with Total Revenue",
  rownames = FALSE,
  )

transactions_summary <- aggregate(
  cbind(Qty, Total, Price) ~ Product, # Untuk menggabungkan isi data (value) pada tabel Qty, Total, Price
  data = transactions,
  
  # ini fungsi (function) untuk data di bawahnya (sum = penjumlahan untuk nilai total)
  # (mean = menghitung nilai rata rata)
  FUN = function(x) c(sum = sum(x), avg = mean(x)) 
)


# Bayangin punya lemari besar namanya transactions_summary, 
# di lemari itu ada  4 laci : 1. Product, 2. Qty, 3. Total, 4. Price
transactions_summary <- data.frame(
  Product = transactions_summary$Product,
  
  # Ambil map 'sum' dari laci 'Qty'
  Total_Qty = transactions_summary$Qty[, "sum"],
  
  # Ambil map 'sum' dari laci 'Total'
  Total_Revenue = transactions_summary$Total[, "sum"],
  
  # Ambil map 'avg' dari laci 'Price', lalu bulatkan ke 2 angka desimal
  Avg_Price = round(transactions_summary$Price[, "avg"], 2)
)


#Tampilkan Table Numeric
numeric_table <- transactions[, c("Date", "Qty", "Price", "Total")] # data yang hanya di ambil Date, Qty, Price, Total

datatable(
  numeric_table,
  caption = "Table 2: Numeric Table",
  rownames = FALSE,
  options = list(pageLength = 5) # hanya menampilkan 5 baris data
)

# Tampilkan Table Categorical
categorical_table <- transactions[, c("Date", "Product", "CustomerTier")] # data yang hanya di ambil Date, Product, CustomerTier

datatable(
  categorical_table,
  caption = "Table 3: Categorical Table",
  rownames = FALSE,
  options = list(pageLength = 5) # hanya menampilkan 5 baris data
)

# Tampilkan tabel Summary
datatable(
  transactions_summary,
  caption = "Table 4: Summary Statistics per Product",
  rownames = FALSE,
  options = list(pageLength = 3) # hanya menampilkan 3 baris data
)


# PEMBUATAN BARPLOT

# aggregate itu untuk Mengelompokkan data. jadi pengelompokan data transactions berdasarkan kolom Product, 
qty_per_product <- aggregate(Qty ~ Product, data = transactions, sum) # Qty ~ Product -> kelompokkan Qty menurut Product
# Buat barplot
barplot(
  qty_per_product$Qty,  # tinggi batang sesuai jumlah penjualan
  names.arg = qty_per_product$Product, # nama batang = nama produk
  col = "skyblue", # menampilkan bar warna menjadi biru (skyblue)
  main = "Total Quantity Sold per Product", # Judul barplot
  xlab = "Product", # menunjukkan data sumbu X (Horizontal)
  ylab = "Total Quantity Sold" # menunjukkan data sumbu Y (Vertikal)
  )

# Mengelompokkan data transactions berdasarkan CustomerTier, lalu menjumlahkan nilai Total untuk setiap level pelanggan.
revenue_per_tier <- aggregate(Total ~ CustomerTier, data = transactions, sum) # Total ~ CustomerTier -> kelompokkan Total menurut CustomerTier
# Buat pie chart
pie(
  revenue_per_tier$Total, # nilai total per tier
  labels = revenue_per_tier$CustomerTier, # label tiap potongan pie
  col = c("red", "yellow", "green"), # menampilkan bar warna merah, kuning, hijau.
  main = "Proportion of Total Revenue per Customer Tier" # Judul Pie chart
)

```

## 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}
# Install kableExtra package if not already installed
# install.packages("kableExtra")
library(knitr)
library(kableExtra) #library untuk double header di table

# 1. Kolom Date: 30 hari berturut-turut di bulan Oktober 2025
Date <- seq.Date(from = as.Date("2025-10-01"), by = "day", length.out = 30)

# 2. Kolom Weather Temperature
# Menggunakan runif() untuk menghasilkan angka desimal acak antara 15°C dan 35°C
Weather_Temperature <- runif(30, min = 15, max = 35)

# 3. Kolom Number of Green Areas
# Menggunakan sample() untuk menghasilkan angka bulat acak antara 1 - 20
Number_of_Green_Areas <- sample(1:20, 30, replace = TRUE) 

  # 4. Kolom City Name
Cities <- c("New York", "Los Angeles", "Chicago", "Houston", "Phoenix",
            "Philadelphia", "San Antonio", "San Diego", "Dallas", "San Jose")
City_Name <- sample(Cities, 30, replace = TRUE) # untuk memilih nama kota secara acak dari daftar

# 5. Kolom Crime Level
# Menggunakan factor() dengan urutan level dari rendah ke tinggi (Low < Medium < High)
Crime_Level <- factor(
  sample(c("Low", "Medium", "High"), 30, replace = TRUE),
  levels = c("Low", "Medium", "High"),
  ordered = TRUE
)

# Menggabungkan semua kolom menjadi satu data frame bernama my_data
my_data <- data.frame(
  Date = Date,
  Weather_Temperature = Weather_Temperature,
  Number_of_Green_Areas = Number_of_Green_Areas,
  City_Name = City_Name,
  Crime_Level = Crime_Level
)

View(my_data)#melihat semua data

#Untuk menampilkan Kolum Data dan Baris header table
kable(my_data,
        caption = "City Environment & Crime Level Dataset",
        col.names = c("Date", "Air Temperature (°C)", "Number of Green Areas", "City Name", "Crime Level"),
        align = "c") %>%
    add_header_above(c(" " = 1, "Continous" = 1, "Discrete" = 1, "Nominal" = 1, "Ordinal" = 1)
) 

```



