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)

# 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(
    "Continuous",
    "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 Continuous
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)
# 4.1 Transaction
transactions <- data.frame(
  No = 1 : 10,
  
  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")
  )
library(knitr)
# 4.2 Create a data frame for Data Types
variables_info <- data.frame(
  No = 1:4,
  Variable = c(
    "Qty",
    "Price",
    "Product",
    "Customertier"
  ),
  DataType = c(
    "Numeric",
    "Numeric",
    "Categorical",
    "Categorical"
  ),
  stringAsFactors = FALSE
)

library(knitr)
# 4.3 Display the data table as a neat table
kable(variables_info,
      caption = "Table of Variables and Data Types")
Table of Variables and Data Types
No Variable DataType stringAsFactors
1 Qty Numeric FALSE
2 Price Numeric FALSE
3 Product Categorical FALSE
4 Customertier Categorical FALSE 
#4.3 Transactions Total
transactions$Total <- transactions$Qty * transactions$Price
datatable(transactions, 
          caption = "Table of Transaction",
          rownames = FALSE)
library(knitr)
# 4.4 Compute Summary Statistics
# Total Qty per Product
total_qty_per_product <- aggregate(Qty ~ Product, data = transactions, FUN = sum)
print("Total Kuantitas Terjual per Produk:")
## [1] "Total Kuantitas Terjual per Produk:"
print(total_qty_per_product)
##    Product Qty
## 1 Keyboard  14
## 2   Laptop   6
## 3    Mouse  12
# Total Revenue per Product
total_revenue_per_product <- aggregate(Total ~ Product, data = transactions, FUN = sum)
print("Total Pendapatan per Produk:")
## [1] "Total Pendapatan per Produk:"
print(total_revenue_per_product)
##    Product Total
## 1 Keyboard   290
## 2   Laptop  6000
## 3    Mouse   420
# Total Revenue per Customer Tier
total_revenue_per_tier <- aggregate(Total ~ CostumerTier, data = transactions, FUN = sum)
print("Total Pendapatan Per Pelanggan:")
## [1] "Total Pendapatan Per Pelanggan:"
print(total_revenue_per_tier)
##   CostumerTier Total
## 1         High  5000
## 2          Low  1270
## 3       Medium   440
# Average Price per Product
average_price_per_product <- aggregate(Price ~ Product, data = transactions, FUN = mean)
print("Harga Rata-rata per Produk")
## [1] "Harga Rata-rata per Produk"
print(average_price_per_product)
##    Product      Price
## 1 Keyboard   21.66667
## 2   Laptop 1000.00000
## 3    Mouse   36.66667
library(knitr)
# 4.5 Visualize Data
barplot(
  height = total_qty_per_product$Qty, 
  names.arg = total_qty_per_product$Product,
  col = c("pink", "blue", "green"),
  main = "Total Transactions",
  xlab = "Product",
  ylab = "Quantity",
  ylim = c(0, max(total_qty_per_product$Qty) + 2))

#Pie chart
revenue_per_tier <- aggregate(Total ~ CostumerTier, data = transactions, FUN = sum)
total_revenue <- sum(revenue_per_tier$Total)
percentages <- round(revenue_per_tier$Total / total_revenue * 100, 1)
pie_labels <- paste(revenue_per_tier$CostumerTier, "(", percentages, "%)", sep = "")

pie(
  x = revenue_per_tier$Total,
  labels = pie_labels,
  main = "Proportion of Total Revenue per CustomerTier",
  col = c("purple", "yellow", "red")
)

library(knitr)
# 4.6 Find the Highest date of total revenue
transactions$Revenue <- transactions$Price * transactions$Qty
total_per_date <- aggregate(Revenue ~ Date, data = transactions, FUN = sum)
highest <- total_per_date[which.max(total_per_date$Revenue), ]
print("The highest data of total Revenue")
## [1] "The highest data of total Revenue"
print(total_per_date)
##         Date Revenue
## 1 2025-10-01    2100
## 2 2025-10-02    1090
## 3 2025-10-03    2200
## 4 2025-10-04    1150
## 5 2025-10-05     170
print(highest)
##         Date Revenue
## 3 2025-10-03    2200
# Stacked Bar Chart
table_transactions <- with(transactions, tapply(Qty, list(Product, CostumerTier), sum, na.rm = TRUE))
table_transactions[is.na(table_transactions)] <- 0
barplot(
  t(table_transactions),
  beside = FALSE,
  col = c("brown", "gold", "orange"),
  main = "Quantity Sold per Product by CustomerTier",
  xlab = "Product",
  ylab = "Quantity Sold",
  legend.text = TRUE)

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()
library(DT)
library(knitr)
# 5.1 Create a vector for each column
set.seed(50)
tanggal <- as.Date("2024-01-01") + sample(0:365, 30, replace = TRUE)
hewan <- c("Kucing", "Anjing", "Kelinci", "Burung",
           "Kuda", "Gajah", "Singa", "Harimau", "Zebra", "Panda", "Rusa",
           "Unta", "Kudanil", "Badak", "Beruang", "Monyet", "Gorila", "Kanguru",
           "Koala", "Serigala", "Kucing", "Anjing", "Burung", "Gajah", "Singa", 
           "Harimau", "Zebra", "Panda", "Rusa", "Unta")
hewan <- factor(hewan)

jumlah_porsi_makanan <- c(5, 5, 3, 4, 3, 
                  3, 5, 5, 5, 6, 
                  4, 4, 6, 6, 5, 
                  5, 6, 4, 4, 5, 
                  5, 4, 5, 6, 7, 
                  6, 5, 6, 4, 4)

berat <- c(24.56, 48.76, 15.48, 9.64, 289.67, 785.54, 293.27, 257.38, 158.35, 
           93.32, 82.67, 632.75, 429.71, 836.54, 274.68, 18.56, 467.82, 152.74,
           52.85, 172.92, 38.56, 95.27, 6.32, 364.78, 284.92, 256.85, 193.65,
           123.45, 110.43, 576.98)

kelincahan <- c("Tinggi", "Tinggi", "Sedang", "Tinggi", "Sedang", "Rendah", "Tinggi",
                "Tinggi", "Sedang", "Sedang", "Sedang", "Rendah", "Rendah", "Rendah",
                "Sedang", "Tinggi", "Rendah", "Tinggi", "Sedang", "Tinggi", "Sedang", 
                "Tinggi", "Tinggi", "Sedang", "Tinggi", "Tinggi", "Sedang", "Sedang",
                "Sedang", "Rendah")
kelincahan <- factor(kelincahan,
                     levels = c("Rendah", "Sedang", "Tinggi"),
                     ordered = TRUE)

# 5.2 Combine all vectors into a data frame
Data_Hewan <- data.frame(tanggal, hewan, jumlah_porsi_makanan, berat, kelincahan)

# 5.3 Create a data frame as a neat table
datatable(Data_Hewan, caption = "Rekapitulasi Data Hewan: Jenis, Berat dan Kelincahan", options = list(pageLength = 10))
# 5.3 Create a table of variable types
variables_info2 <- data.frame(
  No = 1:4,
  Variable = c(
    "Hewan",
    "JumlahPorsiMakanan",
    "Berat",
    "Kelincahan"),
  DataType = c(
    "Nominal",
    "Diskrit",
    "Continuous",
    "Ordinal"),
  stringsAsFactors = FALSE
)

knitr::kable(variables_info2, caption = "Table of Variable and Data Type")
Table of Variable and Data Type
No Variable DataType
1 Hewan Nominal
2 JumlahPorsiMakanan Diskrit
3 Berat Continuous
4 Kelincahan Ordinal 
# 5.4 Summarize each column
summary(Data_Hewan)
##     tanggal               hewan    jumlah_porsi_makanan     berat       
##  Min.   :2024-01-07   Anjing : 2   Min.   :3.000        Min.   :  6.32  
##  1st Qu.:2024-04-04   Burung : 2   1st Qu.:4.000        1st Qu.: 60.30  
##  Median :2024-09-06   Gajah  : 2   Median :5.000        Median :165.63  
##  Mean   :2024-07-21   Harimau: 2   Mean   :4.833        Mean   :238.28  
##  3rd Qu.:2024-10-28   Kucing : 2   3rd Qu.:5.750        3rd Qu.:292.37  
##  Max.   :2024-12-28   Panda  : 2   Max.   :7.000        Max.   :836.54  
##                       (Other):18                                        
##   kelincahan
##  Rendah: 6  
##  Sedang:12  
##  Tinggi:12  
##             
##             
##             
## 
summary(transactions)
##        No            Date                Qty           Price        
##  Min.   : 1.00   Length:10          Min.   :1.00   Min.   :  10.00  
##  1st Qu.: 3.25   Class :character   1st Qu.:2.00   1st Qu.:  26.25  
##  Median : 5.50   Mode  :character   Median :3.00   Median :  45.00  
##  Mean   : 5.50                      Mean   :3.20   Mean   : 417.50  
##  3rd Qu.: 7.75                      3rd Qu.:4.75   3rd Qu.:1000.00  
##  Max.   :10.00                      Max.   :6.00   Max.   :1000.00  
##    Product          CostumerTier           Total         Revenue    
##  Length:10          Length:10          Min.   :  50   Min.   :  50  
##  Class :character   Class :character   1st Qu.: 105   1st Qu.: 105  
##  Mode  :character   Mode  :character   Median : 175   Median : 175  
##                                        Mean   : 671   Mean   : 671  
##                                        3rd Qu.:1000   3rd Qu.:1000  
##                                        Max.   :2000   Max.   :2000
summary_table <- summary(Data_Hewan)
knitr::kable(as.data.frame(summary_table), caption = "Summary of Data Hewan")
Summary of Data Hewan
Var1 Var2 Freq
tanggal Min. :2024-01-07
tanggal 1st Qu.:2024-04-04
tanggal Median :2024-09-06
tanggal Mean :2024-07-21
tanggal 3rd Qu.:2024-10-28
tanggal Max. :2024-12-28
tanggal NA
hewan Anjing : 2
hewan Burung : 2
hewan Gajah : 2
hewan Harimau: 2
hewan Kucing : 2
hewan Panda : 2
hewan (Other):18
jumlah_porsi_makanan Min. :3.000
jumlah_porsi_makanan 1st Qu.:4.000
jumlah_porsi_makanan Median :5.000
jumlah_porsi_makanan Mean :4.833
jumlah_porsi_makanan 3rd Qu.:5.750
jumlah_porsi_makanan Max. :7.000
jumlah_porsi_makanan NA
berat Min. : 6.32
berat 1st Qu.: 60.30
berat Median :165.63
berat Mean :238.28
berat 3rd Qu.:292.37
berat Max. :836.54
berat NA
kelincahan Rendah: 6
kelincahan Sedang:12
kelincahan Tinggi:12
kelincahan NA
kelincahan NA
kelincahan NA
kelincahan NA 
# 5.4 Count the frequency of each category for **Nominal** and **Ordinal** columns
list(
  Nominal = table(Data_Hewan$Hewan),
  Ordinal = table(Data_Hewan$kelincahan)
)
## $Nominal
## < table of extent 0 >
## 
## $Ordinal
## 
## Rendah Sedang Tinggi 
##      6     12     12
---
title: "Data Exploration"       # Main title of the document
subtitle: "Exercises ~ Week 3"  # Subtitle or topic for week 2
author: 
- "Adam  Richie Wijaya"
- "Morris Alexander Pangaribuan"
- "Andre"
- "Kayla Aprilia"
- "Oktavia Maia Rego"
- "Januaria Teresinha"          # 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://github.com/dsciencelabs/images/blob/master/Logo_Dsciencelabs_v1.png?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(
    "Continuous",
    "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)
# 4.1 Transaction
transactions <- data.frame(
  No = 1 : 10,
  
  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")
  )
library(knitr)
# 4.2 Create a data frame for Data Types
variables_info <- data.frame(
  No = 1:4,
  Variable = c(
    "Qty",
    "Price",
    "Product",
    "Customertier"
  ),
  DataType = c(
    "Numeric",
    "Numeric",
    "Categorical",
    "Categorical"
  ),
  stringAsFactors = FALSE
)

library(knitr)
# 4.3 Display the data table as a neat table
kable(variables_info,
      caption = "Table of Variables and Data Types")

#4.3 Transactions Total
transactions$Total <- transactions$Qty * transactions$Price
datatable(transactions, 
          caption = "Table of Transaction",
          rownames = FALSE)

library(knitr)
# 4.4 Compute Summary Statistics
# Total Qty per Product
total_qty_per_product <- aggregate(Qty ~ Product, data = transactions, FUN = sum)
print("Total Kuantitas Terjual per Produk:")
print(total_qty_per_product)

# Total Revenue per Product
total_revenue_per_product <- aggregate(Total ~ Product, data = transactions, FUN = sum)
print("Total Pendapatan per Produk:")
print(total_revenue_per_product)

# Total Revenue per Customer Tier
total_revenue_per_tier <- aggregate(Total ~ CostumerTier, data = transactions, FUN = sum)
print("Total Pendapatan Per Pelanggan:")
print(total_revenue_per_tier)

# Average Price per Product
average_price_per_product <- aggregate(Price ~ Product, data = transactions, FUN = mean)
print("Harga Rata-rata per Produk")
print(average_price_per_product)

library(knitr)
# 4.5 Visualize Data
barplot(
  height = total_qty_per_product$Qty, 
  names.arg = total_qty_per_product$Product,
  col = c("pink", "blue", "green"),
  main = "Total Transactions",
  xlab = "Product",
  ylab = "Quantity",
  ylim = c(0, max(total_qty_per_product$Qty) + 2))

#Pie chart
revenue_per_tier <- aggregate(Total ~ CostumerTier, data = transactions, FUN = sum)
total_revenue <- sum(revenue_per_tier$Total)
percentages <- round(revenue_per_tier$Total / total_revenue * 100, 1)
pie_labels <- paste(revenue_per_tier$CostumerTier, "(", percentages, "%)", sep = "")

pie(
  x = revenue_per_tier$Total,
  labels = pie_labels,
  main = "Proportion of Total Revenue per CustomerTier",
  col = c("purple", "yellow", "red")
)

library(knitr)
# 4.6 Find the Highest date of total revenue
transactions$Revenue <- transactions$Price * transactions$Qty
total_per_date <- aggregate(Revenue ~ Date, data = transactions, FUN = sum)
highest <- total_per_date[which.max(total_per_date$Revenue), ]
print("The highest data of total Revenue")
print(total_per_date)
print(highest)

# Stacked Bar Chart
table_transactions <- with(transactions, tapply(Qty, list(Product, CostumerTier), sum, na.rm = TRUE))
table_transactions[is.na(table_transactions)] <- 0
barplot(
  t(table_transactions),
  beside = FALSE,
  col = c("brown", "gold", "orange"),
  main = "Quantity Sold per Product by CustomerTier",
  xlab = "Product",
  ylab = "Quantity Sold",
  legend.text = TRUE)
  

```

## 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()`  

```{r}
library(DT)
library(knitr)
# 5.1 Create a vector for each column
set.seed(50)
tanggal <- as.Date("2024-01-01") + sample(0:365, 30, replace = TRUE)
hewan <- c("Kucing", "Anjing", "Kelinci", "Burung",
           "Kuda", "Gajah", "Singa", "Harimau", "Zebra", "Panda", "Rusa",
           "Unta", "Kudanil", "Badak", "Beruang", "Monyet", "Gorila", "Kanguru",
           "Koala", "Serigala", "Kucing", "Anjing", "Burung", "Gajah", "Singa", 
           "Harimau", "Zebra", "Panda", "Rusa", "Unta")
hewan <- factor(hewan)

jumlah_porsi_makanan <- c(5, 5, 3, 4, 3, 
                  3, 5, 5, 5, 6, 
                  4, 4, 6, 6, 5, 
                  5, 6, 4, 4, 5, 
                  5, 4, 5, 6, 7, 
                  6, 5, 6, 4, 4)

berat <- c(24.56, 48.76, 15.48, 9.64, 289.67, 785.54, 293.27, 257.38, 158.35, 
           93.32, 82.67, 632.75, 429.71, 836.54, 274.68, 18.56, 467.82, 152.74,
           52.85, 172.92, 38.56, 95.27, 6.32, 364.78, 284.92, 256.85, 193.65,
           123.45, 110.43, 576.98)

kelincahan <- c("Tinggi", "Tinggi", "Sedang", "Tinggi", "Sedang", "Rendah", "Tinggi",
                "Tinggi", "Sedang", "Sedang", "Sedang", "Rendah", "Rendah", "Rendah",
                "Sedang", "Tinggi", "Rendah", "Tinggi", "Sedang", "Tinggi", "Sedang", 
                "Tinggi", "Tinggi", "Sedang", "Tinggi", "Tinggi", "Sedang", "Sedang",
                "Sedang", "Rendah")
kelincahan <- factor(kelincahan,
                     levels = c("Rendah", "Sedang", "Tinggi"),
                     ordered = TRUE)

# 5.2 Combine all vectors into a data frame
Data_Hewan <- data.frame(tanggal, hewan, jumlah_porsi_makanan, berat, kelincahan)

# 5.3 Create a data frame as a neat table
datatable(Data_Hewan, caption = "Rekapitulasi Data Hewan: Jenis, Berat dan Kelincahan", options = list(pageLength = 10))

# 5.3 Create a table of variable types
variables_info2 <- data.frame(
  No = 1:4,
  Variable = c(
    "Hewan",
    "JumlahPorsiMakanan",
    "Berat",
    "Kelincahan"),
  DataType = c(
    "Nominal",
    "Diskrit",
    "Continuous",
    "Ordinal"),
  stringsAsFactors = FALSE
)

knitr::kable(variables_info2, caption = "Table of Variable and Data Type")

# 5.4 Summarize each column
summary(Data_Hewan)
summary(transactions)

summary_table <- summary(Data_Hewan)
knitr::kable(as.data.frame(summary_table), caption = "Summary of Data Hewan")


# 5.4 Count the frequency of each category for **Nominal** and **Ordinal** columns
list(
  Nominal = table(Data_Hewan$Hewan),
  Ordinal = table(Data_Hewan$kelincahan)
)


```







