Second Task

Exercises Week 4

Data Science Student

Carol Dupino Pereira

NIM: 52250051

R Studio
Statistics
BS
Bakti Siregar
LinkedIn

1 Practicum

independent Practice: Conditional Statements and Loops In R

Use the following dummy dataset:

Table 1: Data Performa Karyawan
ID Name Age Salary Position Performance
1 Bagas 25 5000 Staff Good
2 Joan 30 7000 Supervisor Very Good
3 Alya 27 6500 Staff Average
4 Dwi 35 10000 Manager Good
5 Nabil 40 12000 Director Very Good 
library(dplyr)
# Simpan data ke variabel 'df_karyawan'
df_karyawan <- data.frame(
  ID = 1:5,
  Name = c("Bagas", "Joan", "Alya", "Dwi", "Nabil"),
  Age = c(25, 30, 27, 35, 40),
  Salary = c(5000, 7000, 6500, 10000, 12000),
  Position = c("Staff", "Supervisor", "Staff", "Manager", "Director"),
  Performance = c("Good", "Very Good", "Average", "Good", "Very Good")
)

# Cek apakah data sudah masuk
print(df_karyawan)
  ID  Name Age Salary   Position Performance
1  1 Bagas  25   5000      Staff        Good
2  2  Joan  30   7000 Supervisor   Very Good
3  3  Alya  27   6500      Staff     Average
4  4   Dwi  35  10000    Manager        Good
5  5 Nabil  40  12000   Director   Very Good

1.1 Conditional Statements

Determine bonus levels based on employee performence:

  • Very Good → 20% of salary
  • Good → 10% of salary
  • Average → 5% of salary
# Kita akan menggunakan variabel df_karyawan yang sudah dibuat di awal agar kamu tidak perlu menulis ulang datanya.

# Menggunakan looping dan kondisional if-else

# Logika Kondisional: Menentukan multiplier berdasarkan performa

for (i in 1:nrow(df_karyawan)) {
  perf <- df_karyawan$Performance[i]
  sal  <- df_karyawan$Salary[i]
  
  if (perf == "Very Good") {
    bonus <- sal * 0.20 # Bonus 20% jika performa sangat baik
  } else if (perf == "Good") {
    bonus <- sal * 0.10 # Bonus 10% jika performa baik
  } else if (perf == "Average") {
    bonus <- sal * 0.05 # Bonus 5% jika performa rata-rata
  } else {
    bonus <- 0
  }
  
# Tidak ada bonus jika di luar kategori tersebut

  # Menampilkan hasil dengan cat() agar output teks bersih tanpa nomor baris
  
  cat(paste0("Name: ", df_karyawan$Name[i], ", Bonus: ", bonus, "\n"))
}
Name: Bagas, Bonus: 500
Name: Joan, Bonus: 1400
Name: Alya, Bonus: 325
Name: Dwi, Bonus: 1000
Name: Nabil, Bonus: 2400

1.2 Loops(For & While)

  • Use for loop to list employess with a salary greater than 6000.
# Menggunakan For Loop untuk struktur pengulanga  dataset sesuai instruksi

# Menggunakan for loop untuk memproses baris demi baris (iterasi)

for (i in 1:nrow(df_karyawan)) {
  if (df_karyawan$Salary[i] > 6000) {
    cat(paste0(
      "Name:", df_karyawan$Name[i], 
      ", Salary:", df_karyawan$Salary[i],
      "\n"))
  }
}
Name:Joan, Salary:7000
Name:Alya, Salary:6500
Name:Dwi, Salary:10000
Name:Nabil, Salary:12000
  • Use a while loop to display employess until a “Manager” is found.
# Menggunakan While Loop untuk perulangan yang bekerja berdasarkan syarat atau kondisi 
i <- 1
while (i <= nrow(df_karyawan)) {
  posisi <- df_karyawan$Position[i]
  cat(paste0(
    "Name: ", df_karyawan$Name[i], 
    ", Position: ", posisi))
  
  if (posisi == "Manager") {
    cat(" (Stop here)\n")
    break
  }
  
  cat("\n")
  i <- i + 1
}
Name: Bagas, Position: Staff
Name: Joan, Position: Supervisor
Name: Alya, Position: Staff
Name: Dwi, Position: Manager (Stop here)
  • Use break to stop the loop when an employee with a salary above 10,000 is found.
# Berhenti saat gaji di atas 10.000

for (i in 1:nrow(df_karyawan)) {
  if (df_karyawan$Salary[i] > 10000) {
    cat(paste0(
      "(Stopped because ", df_karyawan$Name[i],
      " has a salary above 10,000)\n"))
    break
  }
  cat(paste0("Name: ", df_karyawan$Name[i], ", Salary: ",
             df_karyawan$Salary[i], "\n"))
}
Name: Bagas, Salary: 5000
Name: Joan, Salary: 7000
Name: Alya, Salary: 6500
Name: Dwi, Salary: 10000
(Stopped because Nabil has a salary above 10,000)
  • Use continue to skip employess with“Average” performence.
# "Average"digunakan sebagai label atau kategori di kolom Performance dan untuk menentukan jumlah bonus 
for (i in 1:nrow(df_karyawan)) {
  if (df_karyawan$Performance[i] == "Average") {
    next
  }
  cat(paste0(
    "Name: ", df_karyawan$Name[i], 
    ", Performance: ", df_karyawan$Performance[i],
    "\n"))
}
Name: Bagas, Performance: Good
Name: Joan, Performance: Very Good
Name: Dwi, Performance: Good
Name: Nabil, Performance: Very Good
cat("(Alya is skipped because the performance is \"Average\")\n")
(Alya is skipped because the performance is "Average")
---
title: "Second Task"
subtitle: "Exercises Week 4"  
author: "Carol Dupino Pereira"
date: "`r Sys.Date()`"
output:
  rmdformats::robobook:
    highlight: kate
    self_contained: true        
    thumbnails: true            
    lightbox: true              
    gallery: true               
    number_sections: true       
    lib_dir: libs               
    df_print: "paged"           
    code_folding: "show"        
    code_download: True         
    toc_depth: 3                
    use_bookdown: true          
    css: "Tugas2.css"
---

```{r,echo=FALSE}
library(htmltools)

HTML('
<style>
  /* Menggunakan font Inter yang bersih */
  @import url(\'https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800;900&display=swap\');

  /* Efek hover untuk card gelap di atas bg putih */
  .profile-card:hover {
    border-color: rgba(56, 189, 248, 0.5) !important;
    box-shadow: 0 25px 50px -12px rgba(0, 0, 0, 0.25) !important;
    transform: translateY(-2px);
  }

  .btn-connect:hover {
    background: #38bdf8 !important;
    color: white !important;
  }
</style>

<div style="position: relative; display: flex; justify-content: center; align-items: flex-start; background-color: #ffffff; padding: 60px 10px; font-family:"Inter, sans-serif; overflow: hidden;">
  
  <div style="position: absolute; width: 100%; height: 100%; top: 0; left: 0; opacity: 0.1; z-index: 0;">
    <svg width="100%" height="100%" viewBox="0 0 1440 800" xmlns="http://www.w3.org/2000/svg">
      <circle cx="10%" cy="10%" r="150" fill="#38bdf8" filter="blur(80px)"/>
      <circle cx="90%" cy="90%" r="200" fill="#9333ea" filter="blur(100px)"/>
    </svg>
  </div>

  <div class="profile-card" style="position: relative; background: #1e293b; border: 1px solid rgba(255, 255, 255, 0.1); border-radius: 25px; display: flex; flex-wrap: wrap; gap: 25px; padding: 30px; max-width: 550px; width: 100%; box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1); z-index: 1; transition: all 0.3s ease;">
    
    <div style="flex: 0 0 150px; z-index: 1; display: flex; flex-direction: column; align-items: center;">
      <div style="position: relative; padding: 4px; background: linear-gradient(135deg, #38bdf8, #9333ea); border-radius: 50%;">
        <img src="carol_1.jpg" 
             style="width: 140px; height: 140px; border-radius: 50%; object-fit: cover; display: block; border: 4px solid #1e293b;">
      </div>
    </div>

    <div style="flex: 1; min-width: 200px; color: #f8fafc; z-index: 1;">
      <div style="margin-bottom: 8px;">
        <span style="background: rgba(56, 189, 248, 0.1); color: #38bdf8; padding: 3px 10px; border-radius: 8px; font-size: 9px; font-weight: 800; text-transform: uppercase; letter-spacing: 1px; border: 1px solid rgba(56, 189, 248, 0.2);">
          Data Science Student
        </span>
      </div>

      <h1 style="margin: 0; font-size: 26px; font-weight: 800; background: linear-gradient(to right, #ffffff, #cbd5e1); -webkit-background-clip: text; -webkit-text-fill-color: transparent; letter-spacing: -1px; padding-bottom: 2px;">
        Carol Dupino Pereira
      </h1>
      
      <p style="font-size: 13px; color: #94a3b8; margin: 5px 0 15px 0;">
        NIM: <span style="color: #38bdf8; font-weight: 600;">52250051</span>
      </p>

      <div style="display: flex; gap: 8px; margin-bottom: 20px; flex-wrap: wrap;">
        <div style="background: rgba(255,255,255,0.05); padding: 5px 12px; border-radius: 10px; font-size: 10px; border: 1px solid rgba(255,255,255,0.05);">R Studio</div>
        <div style="background: rgba(255,255,255,0.05); padding: 5px 12px; border-radius: 10px; font-size: 10px; border: 1px solid rgba(255,255,255,0.05);">Statistics</div>
      </div>

      <div style="display: flex; align-items: center; justify-content: space-between; gap: 15px; border-top: 1px solid rgba(255,255,255,0.08); padding-top: 15px;">
        
        <div style="display: flex; align-items: center; gap: 10px;">
          <div style="width: 35px; height: 35px; background: #0f172a; border: 1px solid rgba(255,255,255,0.1); border-radius: 10px; display: flex; align-items: center; justify-content: center; font-weight: bold; font-size: 11px; color: #38bdf8;">
            BS
          </div>
          <div>
            <div style="font-size: 12px; font-weight: 600; color: #f1f5f9;">Bakti Siregar</div>
          </div>
        </div>

        <a href="https://www.linkedin.com/in/carol-dupino-pereira-a4948238a" target="_blank" style="text-decoration: none;">
          <div class="btn-connect" style="background: #ffffff; color: #0f172a; padding: 8px 16px; border-radius: 10px; font-size: 12px; font-weight: 700; display: flex; align-items: center; gap: 6px; transition: 0.3s ease;">
            <span>LinkedIn</span>
          </div>
        </a>

      </div>
    </div>
  </div>
</div>
')
```


## Practicum

independent Practice: Conditional Statements and Loops In R

Use the following **dummy dataset**:

```{r,echo=FALSE}
# # Membuat data frame sebagai basis data karyawan
data_karyawan <- data.frame(
  ID = c(1, 2, 3, 4, 5),
  Name = c("Bagas", "Joan", "Alya", "Dwi", "Nabil"),
  Age = c(25, 30, 27, 35, 40),
  Salary = c(5000, 7000, 6500, 10000, 12000),
  Position = c("Staff", "Supervisor", "Staff", "Manager", "Director"),
  Performance = c("Good", "Very Good", "Average", "Good", "Very Good")
)

#install.packages("knitr")
library(knitr)

kable(data_karyawan, caption = "Data Performa Karyawan")

```

```{r,message=FALSE,warning=FALSE,comment=""}
library(dplyr)
# Simpan data ke variabel 'df_karyawan'
df_karyawan <- data.frame(
  ID = 1:5,
  Name = c("Bagas", "Joan", "Alya", "Dwi", "Nabil"),
  Age = c(25, 30, 27, 35, 40),
  Salary = c(5000, 7000, 6500, 10000, 12000),
  Position = c("Staff", "Supervisor", "Staff", "Manager", "Director"),
  Performance = c("Good", "Very Good", "Average", "Good", "Very Good")
)

# Cek apakah data sudah masuk
print(df_karyawan)
```
### Conditional Statements

Determine **bonus levels** based on employee **performence**:

- **Very Good** → 20% of salary
- **Good** → 10% of salary 
- **Average** → 5% of salary

```{r,comment=""}
# Kita akan menggunakan variabel df_karyawan yang sudah dibuat di awal agar kamu tidak perlu menulis ulang datanya.

# Menggunakan looping dan kondisional if-else

# Logika Kondisional: Menentukan multiplier berdasarkan performa

for (i in 1:nrow(df_karyawan)) {
  perf <- df_karyawan$Performance[i]
  sal  <- df_karyawan$Salary[i]
  
  if (perf == "Very Good") {
    bonus <- sal * 0.20 # Bonus 20% jika performa sangat baik
  } else if (perf == "Good") {
    bonus <- sal * 0.10 # Bonus 10% jika performa baik
  } else if (perf == "Average") {
    bonus <- sal * 0.05 # Bonus 5% jika performa rata-rata
  } else {
    bonus <- 0
  }
  
# Tidak ada bonus jika di luar kategori tersebut

  # Menampilkan hasil dengan cat() agar output teks bersih tanpa nomor baris
  
  cat(paste0("Name: ", df_karyawan$Name[i], ", Bonus: ", bonus, "\n"))
}
```

### Loops(For & While)

- Use  **for loop** to list employess with a salary greater than **6000**.
```{r,comment=""}

# Menggunakan For Loop untuk struktur pengulanga  dataset sesuai instruksi

# Menggunakan for loop untuk memproses baris demi baris (iterasi)

for (i in 1:nrow(df_karyawan)) {
  if (df_karyawan$Salary[i] > 6000) {
    cat(paste0(
      "Name:", df_karyawan$Name[i], 
      ", Salary:", df_karyawan$Salary[i],
      "\n"))
  }
}

```

- Use a **while loop** to display employess until a **"Manager"** is found.

```{r,comment=""}
# Menggunakan While Loop untuk perulangan yang bekerja berdasarkan syarat atau kondisi 
i <- 1
while (i <= nrow(df_karyawan)) {
  posisi <- df_karyawan$Position[i]
  cat(paste0(
    "Name: ", df_karyawan$Name[i], 
    ", Position: ", posisi))
  
  if (posisi == "Manager") {
    cat(" (Stop here)\n")
    break
  }
  
  cat("\n")
  i <- i + 1
}
```

- Use **break** to stop the loop when an employee with a salary above **10,000** is found.

```{r,comment=""}
# Berhenti saat gaji di atas 10.000

for (i in 1:nrow(df_karyawan)) {
  if (df_karyawan$Salary[i] > 10000) {
    cat(paste0(
      "(Stopped because ", df_karyawan$Name[i],
      " has a salary above 10,000)\n"))
    break
  }
  cat(paste0("Name: ", df_karyawan$Name[i], ", Salary: ",
             df_karyawan$Salary[i], "\n"))
}
```

- Use **continue** to **skip** employess with**"Average"** performence.

```{r,comment=""}
# "Average"digunakan sebagai label atau kategori di kolom Performance dan untuk menentukan jumlah bonus 
for (i in 1:nrow(df_karyawan)) {
  if (df_karyawan$Performance[i] == "Average") {
    next
  }
  cat(paste0(
    "Name: ", df_karyawan$Name[i], 
    ", Performance: ", df_karyawan$Performance[i],
    "\n"))
}
cat("(Alya is skipped because the performance is \"Average\")\n")

```

