Practicum Syntax and Control Flow

Profil Mahasiswa - Anindya Kristianingputri

R Programming Programming Data Science Syntax and Contraflow

Konsep Probabilitas

Application of Looping Structures in Employee Dataset Analysis Using R

This dataset contains data of five employees with several variables: ID as employee identification, Name, Age, Salary as the amount of salary, Position as the job title within the company, and Performance as a performance rating such as Good, Very Good, and Average.

This data serves as a simple example to practice the use of loops in R programming, specifically for loops and while loops, in conducting dataset analysis. Through this exercise, we can understand how to traverse each row of data, perform specific condition checks, and control the flow of loops using the break command to stop the process and continue to skip data that does not meet certain conditions.

suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))

# Employee dataset
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")
)

# table
kable(data_karyawan, caption = "Employee Dataset") %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped","bordered")) %>%
  row_spec(0, background = "#6F4E37", color = "white")

Employee Dataset
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

1 Conditional Statement

In R programming, a conditional statement is a control structure used to execute specific commands based on a logical condition that evaluates to TRUE or FALSE. In R, conditional statements typically use the commands if, else if, and else to evaluate a condition and determine the action to be executed by the program. This structure is often used in data analysis to check variable values, group data based on certain criteria, or determine the next steps in the data processing workflow.

Question: A company wants to calculate employee bonuses based on their performance. Employees with Very Good performance receive 20% of their salary as a bonus, employees with Good performance receive 10% of their salary, and employees with Average performance receive 5% of their salary. Using these rules, write a program to determine the bonus level for each employee and calculate the bonus amount based on their salary.

# Employee dataset
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")
)

# Calculate the bonus and display the output
for(i in 1:nrow(data_karyawan)){
  
  if(data_karyawan$Performance[i] == "Very Good"){
    bonus <- 0.20 * data_karyawan$Salary[i]
    
  } else if(data_karyawan$Performance[i] == "Good"){
    bonus <- 0.10 * data_karyawan$Salary[i]
    
  } else if(data_karyawan$Performance[i] == "Average"){
    bonus <- 0.05 * data_karyawan$Salary[i]
    
  } else{
    bonus <- 0
  }
  
  cat(paste0("Name: ", data_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

suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))

library(knitr)
library(kableExtra)

# Employee dataset
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")
)

# Create bonus column
bonus <- c()

for(i in 1:nrow(data_karyawan)){
  
  if(data_karyawan$Performance[i] == "Very Good"){
    bonus[i] <- 0.20 * data_karyawan$Salary[i]
    
  } else if(data_karyawan$Performance[i] == "Good"){
    bonus[i] <- 0.10 * data_karyawan$Salary[i]
    
  } else if(data_karyawan$Performance[i] == "Average"){
    bonus[i] <- 0.05 * data_karyawan$Salary[i]
    
  } else{
    bonus[i] <- 0
  }
}

# Result data 
hasil_bonus <- data.frame(
  Name = data_karyawan$Name,
  Bonus = bonus
)

kable(hasil_bonus, caption = "Employee Bonus Result") %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped","bordered")) %>%
  row_spec(0, background = "#6F4E37", color = "white") %>%
  column_spec(1:2, border_left = TRUE, border_right = TRUE)

Employee Bonus Result
Name	Bonus
Bagas	500
Joan	1400
Alya	325
Dwi	1000
Nabil	2400

2 Loops (For & While)

2.1 for loop

A for loop is used when the number of iterations is already known in advance. The loop begins by initializing a counter, then checking a condition before executing the code inside the loop. After each execution, the counter increases, and the process repeats until the condition is no longer satisfied.

Instruction:

Use a for loop to display the names of employees whose salary is greater than 6000.

# Employee dataset
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)
)

# For loop to display employees with salary greater than 6000
for(i in 1:nrow(data_karyawan)) {
  if(data_karyawan$Salary[i] > 6000) {
    cat("Name:", data_karyawan$Name[i], 
        ", Salary:", data_karyawan$Salary[i], "\n")
  }
}

## Name: Joan , Salary: 7000 
## Name: Alya , Salary: 6500 
## Name: Dwi , Salary: 10000 
## Name: Nabil , Salary: 12000

suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))

# Employee dataset
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)
)

# Store the results
nama <- c()
gaji <- c()

# For loop to find salaries greater than 6000
for(i in 1:nrow(data_karyawan)) {
  if(data_karyawan$Salary[i] > 6000) {
    nama <- c(nama, data_karyawan$Name[i])
    gaji <- c(gaji, data_karyawan$Salary[i])
  }
}

# Result data
hasil <- data.frame(
  Name = nama,
  Salary = gaji
)

# table
kable(hasil, caption = "Employees with Salary Greater Than 6000") %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped","bordered")) %>%
  row_spec(0, background = "#6F4E37", color = "white")

Employees with Salary Greater Than 6000
Name	Salary
Joan	7000
Alya	6500
Dwi	10000
Nabil	12000

2.2 While Loop

A while loop is used when a process needs to continue running as long as a certain condition remains true. The loop repeatedly executes the code and checks the condition after each iteration. This process continues until the condition eventually becomes false, at which point the loop stops.

Instruction:

Use a while loop to display employees until a “Manager” is found.

# Employee dataset
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")
)

# While loop to display employees until a Manager is found
i <- 1

while(i <= nrow(data_karyawan)) {
  
  if(data_karyawan$Position[i] == "Manager") {
    cat("Name:", data_karyawan$Name[i],
        ", Position:", data_karyawan$Position[i],
        "(Stop here)\n")
    break
  }
  
  cat("Name:", data_karyawan$Name[i],
      ", Position:", data_karyawan$Position[i], "\n")
  
  i <- i + 1
}

## Name: Bagas , Position: Staff 
## Name: Joan , Position: Supervisor 
## Name: Alya , Position: Staff 
## Name: Dwi , Position: Manager (Stop here)

suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))

# Employee dataset
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")
)

# Store the results
nama <- c()
posisi <- c()

i <- 1

# While loop
while(i <= nrow(data_karyawan)){
  
  nama <- c(nama, data_karyawan$Name[i])
  posisi <- c(posisi, data_karyawan$Position[i])
  
  if(data_karyawan$Position[i] == "Manager"){
    posisi[length(posisi)] <- paste(posisi[length(posisi)], "(Stop here)")
    break
  }
  
  i <- i + 1
}

# Result 
hasil <- data.frame(
  Name = nama,
  Position = posisi
)

# table
kable(hasil, caption = " Until Manager Found") %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped","bordered")) %>%
  row_spec(0, background = "#6F4E37", color = "white")

Until Manager Found
Name	Position
Bagas	Staff
Joan	Supervisor
Alya	Staff
Dwi	Manager (Stop here)

2.3 Break

A break statement is used to stop a loop immediately before it finishes all of its iterations. When the break condition is met, the program exits the loop right away. This allows the loop to terminate early without continuing the remaining repetitions.

Instruction:

Use break to stop the loop when an employee with a salary above 10,000 is found.

# Employee dataset
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)
)

# For loop with break when salary above 10000
for(i in 1:nrow(data_karyawan)) {
  
  if(data_karyawan$Salary[i] > 10000) {
    break
  }
  
  cat("Name:", data_karyawan$Name[i], 
      ", Salary:", data_karyawan$Salary[i], "\n")
}

## Name: Bagas , Salary: 5000 
## Name: Joan , Salary: 7000 
## Name: Alya , Salary: 6500 
## Name: Dwi , Salary: 10000

suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))

# Employee dataset
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)
)

# Store results
nama <- c()
gaji <- c()

# For loop with break
for(i in 1:nrow(data_karyawan)) {
  
  if(data_karyawan$Salary[i] > 10000) {
    break
  }
  
  nama <- c(nama, data_karyawan$Name[i])
  gaji <- c(gaji, data_karyawan$Salary[i])
}

# Result data
hasil <- data.frame(
  Name = nama,
  Salary = gaji
)

# table
kable(hasil, caption = "Until Salary Above 10,000 Found") %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped","bordered")) %>%
  row_spec(0, background = "#6F4E37", color = "white")

Until Salary Above 10,000 Found
Name	Salary
Bagas	5000
Joan	7000
Alya	6500
Dwi	10000

2.4 Continue

The continue statement skips the current iteration without stopping the loop and immediately moves to the next iteration. The loop then checks the condition again before executing the next cycle. The loop will stop when the condition is no longer satisfied or when a break statement is encountered. If the condition remains true, the loop continues to run unless it is interrupted by a break.

Instruction:

Use continue to skip employees with “Average” performance.

# Employee dataset
data_karyawan <- data.frame(
  ID = c(1,2,3,4,5),
  Name = c("Bagas","Joan","Alya","Dwi","Nabil"),
  Performance = c("Good","Very Good","Average","Good","Very Good")
)

# For loop using next (continue) to skip "Average"
for(i in 1:nrow(data_karyawan)) {
  
  if(data_karyawan$Performance[i] == "Average") {
    next
  }
  
  cat("Name:", data_karyawan$Name[i], 
      ", Performance:", data_karyawan$Performance[i], "\n")
}

## Name: Bagas , Performance: Good 
## Name: Joan , Performance: Very Good 
## Name: Dwi , Performance: Good 
## Name: Nabil , Performance: Very Good

suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))

# Employee dataset
data_karyawan <- data.frame(
  ID = c(1,2,3,4,5),
  Name = c("Bagas","Joan","Alya","Dwi","Nabil"),
  Performance = c("Good","Very Good","Average","Good","Very Good")
)

# Store results
nama <- c()
perf <- c()

# For loop using next (continue)
for(i in 1:nrow(data_karyawan)) {
  
  if(data_karyawan$Performance[i] == "Average") {
    next
  }
  
  nama <- c(nama, data_karyawan$Name[i])
  perf <- c(perf, data_karyawan$Performance[i])
}

# Result data
hasil <- data.frame(
  Name = nama,
  Performance = perf
)

# table
kable(hasil, caption = "Employees with Non-Average") %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped","bordered")) %>%
  row_spec(0, background = "#6F4E37", color = "white")

Employees with Non-Average
Name	Performance
Bagas	Good
Joan	Very Good
Dwi	Good
Nabil	Very Good

Conclusion

This activity demonstrates how conditional statements enable programs to make decisions based on specific criteria. Here, conditions are used to assign employee bonus percentages according to performance levels such as Very Good, Good, and Average. Through these logical structures, the program can automatically compute the appropriate bonus from each employee's salary, enhancing both efficiency and consistency in the process.

Moreover, looping mechanisms like the for and while loops, combined with control statements such as break and continue, streamline repetitive operations involved in handling employee records. The for loop proves effective for scanning datasets to identify employees meeting particular conditions, while the while loop continues execution until a specified condition is fulfilled. The break statement terminates a loop prematurely when a desired condition is met, and continue allows the loop to bypass certain data without halting entirely. Collectively, these programming constructs contribute to the development of efficient systems for managing and analyzing employee data.

---
title: "Practicum Syntax and Control Flow"            # Main title of the document
author: "Anindya Kristianingputri"      # 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
---

```{=html}
<style>
h1.title {
  font-size: 47px !important;
  font-weight: 800;
  text-align: center;
  font-family: "Hiragino Maru Gothic", "Yu Gothic", "MS PGothic", sans-serif !important;
  font-style: italic !important;
  margin-bottom: 20px !important;
  line-height: 1.2;
  position: relative;
  padding-bottom: 15px !important;
}

h1.title::after {
  content: "";
  position: absolute;
  bottom: 0;
  left: 50%;
  transform: translateX(-50%);
  width: 150px;
  height: 2px;
  background: currentColor;
  opacity: 0.3;
}

.perfect-center-box {
  position: relative;
  padding: 25px 40px !important;
  margin: 20px auto !important;
  text-align: center;
  display: table;
  background: #fff9f9;
  border: 1px solid #ffb6c1;
  border-radius: 25px;
  box-shadow: 0 4px 15px rgba(255, 182, 193, 0.3);
  max-width: 500px;
  font-family: "Kawaii", "Comic Sans MS", "Chalkboard", cursive !important;
}

.perfect-center-box::before,
.perfect-center-box::after {
  content: "✿";
  position: absolute;
  width: auto;
  height: auto;
  font-size: 24px;
  color: #ff99cc;
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}

.perfect-center-box::before {
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}

.perfect-center-box::after {
  bottom: 5px;
  right: 10px;
  border: none;
}

.perfect-center-box:hover::before,
.perfect-center-box:hover::after {
  transform: scale(1.2);
  opacity: 1;
}

.week-number {
  font-size: 18px !important;
  font-weight: 700;
  letter-spacing: 0.5px;
  margin-bottom: 5px !important;
  line-height: 1.3;
  font-family: "Rounded Mplus 1c", "Mochiy Pop One", "Chalkboard", sans-serif !important;
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}

.assignment-title {
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  font-weight: 500;
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  line-height: 1.3;
  font-family: "Kawaii", "Mochiy Pop P One", "Chalkboard SE", cursive !important;
  color: #9370db;
}

.assignment-title::before {
  content: "✦";
  position: absolute;
  top: -5px;
  left: 50%;
  transform: translateX(-50%);
  width: auto;
  height: auto;
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  opacity: 0.5;
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}

.container {
  margin: 0 !important;
  padding: 20px !important;
  background: linear-gradient(135deg, #fff0f5 0%, #ffe4e1 100%);
}

.content-wrapper {
  display: flex;
  flex-direction: column;
  gap: 15px !important;
  align-items: center;
}
</style>
```

```{=html}
<!DOCTYPE html>
<html lang="id">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Profil Mahasiswa - Anindya Kristianingputri</title>
    <style>
        * {
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }

        body {
            font-family: 'Playfair Display', 'Georgia', serif;
            background-image: repeating-linear-gradient(45deg, 
                #5D3A1A 0px, #5D3A1A 2px,
                #7B4F2B 2px, #7B4F2B 8px,
                #5D3A1A 8px, #5D3A1A 10px,
                #8B5A2B 10px, #8B5A2B 16px
            );
            color: #2C1810;
            line-height: 1.5;
            padding: 20px;
            display: flex;
            justify-content: center;
            align-items: center;
            min-height: 100vh;
            position: relative;
        }

        body::before {
            content: "";
            position: absolute;
            top: 0;
            left: 0;
            right: 0;
            bottom: 0;
            background: rgba(139, 69, 19, 0.15);
            pointer-events: none;
        }

        .container {
            max-width: 750px;
            width: 100%;
            background: #F4E6D9;
            background-image: url('data:image/svg+xml;utf8,<svg xmlns="http://www.w3.org/2000/svg" width="80" height="80" viewBox="0 0 80 80"><path d="M0 0 L80 0 L80 80 L0 80 Z" fill="%23F4E6D9"/><path d="M15 0 L25 0 L20 80 L10 80" fill="%23D2B48C" opacity="0.08"/><path d="M35 0 L45 0 L40 80 L30 80" fill="%23D2B48C" opacity="0.08"/><path d="M55 0 L65 0 L60 80 L50 80" fill="%23D2B48C" opacity="0.08"/></svg>');
            border-radius: 24px;
            box-shadow: 0 15px 30px rgba(70, 35, 10, 0.4);
            overflow: hidden;
            border: 6px solid #8B5A2B;
            position: relative;
        }

        .container::before {
            content: "";
            position: absolute;
            top: 8px;
            left: 8px;
            right: 8px;
            bottom: 8px;
            border: 1px dashed #C19A6B;
            border-radius: 16px;
            pointer-events: none;
            opacity: 0.3;
        }

        .header {
            background: linear-gradient(135deg, #6B4F32, #8B5A2B);
            color: #FFEBCD;
            padding: 35px 30px 30px 30px;
            position: relative;
            border-bottom: 3px solid #C19A6B;
        }

        .profile-content {
            display: flex;
            align-items: center;
            gap: 30px;
            position: relative;
            z-index: 2;
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        .photo-container {
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            position: relative;
            transition: transform 0.3s ease;
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        .photo-container:hover {
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            font-family: 'Brush Script MT', 'Lucida Handwriting', cursive;
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            text-align: left;
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        .nim {
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            background: #C19A6B;
            color: #2C1810;
            padding: 5px 15px;
            border-radius: 25px;
            margin-bottom: 12px;
            display: inline-block;
            font-weight: 600;
            border: 1px solid #8B5A2B;
            font-family: 'Courier New', monospace;
            box-shadow: 0 2px 5px rgba(0, 0, 0, 0.15);
            text-align: left;
        }

        .study-info {
            font-size: 15px;
            margin-bottom: 6px;
            color: #F5DEB3;
            font-family: 'Palatino', 'Georgia', serif;
            font-style: italic;
            line-height: 1.5;
            text-align: left;
        }

        .study-info p {
            margin-bottom: 3px;
        }

        .dosen-info {
            font-size: 14px;
            margin-top: 12px;
            font-weight: 600;
            color: #FFDAB9;
            background: #5D3A1A;
            padding: 6px 16px;
            border-radius: 30px;
            display: inline-block;
            border: 1px solid #C19A6B;
            font-family: 'Trebuchet MS', sans-serif;
            letter-spacing: 0.5px;
            text-align: left;
        }

        .skills-section {
            padding: 20px 25px;
            background: #E6D5B8;
            background-image: repeating-linear-gradient(0deg, 
                transparent, 
                transparent 25px, 
                rgba(139, 69, 19, 0.03) 25px, 
                rgba(139, 69, 19, 0.03) 50px
            );
            text-align: center;
            position: relative;
        }

        .skills-section::before {
            content: "";
            position: absolute;
            top: 5px;
            left: 15px;
            width: 20px;
            height: 20px;
            border-left: 1.5px solid #8B5A2B;
            border-top: 1.5px solid #8B5A2B;
            opacity: 0.25;
        }

        .skills-section::after {
            content: "";
            position: absolute;
            bottom: 5px;
            right: 15px;
            width: 20px;
            height: 20px;
            border-right: 1.5px solid #8B5A2B;
            border-bottom: 1.5px solid #8B5A2B;
            opacity: 0.25;
        }

        .skills-list {
            display: flex;
            justify-content: center;
            flex-wrap: wrap;
            gap: 12px;
        }

        .skill-item {
            background: #8B5A2B;
            color: #FFF5E6;
            padding: 8px 18px;
            border-radius: 30px;
            font-size: 13px;
            border: 1.5px solid #DEB887;
            box-shadow: 0 3px 0 #5D3A1A, 0 6px 10px rgba(70, 35, 10, 0.15);
            transition: all 0.2s ease;
            font-weight: 600;
            font-family: 'Garamond', 'Times New Roman', serif;
            text-transform: uppercase;
            letter-spacing: 0.8px;
        }

        .skill-item:nth-child(2) {
            background: #9B6E3A;
        }

        .skill-item:hover {
            transform: translateY(-2px);
            box-shadow: 0 2px 0 #5D3A1A, 0 8px 12px rgba(70, 35, 10, 0.2);
            background: #A87B45;
            border-color: #F4A460;
        }

        .wood-decoration {
            position: absolute;
            width: 100%;
            height: 100%;
            pointer-events: none;
        }

        .wood-decoration span {
            position: absolute;
            background: #C19A6B;
            opacity: 0.08;
            border-radius: 8px;
        }

        .wood-decoration span:nth-child(1) {
            top: 15px;
            left: 15px;
            width: 40px;
            height: 8px;
            transform: rotate(25deg);
        }

        .wood-decoration span:nth-child(2) {
            bottom: 20px;
            right: 20px;
            width: 60px;
            height: 10px;
            transform: rotate(-15deg);
        }

        @media (max-width: 600px) {
            .profile-content {
                flex-direction: column;
                text-align: center;
                gap: 15px;
            }

            .profile-info {
                text-align: center;
            }

            .name {
                text-align: center;
            }

            .nim {
                text-align: center;
            }

            .study-info {
                text-align: center;
            }

            .dosen-info {
                text-align: center;
            }

            .header {
                padding: 25px 20px;
            }
            
            .photo-container {
                width: 110px;
                height: 110px;
            }
            
            .name {
                font-size: 24px;
            }
            
            .skills-section {
                padding: 18px 15px;
            }
            
            .skill-item {
                padding: 6px 14px;
                font-size: 12px;
            }
        }
    </style>
</head>
<body>
    <div class="container">
        <div class="wood-decoration">
            <span></span>
            <span></span>
        </div>
        
        <header class="header">
            <div class="profile-content">
                <div class="photo-container">
                    <img src="C:/Users/WINDOWS 11/OneDrive/Desktop/Pemrograman Sains Data Semester 2/profile.png" 
                         alt="Foto Profil Anindya Kristianingputri" 
                         class="profile-photo">
                </div>
                
                <div class="profile-info">
                    <h1 class="name">Anindya Kristianingputri</h1>
                    <div class="nim">NIM: 52250025</div>
                    <div class="study-info">
                        <p>Student Major Data Science</p>
                        <p>Bandung Institute of Science and Technology</p>
                    </div>
                    <div class="dosen-info">
                        Lecture: Bakti Siregar, M.Sc., CDS
                    </div>
                </div>
            </div>
        </header>

        <div class="skills-section">
            <div class="skills-list">
                <span class="skill-item">R Programming</span>
                <span class="skill-item">Programming Data Science</span>
                <span class="skill-item">Syntax and Contraflow</span>
            </div>
        </div>
    </div>
</body>
</html>
```


```{=html}
<!DOCTYPE html>
<html lang="id">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Konsep Probabilitas</title>
    <style>
        .probabilitas-box {
            background-color: #8B4513;  
            color: white;               
            padding: 30px;
            border-radius: 8px;
            max-width: 800px;
            margin: 20px auto;
            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
            line-height: 1.8;             /* Jarak antar baris lebih lega */
            text-align: justify;           
            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
        }
        
        .probabilitas-box p {
            margin: 0 0 15px 0;            /* Jarak antar paragraf */
            font-size: 16px;
        }

        .probabilitas-box strong {
            color: #FFD700;                /* Warna emas untuk judul */
        }
    </style>
</head>
<body>
    <div class="probabilitas-box">
        <p><strong>Application of Looping Structures in Employee Dataset Analysis Using R</strong></p>
        <p>This dataset contains data of five employees with several variables: ID as employee identification, Name, Age, Salary as the amount of salary, Position as the job title within the company, and Performance as a performance rating such as Good, Very Good, and Average.</p>
        <p>This data serves as a simple example to practice the use of loops in R programming, specifically for loops and while loops, in conducting dataset analysis. Through this exercise, we can understand how to traverse each row of data, perform specific condition checks, and control the flow of loops using the break command to stop the process and continue to skip data that does not meet certain conditions.</p>
    </div>
</body>
</html>
```


```{r}
suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))

# Employee dataset
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")
)

# table
kable(data_karyawan, caption = "Employee Dataset") %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped","bordered")) %>%
  row_spec(0, background = "#6F4E37", color = "white")
```

# Conditional Statement 

::: {style="background-color:#f0f5ff; border-left:6px solid #000080; padding:12px; border-radius:8px; margin:20px 0;"}
<p style="text-align: justify; text-justify: inter-word;">
In R programming, a conditional statement is a control structure used to execute specific commands based on a logical condition that evaluates to TRUE or FALSE. In R, conditional statements typically use the commands if, else if, and else to evaluate a condition and determine the action to be executed by the program. This structure is often used in data analysis to check variable values, group data based on certain criteria, or determine the next steps in the data processing workflow.
</p>
:::

:::{style="background-color:#D1FFFA; border-left:6px solid #000080; padding:12px; border-radius:8px; margin:20px 0;"}
<p style="text-align: justify; text-justify: inter-word;">
**Question:**
A company wants to calculate employee bonuses based on their performance. Employees with **Very Good** performance receive **20% of their salary as a bonus**, employees with **Good** performance receive **10% of their salary**, and employees with **Average** performance receive **5% of their salary**. Using these rules, write a program to determine the bonus level for each employee and calculate the bonus amount based on their salary.
</p>
:::

```{r}
# Employee dataset
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")
)

# Calculate the bonus and display the output
for(i in 1:nrow(data_karyawan)){
  
  if(data_karyawan$Performance[i] == "Very Good"){
    bonus <- 0.20 * data_karyawan$Salary[i]
    
  } else if(data_karyawan$Performance[i] == "Good"){
    bonus <- 0.10 * data_karyawan$Salary[i]
    
  } else if(data_karyawan$Performance[i] == "Average"){
    bonus <- 0.05 * data_karyawan$Salary[i]
    
  } else{
    bonus <- 0
  }
  
  cat(paste0("Name: ", data_karyawan$Name[i], ", Bonus: ", bonus, "\n"))
}
```


```{r}
suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))

library(knitr)
library(kableExtra)

# Employee dataset
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")
)

# Create bonus column
bonus <- c()

for(i in 1:nrow(data_karyawan)){
  
  if(data_karyawan$Performance[i] == "Very Good"){
    bonus[i] <- 0.20 * data_karyawan$Salary[i]
    
  } else if(data_karyawan$Performance[i] == "Good"){
    bonus[i] <- 0.10 * data_karyawan$Salary[i]
    
  } else if(data_karyawan$Performance[i] == "Average"){
    bonus[i] <- 0.05 * data_karyawan$Salary[i]
    
  } else{
    bonus[i] <- 0
  }
}

# Result data 
hasil_bonus <- data.frame(
  Name = data_karyawan$Name,
  Bonus = bonus
)

kable(hasil_bonus, caption = "Employee Bonus Result") %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped","bordered")) %>%
  row_spec(0, background = "#6F4E37", color = "white") %>%
  column_spec(1:2, border_left = TRUE, border_right = TRUE)
```



# Loops (For & While)

## for loop 

::: {style="background-color:#f0f5ff; border-left:6px solid #000080; padding:12px; border-radius:8px; margin:20px 0;"}
<p style="text-align: justify; text-justify: inter-word;">
A **for loop** is used when the number of iterations is already known in advance. The loop begins by initializing a counter, then checking a condition before executing the code inside the loop. After each execution, the counter increases, and the process repeats until the condition is no longer satisfied.
</p>
:::


:::{style="background-color:#D1FFFA; border-left:6px solid #000080; padding:12px; border-radius:8px; margin:20px 0;"}
<p style="text-align: justify; text-justify: inter-word;">
**Instruction:**

Use a for loop to display the names of employees whose salary is greater than 6000.
</p>
:::


```{r}
# Employee dataset
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)
)

# For loop to display employees with salary greater than 6000
for(i in 1:nrow(data_karyawan)) {
  if(data_karyawan$Salary[i] > 6000) {
    cat("Name:", data_karyawan$Name[i], 
        ", Salary:", data_karyawan$Salary[i], "\n")
  }
}
```

```{r}
suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))

# Employee dataset
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)
)

# Store the results
nama <- c()
gaji <- c()

# For loop to find salaries greater than 6000
for(i in 1:nrow(data_karyawan)) {
  if(data_karyawan$Salary[i] > 6000) {
    nama <- c(nama, data_karyawan$Name[i])
    gaji <- c(gaji, data_karyawan$Salary[i])
  }
}

# Result data
hasil <- data.frame(
  Name = nama,
  Salary = gaji
)

# table
kable(hasil, caption = "Employees with Salary Greater Than 6000") %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped","bordered")) %>%
  row_spec(0, background = "#6F4E37", color = "white")
```



## While Loop 


::: {style="background-color:#f0f5ff; border-left:6px solid #000080; padding:12px; border-radius:8px; margin:20px 0;"}
<p style="text-align: justify; text-justify: inter-word;">
A **while loop** is used when a process needs to continue running as long as a certain condition remains true. The loop repeatedly executes the code and checks the condition after each iteration. This process continues until the condition eventually becomes false, at which point the loop stops.
</p>
:::

:::{style="background-color:#D1FFFA; border-left:6px solid #000080; padding:12px; border-radius:8px; margin:20px 0;"}
<p style="text-align: justify; text-justify: inter-word;">
**Instruction:**

Use a while loop to display employees until a “Manager” is found.
</p>
:::


```{r}
# Employee dataset
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")
)

# While loop to display employees until a Manager is found
i <- 1

while(i <= nrow(data_karyawan)) {
  
  if(data_karyawan$Position[i] == "Manager") {
    cat("Name:", data_karyawan$Name[i],
        ", Position:", data_karyawan$Position[i],
        "(Stop here)\n")
    break
  }
  
  cat("Name:", data_karyawan$Name[i],
      ", Position:", data_karyawan$Position[i], "\n")
  
  i <- i + 1
}
```


```{r}
suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))

# Employee dataset
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")
)

# Store the results
nama <- c()
posisi <- c()

i <- 1

# While loop
while(i <= nrow(data_karyawan)){
  
  nama <- c(nama, data_karyawan$Name[i])
  posisi <- c(posisi, data_karyawan$Position[i])
  
  if(data_karyawan$Position[i] == "Manager"){
    posisi[length(posisi)] <- paste(posisi[length(posisi)], "(Stop here)")
    break
  }
  
  i <- i + 1
}

# Result 
hasil <- data.frame(
  Name = nama,
  Position = posisi
)

# table
kable(hasil, caption = " Until Manager Found") %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped","bordered")) %>%
  row_spec(0, background = "#6F4E37", color = "white")
```


## Break 


::: {style="background-color:#f0f5ff; border-left:6px solid #000080; padding:12px; border-radius:8px; margin:20px 0;"}
<p style="text-align: justify; text-justify: inter-word;">
A **break statement** is used to stop a loop immediately before it finishes all of its iterations. When the break condition is met, the program exits the loop right away. This allows the loop to terminate early without continuing the remaining repetitions.
</p>
:::

:::{style="background-color:#D1FFFA; border-left:6px solid #000080; padding:12px; border-radius:8px; margin:20px 0;"}
<p style="text-align: justify; text-justify: inter-word;">
**Instruction:**

Use break to stop the loop when an employee with a salary above 10,000 is found.
</p>
:::

```{r}
# Employee dataset
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)
)

# For loop with break when salary above 10000
for(i in 1:nrow(data_karyawan)) {
  
  if(data_karyawan$Salary[i] > 10000) {
    break
  }
  
  cat("Name:", data_karyawan$Name[i], 
      ", Salary:", data_karyawan$Salary[i], "\n")
}
```


```{r}
suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))

# Employee dataset
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)
)

# Store results
nama <- c()
gaji <- c()

# For loop with break
for(i in 1:nrow(data_karyawan)) {
  
  if(data_karyawan$Salary[i] > 10000) {
    break
  }
  
  nama <- c(nama, data_karyawan$Name[i])
  gaji <- c(gaji, data_karyawan$Salary[i])
}

# Result data
hasil <- data.frame(
  Name = nama,
  Salary = gaji
)

# table
kable(hasil, caption = "Until Salary Above 10,000 Found") %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped","bordered")) %>%
  row_spec(0, background = "#6F4E37", color = "white")
```


## Continue 



::: {style="background-color:#f0f5ff; border-left:6px solid #000080; padding:12px; border-radius:8px; margin:20px 0;"}
<p style="text-align: justify; text-justify: inter-word;">
The **continue statement** skips the current iteration without stopping the loop and immediately moves to the next iteration. The loop then checks the condition again before executing the next cycle. The loop will stop when the condition is no longer satisfied or when a break statement is encountered. If the condition remains true, the loop continues to run unless it is interrupted by a break.
</p>
:::

:::{style="background-color:#D1FFFA; border-left:6px solid #000080; padding:12px; border-radius:8px; margin:20px 0;"}
<p style="text-align: justify; text-justify: inter-word;">
**Instruction:**

Use continue to skip employees with “Average” performance.
</p>
:::


```{r}
# Employee dataset
data_karyawan <- data.frame(
  ID = c(1,2,3,4,5),
  Name = c("Bagas","Joan","Alya","Dwi","Nabil"),
  Performance = c("Good","Very Good","Average","Good","Very Good")
)

# For loop using next (continue) to skip "Average"
for(i in 1:nrow(data_karyawan)) {
  
  if(data_karyawan$Performance[i] == "Average") {
    next
  }
  
  cat("Name:", data_karyawan$Name[i], 
      ", Performance:", data_karyawan$Performance[i], "\n")
}
```

```{r}
suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))

# Employee dataset
data_karyawan <- data.frame(
  ID = c(1,2,3,4,5),
  Name = c("Bagas","Joan","Alya","Dwi","Nabil"),
  Performance = c("Good","Very Good","Average","Good","Very Good")
)

# Store results
nama <- c()
perf <- c()

# For loop using next (continue)
for(i in 1:nrow(data_karyawan)) {
  
  if(data_karyawan$Performance[i] == "Average") {
    next
  }
  
  nama <- c(nama, data_karyawan$Name[i])
  perf <- c(perf, data_karyawan$Performance[i])
}

# Result data
hasil <- data.frame(
  Name = nama,
  Performance = perf
)

# table
kable(hasil, caption = "Employees with Non-Average") %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped","bordered")) %>%
  row_spec(0, background = "#6F4E37", color = "white")
```


```{=html}
<div style="
background-color:#8B4513;
color:#ffffff;
padding:35px;
border-radius:12px;
max-width:900px;
margin:30px auto;
font-family:'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
line-height:1.9;
text-align:justify;
box-shadow:0 6px 14px rgba(0,0,0,0.25);
">

<p style="font-size:22px; font-weight:700; color:#FFC107; margin-bottom:18px;">
Conclusion
</p>

<p style="font-size:17px;">
This activity demonstrates how <strong>conditional statements</strong> enable programs to make decisions based on specific criteria. Here, conditions are used to assign employee bonus percentages according to performance levels such as <em>Very Good</em>, <em>Good</em>, and <em>Average</em>. Through these logical structures, the program can automatically compute the appropriate bonus from each employee's salary, enhancing both efficiency and consistency in the process.
</p>

<p style="font-size:17px;">
Moreover, <strong>looping mechanisms</strong> like the <strong>for</strong> and <strong>while</strong> loops, combined with control statements such as <strong>break</strong> and <strong>continue</strong>, streamline repetitive operations involved in handling employee records. The <strong>for loop</strong> proves effective for scanning datasets to identify employees meeting particular conditions, while the <strong>while loop</strong> continues execution until a specified condition is fulfilled. The <strong>break</strong> statement terminates a loop prematurely when a desired condition is met, and <strong>continue</strong> allows the loop to bypass certain data without halting entirely. Collectively, these programming constructs contribute to the development of efficient systems for managing and analyzing employee data.
</p>

</div>
```
