# Load necessary library
library(ggplot2)
library(gridExtra)

# Set seed for reproducibility
set.seed(123)

# Generate example student data
num_students <- 15
students <- data.frame(
  Name = paste("Student", 1:num_students),
  Scores = sample(60:100, num_students, replace = TRUE),
  Attendance = sample(60:100, num_students, replace = TRUE)
)

# Task 1: Scatter Plot - Scores vs Attendance
scatter_plot <- ggplot(students, aes(x = Scores, y = Attendance)) +
  geom_point(color = "red") +
  labs(
    title = "Scatter Plot - Scores vs Attendance",
    x = "Scores",
    y = "Attendance"
  )

# Task 2: Bar Plot - Distribution of Scores
bar_plot <- ggplot(students, aes(x = Name, y = Scores)) +
  geom_bar(stat = "identity", fill = "orange") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(
    title = "Bar Plot - Distribution of Scores",
    x = "Student Names",
    y = "Scores"
  )

# Add a Time column for trend visualization
students$Time <- seq_len(num_students)

# Task 3: Line Plot - Trend of Scores Over Time
line_plot <- ggplot(students, aes(x = Time, y = Scores, group = 1)) +
  geom_line(color = "purple") +
  labs(
    title = "Line Plot - Trend of Scores Over Time",
    x = "Time",
    y = "Scores"
  )

# Task 4: Histogram - Distribution of Scores
histogram_plot <- ggplot(students, aes(x = Scores)) +
  geom_histogram(binwidth = 10, fill = "brown", color = "black") +
  labs(
    title = "Histogram - Distribution of Scores",
    x = "Scores",
    y = "Frequency"
  )

# Combine and arrange the plots for easy comparison
grid.arrange(scatter_plot, bar_plot, line_plot, histogram_plot, ncol = 2)