Functions in R

Author

IND215

Published

September 22, 2025

Introduction to Functions

Functions are one of the most powerful features in R. They allow you to:

  • Reuse code: Write once, use many times
  • Organize code: Break complex problems into smaller pieces
  • Reduce errors: Centralize logic in one place
  • Make code readable: Give meaningful names to operations

In R, functions are “first-class objects” - they can be assigned to variables, passed as arguments, and returned from other functions.

Using Built-in Functions

R comes with hundreds of built-in functions. You’ve already used many of them!

Common Mathematical Functions

# Basic mathematical functions
numbers <- c(1, 4, 9, 16, 25)

sqrt(numbers)        # Square root
[1] 1 2 3 4 5
abs(c(-5, -2, 3))    # Absolute value
[1] 5 2 3
round(3.14159, 2)    # Round to 2 decimal places
[1] 3.14
ceiling(3.2)         # Round up
[1] 4
floor(3.8)           # Round down
[1] 3
# Trigonometric functions
angles <- c(0, pi/4, pi/2, pi)
sin(angles)
[1] 0.000000e+00 7.071068e-01 1.000000e+00 1.224647e-16
cos(angles)
[1]  1.000000e+00  7.071068e-01  6.123234e-17 -1.000000e+00

Statistical Functions

# Sample data
scores <- c(85, 92, 78, 96, 88, 74, 91, 89)

# Central tendency
mean(scores)         # Average
[1] 86.625
median(scores)       # Middle value
[1] 88.5
mode(scores)         # Most frequent (not built-in, but let's see)
[1] "numeric"
# Spread
var(scores)          # Variance
[1] 54.26786
sd(scores)           # Standard deviation
[1] 7.366672
range(scores)        # Min and max
[1] 74 96
IQR(scores)          # Interquartile range
[1] 8
# Quantiles
quantile(scores)
   0%   25%   50%   75%  100% 
74.00 83.25 88.50 91.25 96.00 
quantile(scores, probs = c(0.25, 0.5, 0.75))
  25%   50%   75% 
83.25 88.50 91.25

String Functions

# Text manipulation
text <- "Data Science"

nchar(text)          # Number of characters
[1] 12
toupper(text)        # Convert to uppercase
[1] "DATA SCIENCE"
tolower(text)        # Convert to lowercase
[1] "data science"
substr(text, 1, 4)   # Substring from position 1 to 4
[1] "Data"
# Combining strings
first_name <- "John"
last_name <- "Doe"
paste(first_name, last_name)              # With space
[1] "John Doe"
paste0(first_name, "_", last_name)        # No automatic space
[1] "John_Doe"

Creating Your Own Functions

Basic Function Syntax

The basic syntax for creating a function is:

function_name <- function(arguments) {
  # Function body
  # Operations using the arguments
  return(result)  # Optional - R returns the last expression
}

Simple Function Examples

# Function to calculate area of a rectangle
rectangle_area <- function(length, width) {
  area <- length * width
  return(area)
}

# Test the function
rectangle_area(5, 3)
[1] 15
rectangle_area(10, 7)
[1] 70
# Function to convert Fahrenheit to Celsius
fahrenheit_to_celsius <- function(fahrenheit) {
  celsius <- (fahrenheit - 32) * 5/9
  return(celsius)
}

# Test the conversion
fahrenheit_to_celsius(68)
[1] 20
fahrenheit_to_celsius(c(32, 68, 100))  # Works with vectors too!
[1]  0.00000 20.00000 37.77778
# Function to calculate compound interest
compound_interest <- function(principal, rate, time) {
  amount <- principal * (1 + rate)^time
  return(amount)
}

# Calculate investment growth
compound_interest(1000, 0.05, 10)  # $1000 at 5% for 10 years
[1] 1628.895

Functions with Default Arguments

You can provide default values for function arguments:

# Function with default values
greet_person <- function(name, greeting = "Hello", punctuation = "!") {
  message <- paste0(greeting, " ", name, punctuation)
  return(message)
}

# Use with different argument combinations
greet_person("Alice")                           # Uses defaults
[1] "Hello Alice!"
greet_person("Bob", "Hi")                       # Custom greeting
[1] "Hi Bob!"
greet_person("Charlie", "Hey", ".")             # All custom
[1] "Hey Charlie."
greet_person("Diana", punctuation = "!!!")      # Named argument
[1] "Hello Diana!!!"

Functions with Multiple Outputs

Functions can return multiple values using lists:

# Function to calculate basic statistics
describe_data <- function(data) {
  stats <- list(
    mean = mean(data, na.rm = TRUE),
    median = median(data, na.rm = TRUE),
    sd = sd(data, na.rm = TRUE),
    min = min(data, na.rm = TRUE),
    max = max(data, na.rm = TRUE),
    n = length(data[!is.na(data)])
  )
  return(stats)
}

# Test with sample data
test_scores <- c(85, 92, 78, 96, 88, NA, 91, 89)
results <- describe_data(test_scores)
print(results)
$mean
[1] 88.42857

$median
[1] 89

$sd
[1] 5.740416

$min
[1] 78

$max
[1] 96

$n
[1] 7
# Access individual results
results$mean
[1] 88.42857
results$sd
[1] 5.740416

Function Arguments and Parameters

Understanding Arguments

# Function demonstrating different argument types
analyze_grades <- function(scores,
                          curve_points = 0,     # Default argument
                          remove_lowest = FALSE, # Logical default
                          letter_grades = TRUE) {  # Another default

  # Apply curve if specified
  if (curve_points > 0) {
    scores <- scores + curve_points
    cat("Applied curve of", curve_points, "points\n")
  }

  # Remove lowest score if requested
  if (remove_lowest && length(scores) > 1) {
    scores <- scores[-which.min(scores)]
    cat("Removed lowest score\n")
  }

  # Calculate average
  avg_score <- mean(scores, na.rm = TRUE)

  # Return numeric or letter grade
  if (letter_grades) {
    letter_grade <- ifelse(avg_score >= 90, "A",
                          ifelse(avg_score >= 80, "B",
                                ifelse(avg_score >= 70, "C",
                                      ifelse(avg_score >= 60, "D", "F"))))
    return(list(average = avg_score, letter = letter_grade))
  } else {
    return(avg_score)
  }
}

# Test the function
student_scores <- c(78, 85, 92, 68, 88)

analyze_grades(student_scores)
$average
[1] 82.2

$letter
[1] "B"
analyze_grades(student_scores, curve_points = 5)
Applied curve of 5 points
$average
[1] 87.2

$letter
[1] "B"
analyze_grades(student_scores, remove_lowest = TRUE)
Removed lowest score
$average
[1] 85.75

$letter
[1] "B"
analyze_grades(student_scores, letter_grades = FALSE)
[1] 82.2

The ... (Ellipsis) Argument

The ... allows functions to accept a variable number of arguments:

# Function that summarizes multiple datasets
summarize_multiple <- function(..., digits = 2) {
  datasets <- list(...)

  for (i in seq_along(datasets)) {
    cat("Dataset", i, ":\n")
    cat("  Mean:", round(mean(datasets[[i]], na.rm = TRUE), digits), "\n")
    cat("  SD:", round(sd(datasets[[i]], na.rm = TRUE), digits), "\n")
    cat("  N:", length(datasets[[i]][!is.na(datasets[[i]])]), "\n\n")
  }
}

# Test with multiple datasets
group_a <- c(85, 90, 78, 92, 88)
group_b <- c(76, 84, 91, 79, 87)
group_c <- c(95, 89, 93, 87, 91)

summarize_multiple(group_a, group_b, group_c)
Dataset 1 :
  Mean: 86.6 
  SD: 5.46 
  N: 5 

Dataset 2 :
  Mean: 83.4 
  SD: 6.02 
  N: 5 

Dataset 3 :
  Mean: 91 
  SD: 3.16 
  N: 5

Function Scope and Environments

Local vs Global Variables

# Global variable
global_counter <- 0

# Function that uses local variables
demo_scope <- function(x) {
  # Local variable (only exists inside the function)
  local_counter <- 10

  # We can access global variables (but shouldn't modify them)
  result <- x + local_counter + global_counter

  cat("Inside function:\n")
  cat("  x =", x, "\n")
  cat("  local_counter =", local_counter, "\n")
  cat("  global_counter =", global_counter, "\n")

  return(result)
}

# Test the function
result <- demo_scope(5)
Inside function:
  x = 5 
  local_counter = 10 
  global_counter = 0 
cat("Result:", result, "\n")
Result: 15 
# The local variable doesn't exist outside the function
# print(local_counter)  # This would cause an error!

Modifying Global Variables

# Counter function using global assignment (generally not recommended)
click_counter <- 0

increment_counter <- function() {
  # Use <<- to modify global variable
  click_counter <<- click_counter + 1
  cat("Counter is now:", click_counter, "\n")
}

# Better approach: return new value
increment_counter_better <- function(current_count) {
  new_count <- current_count + 1
  return(new_count)
}

# Demonstrate both approaches
increment_counter()
Counter is now: 1 
increment_counter()
Counter is now: 2 
# Better approach
my_counter <- 0
my_counter <- increment_counter_better(my_counter)
my_counter <- increment_counter_better(my_counter)
cat("Better counter:", my_counter, "\n")
Better counter: 2

Advanced Function Concepts

Anonymous Functions

Functions don’t always need names:

# Apply anonymous function to vector
numbers <- c(1, 4, 9, 16, 25)

# Using lapply with anonymous function
squared_roots <- lapply(numbers, function(x) sqrt(x))
print(unlist(squared_roots))
[1] 1 2 3 4 5
# Using sapply for simpler output
doubled_values <- sapply(numbers, function(x) x * 2)
print(doubled_values)
[1]  2  8 18 32 50
# Real-world example: cleaning text data
messy_names <- c("  Alice  ", "BOB", "charlie", "  DIANA  ")
clean_names <- sapply(messy_names, function(name) {
  name <- trimws(name)  # Remove whitespace
  name <- tolower(name) # Convert to lowercase
  # Capitalize first letter
  substr(name, 1, 1) <- toupper(substr(name, 1, 1))
  return(name)
})
print(clean_names)
  Alice         BOB   charlie   DIANA   
  "Alice"     "Bob" "Charlie"   "Diana"

Functions that Return Functions

# Function factory: creates customized functions
create_multiplier <- function(factor) {
  function(x) {
    x * factor
  }
}

# Create specific multiplier functions
double <- create_multiplier(2)
triple <- create_multiplier(3)
percent <- create_multiplier(100)

# Use the created functions
double(5)
[1] 10
triple(7)
[1] 21
percent(0.85)  # Convert proportion to percentage
[1] 85
# More practical example: create converter functions
create_converter <- function(from_unit, to_unit, factor) {
  function(value) {
    result <- value * factor
    cat(value, from_unit, "=", result, to_unit, "\n")
    return(result)
  }
}

# Create specific converters
kg_to_pounds <- create_converter("kg", "pounds", 2.20462)
celsius_to_fahrenheit <- create_converter("°C", "°F", function(c) c * 9/5 + 32)

# Wait, that last one won't work as expected. Let's fix it:
celsius_to_fahrenheit <- function(celsius) {
  fahrenheit <- celsius * 9/5 + 32
  cat(celsius, "°C =", fahrenheit, "°F\n")
  return(fahrenheit)
}

kg_to_pounds(70)
70 kg = 154.3234 pounds 
[1] 154.3234
celsius_to_fahrenheit(20)
20 °C = 68 °F
[1] 68

Error Handling in Functions

Using stop() and warning()

# Function with input validation
safe_divide <- function(x, y) {
  # Check for valid inputs
  if (!is.numeric(x) || !is.numeric(y)) {
    stop("Both x and y must be numeric")
  }

  if (y == 0) {
    stop("Division by zero is not allowed")
  }

  if (y < 0) {
    warning("Dividing by negative number")
  }

  result <- x / y
  return(result)
}

# Test the function
safe_divide(10, 2)
[1] 5
safe_divide(10, -2)  # Will show warning
[1] -5
# safe_divide(10, 0)   # Would stop with error
# safe_divide("10", 2) # Would stop with error

Using try() and tryCatch()

# Function that handles errors gracefully
robust_mean <- function(data, method = "arithmetic") {
  result <- tryCatch({
    if (method == "arithmetic") {
      mean(data, na.rm = TRUE)
    } else if (method == "geometric") {
      if (any(data <= 0, na.rm = TRUE)) {
        stop("Geometric mean requires positive values")
      }
      exp(mean(log(data), na.rm = TRUE))
    } else {
      stop("Method must be 'arithmetic' or 'geometric'")
    }
  }, error = function(e) {
    cat("Error occurred:", e$message, "\n")
    return(NA)
  }, warning = function(w) {
    cat("Warning:", w$message, "\n")
    return(NA)
  })

  return(result)
}

# Test error handling
test_data <- c(2, 4, 8, 16)
negative_data <- c(-1, 2, 4)

robust_mean(test_data, "arithmetic")
[1] 7.5
robust_mean(test_data, "geometric")
[1] 5.656854
robust_mean(negative_data, "geometric")  # Will handle error
Error occurred: Geometric mean requires positive values 
[1] NA
robust_mean(test_data, "invalid")        # Will handle error
Error occurred: Method must be 'arithmetic' or 'geometric' 
[1] NA

Practical Function Examples

Example 1: Data Cleaning Function

# Comprehensive data cleaning function
clean_dataset <- function(data,
                         remove_duplicates = TRUE,
                         handle_missing = "remove",
                         standardize_names = TRUE) {

  original_rows <- nrow(data)
  cat("Starting with", original_rows, "rows\n")

  # Standardize column names
  if (standardize_names) {
    names(data) <- tolower(gsub("[^A-Za-z0-9_]", "_", names(data)))
    cat("Standardized column names\n")
  }

  # Handle missing values
  if (handle_missing == "remove") {
    data <- na.omit(data)
    cat("Removed rows with missing values:", original_rows - nrow(data), "\n")
  } else if (handle_missing == "fill_mean") {
    numeric_cols <- sapply(data, is.numeric)
    for (col in names(data)[numeric_cols]) {
      data[[col]][is.na(data[[col]])] <- mean(data[[col]], na.rm = TRUE)
    }
    cat("Filled missing numeric values with means\n")
  }

  # Remove duplicates
  if (remove_duplicates) {
    before_dedup <- nrow(data)
    data <- unique(data)
    duplicates_removed <- before_dedup - nrow(data)
    if (duplicates_removed > 0) {
      cat("Removed", duplicates_removed, "duplicate rows\n")
    }
  }

  cat("Final dataset:", nrow(data), "rows\n")
  return(data)
}

# Test the cleaning function
messy_data <- data.frame(
  "Student Name" = c("Alice", "Bob", "Charlie", "Alice", "Diana"),
  "Test Score" = c(85, NA, 92, 85, 88),
  "Grade Level" = c(10, 11, 10, 10, 12),
  stringsAsFactors = FALSE
)

cat("Original data:\n")
Original data:
print(messy_data)
  Student.Name Test.Score Grade.Level
1        Alice         85          10
2          Bob         NA          11
3      Charlie         92          10
4        Alice         85          10
5        Diana         88          12
clean_data <- clean_dataset(messy_data)
Starting with 5 rows
Standardized column names
Removed rows with missing values: 1 
Removed 1 duplicate rows
Final dataset: 3 rows
cat("\nCleaned data:\n")

Cleaned data:
print(clean_data)
  student_name test_score grade_level
1        Alice         85          10
3      Charlie         92          10
5        Diana         88          12

Example 2: Statistical Analysis Function

# Comprehensive statistical analysis function
analyze_groups <- function(data, group_var, measure_var, alpha = 0.05) {

  # Basic validation
  if (!group_var %in% names(data)) {
    stop("Group variable not found in data")
  }
  if (!measure_var %in% names(data)) {
    stop("Measure variable not found in data")
  }

  # Calculate group statistics
  groups <- unique(data[[group_var]])
  results <- list()

  cat("=== Group Analysis ===\n")
  for (group in groups) {
    group_data <- data[data[[group_var]] == group, measure_var]
    group_data <- group_data[!is.na(group_data)]

    stats <- list(
      group = group,
      n = length(group_data),
      mean = mean(group_data),
      sd = sd(group_data),
      median = median(group_data),
      min = min(group_data),
      max = max(group_data)
    )

    results[[as.character(group)]] <- stats

    cat(sprintf("Group %s: n=%d, mean=%.2f, sd=%.2f\n",
                group, stats$n, stats$mean, stats$sd))
  }

  # Overall statistics
  all_data <- data[[measure_var]][!is.na(data[[measure_var]])]
  overall_mean <- mean(all_data)
  overall_sd <- sd(all_data)

  cat(sprintf("\nOverall: n=%d, mean=%.2f, sd=%.2f\n",
              length(all_data), overall_mean, overall_sd))

  return(results)
}

# Test the analysis function
student_data <- data.frame(
  grade = c(rep("A", 5), rep("B", 4), rep("C", 6)),
  score = c(92, 95, 89, 94, 91, 85, 87, 83, 86, 78, 82, 75, 79, 77, 80)
)

analysis_results <- analyze_groups(student_data, "grade", "score")
=== Group Analysis ===
Group A: n=5, mean=92.20, sd=2.39
Group B: n=4, mean=85.25, sd=1.71
Group C: n=6, mean=78.50, sd=2.43

Overall: n=15, mean=84.87, sd=6.40

Example 3: Report Generation Function

# Function to generate formatted reports
generate_summary_report <- function(data, title = "Data Summary") {

  # Create border for title
  border <- paste(rep("=", nchar(title) + 4), collapse = "")
  cat(border, "\n")
  cat(" ", title, " \n")
  cat(border, "\n\n")

  # Dataset overview
  cat("DATASET OVERVIEW\n")
  cat("----------------\n")
  cat("Dimensions:", nrow(data), "rows ×", ncol(data), "columns\n")
  cat("Column names:", paste(names(data), collapse = ", "), "\n\n")

  # Summary for each column
  for (col in names(data)) {
    cat("COLUMN:", toupper(col), "\n")
    cat(strrep("-", nchar(col) + 8), "\n")

    if (is.numeric(data[[col]])) {
      cat("Type: Numeric\n")
      cat(sprintf("Range: %.2f to %.2f\n",
                  min(data[[col]], na.rm = TRUE),
                  max(data[[col]], na.rm = TRUE)))
      cat(sprintf("Mean: %.2f\n", mean(data[[col]], na.rm = TRUE)))
      cat(sprintf("Std Dev: %.2f\n", sd(data[[col]], na.rm = TRUE)))
    } else if (is.character(data[[col]]) || is.factor(data[[col]])) {
      cat("Type: Categorical\n")
      unique_vals <- unique(data[[col]])
      cat("Unique values:", length(unique_vals), "\n")
      if (length(unique_vals) <= 10) {
        cat("Values:", paste(unique_vals, collapse = ", "), "\n")
      }
    }

    # Missing values
    missing_count <- sum(is.na(data[[col]]))
    if (missing_count > 0) {
      cat("Missing values:", missing_count,
          sprintf("(%.1f%%)\n", missing_count / nrow(data) * 100))
    } else {
      cat("Missing values: None\n")
    }
    cat("\n")
  }

  cat("Report generated on:", format(Sys.time(), "%Y-%m-%d %H:%M:%S"), "\n")
}

# Test the report function
sample_data <- data.frame(
  id = 1:10,
  name = paste("Person", LETTERS[1:10]),
  age = c(25, 30, 35, 28, 32, 29, 31, 27, 33, 26),
  salary = c(45000, 52000, 48000, 55000, 51000, 49000, 53000, 46000, 54000, 47000),
  department = c("Sales", "IT", "Sales", "HR", "IT", "Sales", "HR", "IT", "Sales", "HR")
)

generate_summary_report(sample_data, "Employee Analysis Report")
============================ 
  Employee Analysis Report  
============================ 

DATASET OVERVIEW
----------------
Dimensions: 10 rows × 5 columns
Column names: id, name, age, salary, department 

COLUMN: ID 
---------- 
Type: Numeric
Range: 1.00 to 10.00
Mean: 5.50
Std Dev: 3.03
Missing values: None

COLUMN: NAME 
------------ 
Type: Categorical
Unique values: 10 
Values: Person A, Person B, Person C, Person D, Person E, Person F, Person G, Person H, Person I, Person J 
Missing values: None

COLUMN: AGE 
----------- 
Type: Numeric
Range: 25.00 to 35.00
Mean: 29.60
Std Dev: 3.20
Missing values: None

COLUMN: SALARY 
-------------- 
Type: Numeric
Range: 45000.00 to 55000.00
Mean: 50000.00
Std Dev: 3496.03
Missing values: None

COLUMN: DEPARTMENT 
------------------ 
Type: Categorical
Unique values: 3 
Values: Sales, IT, HR 
Missing values: None

Report generated on: 2025-09-22 01:02:34

Best Practices for Writing Functions

1. Function Design Principles

# GOOD: Clear, single purpose
calculate_bmi <- function(weight_kg, height_m) {
  bmi <- weight_kg / (height_m^2)
  return(round(bmi, 1))
}

# AVOID: Doing too many things
# bad_function <- function(weight, height, name, age, ...) {
#   # Calculate BMI, generate report, save to file, send email...
#   # Too many responsibilities!
# }

# GOOD: Descriptive names and clear documentation
convert_temperature <- function(temp, from = "celsius", to = "fahrenheit") {
  # Convert temperature between different scales
  # Args:
  #   temp: numeric temperature value
  #   from: source temperature scale ("celsius", "fahrenheit", "kelvin")
  #   to: target temperature scale ("celsius", "fahrenheit", "kelvin")
  # Returns:
  #   converted temperature value

  if (from == "celsius" && to == "fahrenheit") {
    return(temp * 9/5 + 32)
  } else if (from == "fahrenheit" && to == "celsius") {
    return((temp - 32) * 5/9)
  } else if (from == "celsius" && to == "kelvin") {
    return(temp + 273.15)
  } else if (from == "kelvin" && to == "celsius") {
    return(temp - 273.15)
  } else {
    stop("Conversion not implemented for these scales")
  }
}

# Test the function
convert_temperature(100, "celsius", "fahrenheit")
[1] 212
convert_temperature(32, "fahrenheit", "celsius")
[1] 0

2. Input Validation

# Function with comprehensive input validation
calculate_loan_payment <- function(principal, rate, years) {
  # Validate inputs
  if (!is.numeric(principal) || principal <= 0) {
    stop("Principal must be a positive number")
  }
  if (!is.numeric(rate) || rate < 0 || rate > 1) {
    stop("Interest rate must be between 0 and 1")
  }
  if (!is.numeric(years) || years <= 0) {
    stop("Number of years must be positive")
  }

  # Calculate monthly payment
  monthly_rate <- rate / 12
  num_payments <- years * 12

  if (rate == 0) {
    # Handle zero interest rate
    monthly_payment <- principal / num_payments
  } else {
    # Standard loan formula
    monthly_payment <- principal *
      (monthly_rate * (1 + monthly_rate)^num_payments) /
      ((1 + monthly_rate)^num_payments - 1)
  }

  return(round(monthly_payment, 2))
}

# Test with valid inputs
calculate_loan_payment(200000, 0.05, 30)
[1] 1073.64
calculate_loan_payment(50000, 0, 5)  # Zero interest
[1] 833.33
# These would produce errors:
# calculate_loan_payment(-1000, 0.05, 30)    # Negative principal
# calculate_loan_payment(200000, 1.5, 30)    # Rate > 1

3. Documentation and Comments

#' Calculate Investment Growth
#'
#' This function calculates the future value of an investment given
#' initial principal, annual interest rate, compounding frequency,
#' and time period.
#'
#' @param principal Initial investment amount (numeric)
#' @param rate Annual interest rate as decimal (e.g., 0.05 for 5%)
#' @param compound_freq Number of times interest compounds per year (integer)
#' @param years Number of years for investment (numeric)
#'
#' @return Future value of the investment (numeric)
#'
#' @examples
#' # $1000 at 5% compounded monthly for 10 years
#' investment_growth(1000, 0.05, 12, 10)
#'
#' # $5000 at 3% compounded quarterly for 5 years
#' investment_growth(5000, 0.03, 4, 5)
investment_growth <- function(principal, rate, compound_freq = 1, years) {

  # Input validation
  stopifnot(
    is.numeric(principal), principal > 0,
    is.numeric(rate), rate >= 0,
    is.numeric(compound_freq), compound_freq > 0,
    is.numeric(years), years > 0
  )

  # Calculate compound interest
  # Formula: A = P(1 + r/n)^(nt)
  future_value <- principal * (1 + rate/compound_freq)^(compound_freq * years)

  return(round(future_value, 2))
}

# Test the documented function
investment_growth(1000, 0.05, 12, 10)
[1] 1647.01
investment_growth(5000, 0.03, 4, 5)
[1] 5805.92

Exercises

Exercise 1: Basic Function Creation

Write functions to: 1. Calculate the area of a circle given its radius 2. Convert miles to kilometers (1 mile = 1.60934 km) 3. Determine if a number is even or odd 4. Find the largest of three numbers

Exercise 2: Data Analysis Function

Create a function called analyze_sales() that takes a vector of sales figures and returns: - Total sales - Average daily sales - Best sales day (highest amount) - Worst sales day (lowest amount) - Number of days above average

Exercise 3: Advanced Function

Write a function called grade_calculator() that: - Takes vectors of homework scores, quiz scores, and exam scores - Allows different weights for each category (default: 30% homework, 30% quizzes, 40% exams) - Returns both numerical and letter grades - Handles missing values appropriately - Provides a summary of the calculation

Exercise 4: Function with Error Handling

Create a robust function for calculating percentiles that: - Validates input data (numeric, not all NA) - Handles edge cases (empty vectors, single values) - Provides meaningful error messages - Has optional parameters for different percentile calculation methods

Summary

Functions are essential for writing efficient, maintainable R code:

Key Concepts:

  • Built-in functions: R provides extensive functionality out of the box
  • Custom functions: Create reusable code with the function() keyword
  • Arguments: Use default values and validation for robust functions
  • Scope: Understand local vs global variables
  • Return values: Functions can return single values, vectors, lists, or data frames

Best Practices:

  • Single responsibility: Each function should do one thing well
  • Clear naming: Use descriptive function and argument names
  • Input validation: Check arguments to prevent errors
  • Documentation: Comment your code and describe parameters
  • Error handling: Use stop(), warning(), and tryCatch() appropriately

Advanced Features:

  • Anonymous functions: Useful with apply family functions
  • Function factories: Functions that create other functions
  • Ellipsis (...): Handle variable numbers of arguments
  • Environments: Understand how R finds and stores variables

Functions make your code modular, testable, and reusable. As you progress in R, you’ll find that well-written functions are the foundation of all good R programs!

Next, we’ll learn about working with files and organizing your R projects!