What are functions in R ?

In R, a function is a set of statements organized together to perform a specific task. R has a number of built-in functions, and you can also create your own functions, which are known as user-defined functions.

Basic structure of a function in R

function_name <- function(arg1, arg2, ...){   # Function body   # Computation goes here   return(value)
}

Built-in Functions in R

R provides a plethora of built-in functions for various tasks. For instance:

• Arithmetic functions: sum(), mean(), etc.
  
• Statistical functions: lm(), t.test(), etc.
• Graphics-related: plot(), hist(), etc.

Example:

# Using the built-in mean function
values <- c(2, 4, 6, 8, 10)
average <- mean(values)
print(average) # Outputs: 6

User-defined Functions

You can create your own functions in R for tasks that you perform frequently or to encapsulate complex logic.

Example:

Let's create a function that calculates the square of a number:

# Defining the function
square <- function(x) {  return(x^2)
}

# Using the function
result <- square(5)
print(result) # Outputs: 25

Another example, a function to calculate the factorial of a number:

factorial_calc <- function(n) {  if(n <= 1) {    return(1)  } else {    return(n * factorial_calc(n-1))  }
}

print(factorial_calc(5)) # Outputs: 120

Functions with Default Arguments

You can define default values for function arguments. If the function is called without the argument value, it will use the default value.

Example:

# A function to raise a number to a power with a default power of 2
power_function <- function(base, exponent = 2) {  return(base^exponent)
}

print(power_function(3))     # Outputs: 9, since it uses the default exponent of 2
print(power_function(3, 3))  # Outputs: 27

In practice, R's capability to handle and create functions is a crucial aspect of the language, especially for data analysis workflows. Functions help to modularize and reuse code, making the overall analysis more organized and manageable.