Functions in R

Functions in R: A Complete Guide

Why Functions in R Are Important

Functions are fundamental building blocks in R programming that allow you to automate repetitive tasks, organize your code efficiently, and perform complex data analysis operations. In the Google Data Analytics context, mastering functions enables you to clean data faster, perform calculations consistently, and create reusable code that saves time on future projects. Understanding functions is essential for any data analyst working with R.

What Are Functions in R?

A function in R is a block of organized, reusable code that performs a specific task. Functions take inputs (called arguments or parameters), process them, and return an output. R has two types of functions:

1. Built-in Functions: Pre-defined functions that come with R, such as mean(), sum(), print(), and str()

2. User-defined Functions: Custom functions you create to perform specific tasks unique to your analysis needs

How Functions Work in R

The basic syntax for creating a function is:

function_name <- function(argument1, argument2) {
  code to execute
  return(value)
}

Key Components:
- Function name: The identifier you use to call the function
- Arguments: Input values the function needs to work
- Function body: The code inside curly braces that executes
- Return statement: Specifies what the function outputs

Common Built-in Functions:
- mean(x) - calculates average
- median(x) - finds middle value
- sd(x) - standard deviation
- length(x) - counts elements
- summary(x) - provides statistical summary
- head(x) - shows first six rows
- tail(x) - shows last six rows

Example of a User-defined Function:

calculate_tip <- function(bill_amount, tip_percent) {
  tip <- bill_amount * (tip_percent / 100)
  return(tip)
}

Exam Tips: Answering Questions on Functions in R

1. Know the syntax: Memorize the basic structure - function(), arguments in parentheses, code in curly braces

2. Understand argument order: When calling functions, arguments can be passed by position or by name. Using names like mean(x = data) prevents errors

3. Default arguments: Some functions have default values. Know that you can override these by specifying your own values

4. Recognize common functions: Be familiar with frequently used functions like filter(), select(), mutate(), and summarize() from the tidyverse

5. Return values: Remember that R functions return the last evaluated expression, or you can use return() explicitly

6. Read questions carefully: Pay attention to whether the question asks about creating a function or using an existing one

7. Practice nested functions: Understand how functions can be used inside other functions, like round(mean(x), 2)

8. Check for parentheses: Every opening parenthesis needs a closing one - a common source of errors

9. Remember case sensitivity: R is case-sensitive, so Mean() is not the same as mean()

10. Understand scope: Variables created inside a function only exist within that function unless returned