Interactive Course

Writing Functions in R

Learn the fundamentals of writing functions in R so you can make your code more readable and automate repetitive tasks.

4 hours
19 Videos
86 Exercises
66,128 Participants
7,250 XP

Create Your Free Account

By pressing Continue you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.

Loved by learners at thousands of top companies:

Course Description

Functions are a fundamental building block of the R language. You've probably used dozens (or even hundreds) of functions written by others, but in order to take your R game to the next level, you'll need to learn to write your own functions. This course will teach you the fundamentals of writing functions in R so that, among other things, you can make your code more readable, avoid coding errors, and automate repetitive tasks.

Before we embark, we'll make sure you’re ready for this course by reviewing some of the prerequisites. You'll review the syntax of writing a function in R, the basic data types in R, subsetting and writing for loops. It won't all be review, we'll introduce you to a few new things that will be helpful throughout the course.

You already know how to use a for loop. The goal of this chapter is to teach you how to use the map functions in the purrr package which remove the code that's duplicated across multiple for loops. After completing this chapter you'll be able to solve new iteration problems with greater ease (faster and with fewer bugs).

In this chapter we'll focus on writing functions that don't surprise you or your users. We'll expose you to some functions that work 95% of the time, and 5% of the time fail in surprising ways. You'll learn which functions you should avoid using inside a function and which you should use with care.

Writing your own functions is one way to reduce duplication in your code. In this chapter, you'll learn when to write a function, how to get started and what to keep in mind when you are writing. You'll also learn to appreciate that functions have two audiences: the computer (which runs the code) and humans (who need to be able to understand the code).

Now you've seen how useful the map functions are for reducing duplication, we'll introduce you to a few more functions in purrr that allow you to handle more complicated inputs and outputs. In particular, you'll learn how to deal with functions that might return an error, how to iterate over multiple arguments and how to iterate over functions that have no output at all.

1
A quick refresher
Free

Before we embark, we'll make sure you’re ready for this course by reviewing some of the prerequisites. You'll review the syntax of writing a function in R, the basic data types in R, subsetting and writing for loops. It won't all be review, we'll introduce you to a few new things that will be helpful throughout the course.
View Chapter Details Play Chapter Now
When and how you should write a function

Writing your own functions is one way to reduce duplication in your code. In this chapter, you'll learn when to write a function, how to get started and what to keep in mind when you are writing. You'll also learn to appreciate that functions have two audiences: the computer (which runs the code) and humans (who need to be able to understand the code).
View Chapter Details Play Chapter Now
Functional programming

You already know how to use a for loop. The goal of this chapter is to teach you how to use the map functions in the purrr package which remove the code that's duplicated across multiple for loops. After completing this chapter you'll be able to solve new iteration problems with greater ease (faster and with fewer bugs).
View Chapter Details Play Chapter Now
Advanced inputs and outputs

Now you've seen how useful the map functions are for reducing duplication, we'll introduce you to a few more functions in purrr that allow you to handle more complicated inputs and outputs. In particular, you'll learn how to deal with functions that might return an error, how to iterate over multiple arguments and how to iterate over functions that have no output at all.
View Chapter Details Play Chapter Now
Robust functions

In this chapter we'll focus on writing functions that don't surprise you or your users. We'll expose you to some functions that work 95% of the time, and 5% of the time fail in surprising ways. You'll learn which functions you should avoid using inside a function and which you should use with care.
View Chapter Details Play Chapter Now

What do other learners have to say?

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA

Team RStudio

Instructor

Collaborators

Nick Carchedi
Tom Jeon

Prerequisites

Datasets

Swimming Pools

Popular Courses

Popular Searches

If you type... We will search for...

Press enter to see all search results

Courses

Tracks

Instructors

Writing Functions in R

Create Your Free Account

Loved by learners at thousands of top companies:

Course Description

A quick refresher

Writing a function in R

Writing a function

Arguments

Function output

Environments

Testing your understanding of scoping (1)

Testing your understanding of scoping (2)

Testing your understanding of scoping (3)

Data structures

Atomic types of vectors

Subsetting lists

Exploring lists

for loops

A safer way to create the sequence

Keeping output

Functional programming

Why functional programming?

Using a for loop to remove duplication

Turning the for loop into a function

What about column means?

What about column standard deviations?

Uh oh...time to write a function again

Functions can be arguments too

Using a function as an argument

Introducing purrr

The map functions

The ... argument to the map functions

Picking the right map function

Shortcuts

Solve a simple problem first

Using an anonymous function

Using a formula

Using a string

Using a numeric vector

Putting it together with pipes

Robust functions

Robust functions

An error is better than a surprise

An informative error is even better

A different kind of surprise: side effects

Unstable types

sapply is another common culprit

Using purrr solves the problem

A type consistent solution

Or fail early if something goes wrong

Non-standard evaluation

Programming with NSE functions

When things go wrong

What to do?

Hidden arguments

A hidden dependence

Legitimate use of options

Wrap-up

When and how you should write a function

Why should you write a function?

Start with a snippet of code

Rewrite for clarity

Finally turn it into a function!

How should you write a function?

Start with a simple problem

Rewrite snippet as function

Put our function to use

How can you write a good function?

Good function names

Argument names

Argument order

Return statements

What does this function do?