Skip to main content

Intermediate Functional Programming with purrr

Continue learning with purrr to create robust, clean, and easy to maintain iterative code.

Start Course for Free
4 Hours17 Videos49 Exercises4,892 Learners

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Loved by learners at thousands of companies

Course Description

Have you ever been wondering what the purrr description (“A functional programming toolkit for R”) refers to? Then, you’ve come to the right place! This course will walk you through the functional programming part of purrr - in other words, you will learn how to take full advantage of the flexibility offered by the .f in map(.x, .f) to iterate other lists, vectors and data.frame with a robust, clean, and easy to maintain code. During this course, you will learn how to write your own mappers (or lambda functions), and how to use predicates and adverbs. Finally, this new knowledge will be applied to a use case, so that you’ll be able to see how you can use this newly acquired knowledge on a concrete example of a simple nested list, how to extract, keep or discard elements, how to compose functions to manipulate and parse results from this list, how to integrate purrr workflow inside other functions, how to avoid copy and pasting with purrr functional tools.
  1. 1

    Programming with purrr

    Free

    Do lambda functions, mappers, and predicates sound scary to you? Fear no more! After refreshing your purrr memory, we will dive into functional programming 101, discover anonymous functions and predicates, and see how we can use them to clean and explore data.

    Play Chapter Now
    purrr basics - a refresher
    50 xp
    Refreshing your purrr memory
    100 xp
    Another purrr refresher
    100 xp
    Introduction to mappers
    50 xp
    Creating lambda functions
    100 xp
    Lambda functions
    100 xp
    Using mappers to clean up your data
    50 xp
    Clean up your data with keep
    100 xp
    Split up with keep() and discard()
    100 xp
    Predicates
    50 xp
    What is a predicate?
    50 xp
    Exploring data with predicates
    100 xp
  2. 2

    Functional programming: from theory to practice

    Ready to go deeper with functional programming and purrr? In this chapter, we'll discover the concept of functional programming, explore error handling using including safely() and possibly(), and introduce the function compact() for cleaning your code.

    Play Chapter Now
    Functional programming in R
    50 xp
    Everything that happens is a function call
    50 xp
    Identifying pure functions
    100 xp
    Tools for functional programming in purrr
    50 xp
    Safe iterations
    100 xp
    Create a function
    100 xp
    Using possibly()
    50 xp
    A possibly() version of read_lines()
    100 xp
    Everything in one call
    100 xp
    Handling adverb results
    50 xp
    Purrrfecting our function
    100 xp
    Extracting status codes with GET()
    100 xp
  3. 3

    Better code with purrr

    In this chapter, we'll use purrr to write code that is clearer, cleaner, and easier to maintain. We'll learn how to write clean functions with compose() and negate(). We'll also use partial() to compose functions by "prefilling" arguments from existing functions. Lastly, we'll introduce list-columns, which are a convenient data structure that helps us write clean code using the Tidyverse.

    Play Chapter Now
    Why cleaner code?
    50 xp
    How to write compose()
    50 xp
    Back to the office
    100 xp
    Building functions with compose() and negate()
    50 xp
    Build a function
    100 xp
    Count the NA
    100 xp
    Prefilling functions
    50 xp
    A content extractor
    100 xp
    Another extractor
    100 xp
    List columns
    50 xp
    About list-columns
    50 xp
    Create a list-column data.frame
    100 xp
  4. 4

    Case study

    We'll wrap up everything we know about purrr in a case study. Here, we'll use purrr to analyze data that has been scraped from Twitter. We'll use clean code to organize the data and then we'll identify Twitter influencers from the 2018 RStudio conference.

    Play Chapter Now
    Discovering the dataset
    50 xp
    Playing with tweets, round 1
    100 xp
    Identify profiles
    100 xp
    Extracting information from the dataset
    50 xp
    Counting favorites
    100 xp
    Extracting mentions
    100 xp
    Manipulating URLs
    50 xp
    Analyzing URLs
    100 xp
    Playing with URLs
    100 xp
    Identifying influencers
    50 xp
    Splitting the dataset
    100 xp
    We have a winner!
    100 xp
    Congratulations!
    50 xp

In the following tracks

Intermediate Tidyverse Toolbox

Collaborators

Chester Ismay
Becca Robins

Prerequisites

Foundations of Functional Programming with purrr
Colin FAY HeadshotColin FAY

Data Scientist & R Hacker

Colin is a Data Scientist & R Hacker at ThinkR. He's also a prolific Open Source developer, and an hyper-active Twitter user.

See More

What do other learners have to say?

    Join over 11 million learners and start Intermediate Functional Programming with purrr today!

    Create Your Free Account

    GoogleLinkedInFacebook

    or

    By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.