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. 

purrr basics - a refresher

Refreshing your purrr memory

Another purrr refresher

Introduction to mappers

Creating lambda functions

Lambda functions

Using mappers to clean up your data

Clean up your data with keep

Split up with keep() and discard()

Predicates

What is a predicate?

Exploring data with predicates

Programming with purrr

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.

Functional programming in R

Everything that happens is a function call

Identifying pure functions

Tools for functional programming in purrr

Safe iterations

Create a function

Using possibly()

A possibly() version of read_lines()

Everything in one call

Handling adverb results

Purrrfecting our function

Extracting status codes with GET()

Functional programming: from theory to practice

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.

Why cleaner code?

How to write compose()

Back to the office

Building functions with compose() and negate()

Build a function

Count the NA

Prefilling functions

A content extractor

Another extractor

List columns

About list-columns

Create a list-column data.frame

Better code with purrr

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.

Discovering the dataset

Playing with tweets, round 1

Identify profiles

Extracting information from the dataset

Counting favorites

Extracting mentions

Manipulating URLs

Analyzing URLs

Playing with URLs

Identifying influencers

Splitting the dataset

We have a winner!

Congratulations!

Case study

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.

Foundations of Functional Programming with purrr

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

Intermediate Functional Programming with purrr

Caixa de ferramentas intermediária do Tidyverse

Data Scientist & R Hacker at ThinkR

Building functions with `compose()` and `negate()`

compose() at will

negate()

With mappers

Digression on http codes

Test if in

Let's practice!

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Better code with purrr - Building functions with compose() and negate()

Building functions with compose() and negate()

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