Identifying pure functions

A pure function satisfies two properties:

Its output only depends on its inputs: when you input a value, the output is always the same.
It has no side-effect, that is to say, no effect outside the function.

A lot of functions in R are not pure, yet they are vital for a day to day use of R: when doing an analysis, you need to download files, create a plot, save results…

When programming, you should aim at making your functions either as pure as possible or as impure as possible (for example, a function that downloads a file should only download this file). But for that, you first need to be able to recognize a pure function from an impure one.

This is what we'll do in this exercise: run functions which are either pure or impure, and see what their outputs are.

This exercise is part of the course

Intermediate Functional Programming with purrr

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Launch Sys.time(), Sys.sleep(1), & Sys.time()
___()
___(___)
___()

Edit and Run Code

This exercise is part of the course

Intermediate Functional Programming with purrr

IntermediateSkill Level

4.9+

Start Course for 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.

Exercise 1: purrr basics - a refresher Exercise 2: Refreshing your purrr memory Exercise 3: Another purrr refresher Exercise 4: Introduction to mappers Exercise 5: Creating lambda functions Exercise 6: Lambda functions Exercise 7: Using mappers to clean up your data Exercise 8: Clean up your data with keep Exercise 9: Split up with keep() and discard()Exercise 10: Predicates Exercise 11: What is a predicate?Exercise 12: Exploring data with predicates

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.

Exercise 1: Functional programming in R Exercise 2: Everything that happens is a function call Exercise 3: Identifying pure functions

Current Exercise

Exercise 4: Tools for functional programming in purrr Exercise 5: Safe iterations Exercise 6: Create a function Exercise 7: Using possibly()Exercise 8: A possibly() version of read_lines()Exercise 9: Everything in one call Exercise 10: Handling adverb results Exercise 11: Purrrfecting our function Exercise 12: Extracting status codes with GET()

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.

Exercise 1: Why cleaner code?Exercise 2: How to write compose()Exercise 3: Back to the office Exercise 4: Building functions with compose() and negate()Exercise 5: Build a function Exercise 6: Count the NA Exercise 7: Prefilling functions Exercise 8: A content extractor Exercise 9: Another extractor Exercise 10: List columns Exercise 11: About list-columns Exercise 12: Create a list-column data.frame

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.

Exercise 1: Discovering the dataset Exercise 2: Playing with tweets, round 1 Exercise 3: Identify profiles Exercise 4: Extracting information from the dataset Exercise 5: Counting favorites Exercise 6: Extracting mentions Exercise 7: Manipulating URLs Exercise 8: Analyzing URLs Exercise 9: Playing with URLs Exercise 10: Identifying influencers Exercise 11: Splitting the dataset Exercise 12: We have a winner!Exercise 13: Congratulations!