Wat is een predicaat?

Een predicate function is "een functie die TRUE of FALSE teruggeeft." Een predicate functional "neemt een vector en een predicate function en doet daar iets nuttigs mee."^***

Met andere woorden: predicate functionals nemen .x aan, een vector, een dataframe of een list, en testen het predicaat op elk element van .x. Je kunt bijvoorbeeld testen of elk element numeriek is met het predicaat is.numeric() uit base R, of of het gemiddelde van sommige elementen onder de 5 ligt met deze mapper: ~mean(.x) < 5.

Welke van deze functies is GEEN predicaat?

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Gevorderd functioneel programmeren met purrr

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Gevorderd functioneel programmeren met purrr

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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 - een opfrisser Exercise 2: Je purrr-kennis opfrissen Exercise 3: Nog een purrr-opfrisser Exercise 4: Introductie tot mappers Exercise 5: Lambda-functies maken Exercise 6: Lambdafuncties Exercise 7: Mappers gebruiken om je data op te schonen Exercise 8: Ruim je data op met keep Exercise 9: Splitsen met keep() en discard()Exercise 10: Predicaten Exercise 11: Wat is een predicaat?

Huidige oefening

Exercise 12: Data verkennen met predicaten

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 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!