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Intermediate Functional Programming with purrr

中级技能水平
更新时间 2024年11月
Continue learning with purrr to create robust, clean, and easy to maintain iterative code.
免费开始课程
RProgramming
4小时
17 视频
49 道练习
3,850 XP
5,659
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课程描述

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.

先决条件

Foundations of Functional Programming with purrr
1

Programming with purrr

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.
开始章节
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.
开始章节
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.
开始章节
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.
开始章节
Intermediate Functional Programming with purrr
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