Mengeksplorasi data dengan predikat

Kita akan melanjutkan eksplorasi data uji A/B. Manajer Anda tidak tertarik pada hari mana yang mencapai ambang batas; ia ingin tahu apakah setiap hari mencapai ambang batas atau apakah sebagian hari mencapainya. Kita akan menggunakan predikat purrr untuk menjawab pertanyaan ini.

Anda menerima beberapa ambang batas dan memutuskan untuk menulis skrip yang dimulai dengan definisi ambang batas tersebut, lalu menjawab, untuk setiap desain, apakah semua hari telah mencapai ambang batas, dan jika tidak, apakah sebagian hari mencapainya.

Hasil dari uji A/B ini ada dalam daftar all_visits.

Latihan ini adalah bagian dari kursus

Pemrograman Fungsional Tingkat Menengah dengan purrr

Petunjuk latihan

Buat variabel bernama threshold yang berisi angka 160.
Buat mapper baru yang akan menguji apakah .x melebihi threshold.
Gabungkan map() dan every() untuk menguji apakah semua elemen melebihi ambang batas.
Gabungkan map() dan some() untuk menguji apakah sebagian elemen melebihi ambang batas.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create a threshold variable, set it to 160


# Create a mapper that tests if .x is over the defined threshold
over_threshold <- ___(~ .x > ___)

# Are all elements in every all_visits vectors over the defined threshold? 
map(all_visits, ~ ___(.x, ___))

# Are some elements in every all_visits vectors over the defined threshold? 
map(all_visits, ~ ___(.x, ___))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pemrograman Fungsional Tingkat Menengah dengan purrr

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: Dasar-dasar purrr — kilas balik Exercise 2: Menyegarkan ingatan Anda tentang purrr Exercise 3: Penyegaran purrr lainnya Exercise 4: Pengantar mapper Exercise 5: Membuat fungsi lambda Exercise 6: Fungsi lambda Exercise 7: Menggunakan mapper untuk merapikan data Anda Exercise 8: Rapikan data Anda dengan keep Exercise 9: Pisahkan dengan keep() dan discard()Exercise 10: Predikat Exercise 11: Apa itu predikat?Exercise 12: Mengeksplorasi data dengan predikat

Latihan Saat Ini

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!