Buat data.frame dengan list-column

Mari kita akhiri bab ini dengan mengimplementasikan ekstraktor tautan kita, tetapi menggunakan list-column. Gagasannya saat menggunakan dataframe bertingkat (yaitu dataframe dengan kolom bertipe list) adalah menjaga semuanya tetap di dalam satu dataframe agar alur kerja tetap rapi.

Anda telah disediakan sebuah tibble bernama df, yang memiliki kolom urls berisi empat URL yang Anda gunakan sejak awal bab ini. Jika ingin melihat dataframe ini, silakan cetak di konsol.

Kita akan membuat kolom baru bernama links, yang memuat hasil dari fungsi get_links() (tersedia di ruang kerja Anda). Karena keluaran fungsi ini memiliki panjang yang berbeda-beda, hasilnya akan berupa kolom list yang kemudian perlu Anda unnest() untuk kembali menjadi dataframe standar.

Latihan ini adalah bagian dari kursus

Pemrograman Fungsional Tingkat Menengah dengan purrr

Petunjuk latihan

Muat tiga paket yang diperlukan: dplyr, tidyr, dan purrr
Ambil elemen df, lalu jalankan mutate() pada objek tersebut. mutate() akan memetakan fungsi get_links() pada kolom urls.
Cetak hasilnya.
Unnest hasilnya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Load dplyr, tidyr, and purrr




# Create a "links" columns, by mapping get_links() on urls
df2 <- df %>%
  mutate(___ = map(___, get_links)) 

# Print df2 to see what it looks like


# unnest() df2 to have a tidy dataframe
df2 %>%
  ___(cols=c(links))

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: 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 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: Mengapa kode yang lebih bersih?Exercise 2: Cara menulis compose()Exercise 3: Kembali ke kantor Exercise 4: Membangun fungsi dengan compose() dan negate()Exercise 5: Bangun sebuah fungsi Exercise 6: Hitung NA Exercise 7: Mengisikan argumen awal pada fungsi Exercise 8: Ekstraktor konten Exercise 9: Ekstraktor lainnya Exercise 10: Kolom daftar Exercise 11: Tentang list-column Exercise 12: Buat data.frame dengan list-column

Latihan Saat Ini

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!