Kembali ke kantor

Anda masih bekerja sebagai analis data di sebuah agensi web, dan Anda diminta melakukan web scraping. Anda menerima sebuah daftar URL untuk dianalisis, analisis yang sudah Anda mulai pada bab sebelumnya.

Anda memperkirakan tugas ini akan berulang: besar kemungkinan Anda akan diminta melakukannya lagi dalam beberapa minggu. Untuk memudahkan pekerjaan Anda di masa depan, Anda memutuskan menulis kode yang rapi hari ini, agar lebih mudah ditinjau kembali nanti.

Kita akan mulai dengan menggabungkan dua fungsi dari httr yang telah kita lihat pada bab sebelumnya: GET(), untuk mengambil halaman web, dan status_code(), untuk mengekstrak status code, guna membuat sebuah ekstraktor status code.

Vektor urls masih tersedia di ruang kerja Anda. Kami hanya menyimpan URL yang dapat diakses.

Latihan ini adalah bagian dari kursus

Pemrograman Fungsional Tingkat Menengah dengan purrr

Petunjuk latihan

Muat purrr dan httr.
Susun sebuah ekstraktor status dengan GET() dan status_code().
Coba fungsi baru ini pada "https://www.thinkr.fr" dan "https://en.wikipedia.org".
Petakan fungsi ini langsung pada vektor urls.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Launch purrr and httr



# Compose a status extractor 
status_extract <- ___(___, ___)

# Try with "https://thinkr.fr" & "https://en.wikipedia.org"
___("https://thinkr.fr")
___("https://en.wikipedia.org")

# Map it on the urls vector, return a vector of numbers
___(urls, ___)

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

Latihan Saat Ini

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

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!