Regular expressions can be pretty intimidating at first as they contain vast amounts of special characters. In this chapter, you'll learn to decipher these and write your own patterns to find exactly what you're looking for.

Welcome

Starts with, ends with

If you don't know what you're looking for

Character classes and repetitions

Digits, words and spaces

Match repetitions

Which special character did what again?

The pipe and the question mark

This or that

The question mark and its two meanings

You can now read this!

Regular Expressions: Writing Custom Patterns

In this chapter, we will slightly move away from regular expressions and focus on string manipulation by creating strings from other data structures like vectors or lists.

Getting to know glue

Stop pasting, start gluing

Gluing data frames

How many arguments can glue take?

Collapsing multiple elements into a string

Formulating a question from a list

Collapsing data frames

Glue and Collapse, what's the difference?

Gluing regular expressions

Construct "or patterns" with glue

Using the "or pattern" with a larger dataset

Make advanced patterns more readable

Creating Strings with Data

One task where regular expressions really shine is making sense from a blob of text. In this chapter, you'll learn to extract the information from messy data that doesn't come in neatly arranged tables but in plain text.

Kelompok Penangkap

Cocokkan semua capturing group

Cari dan ganti

Dapatkah Anda membuat capturing group bersarang?

extract di tidyr

Membuat regex yang sesuai dengan kebutuhan Anda

Mengapa ini gagal?

Mengekstrak sebuah regular expression tingkat lanjut

Mengekstrak kecocokan dan konteks di sekitar dari sebuah teks

Ekstrak nama dengan konteks

Opsi

Capturing Groups

Pola kustom

Pengulangan

Begitu banyak karakter khusus

Extracting Structured Data From Text

In the last chapter, we will shift gears away from regular expressions to understanding string distances. By calculating the differences of multiple strings, we can match those that are similar. This will help us to find duplicates even when they contain small errors like typos. This is an important part to record linkage where we combine datasets from multiple sources.

Understanding string distances

Calculating a string distance

Finding a match to a search typo

Methods of string distances

Edit distances vs. q-gram methods

Trying out different methods

Is one distance better than the other?

Fuzzy joins

Performing a string distance join

String distances of short strings

Custom Fuzzy Matching

Finding matches based on two conditions

Why join on multiple columns?

Congratulations

Similarities Between Strings

Menganalisis data dalam bentuk tabel memang menyenangkan. Namun bagaimana jika hal yang paling menarik justru tidak tersedia sebagai himpunan data yang tertata rapi, melainkan dalam teks biasa? Jangan khawatir: Di kursus ini, Anda akan mempelajari semua yang perlu diketahui untuk membuat ekspresi reguler yang kuat agar Anda dapat menemukan semua informasi yang dibutuhkan untuk analisis hanya dari sepotong teks. Tidak hanya itu. Dengan konsep jarak string, Anda akan belajar bekerja bahkan dengan teks yang mengandung salah ketik atau kesalahan pemindaian, karena Anda dapat mencocokkannya dengan padanan yang benar dari sumber data lain (record linkage). Sebagai bahan pembelajaran, kita akan menganalisis dokumen nyata tentang angka box office di bioskop Swiss.

Introduction to the Tidyverse

String Manipulation with stringr in R

Pelajari manipulasi data teks, analisis kesalahan, dan buat ekspresi reguler serta jarak string di R.

Ekspresi Reguler Tingkat Menengah di R

Manipulasi data teks, analisis, dan lebih banyak lagi dengan menguasai ekspresi reguler dan jarak string di R.

Mengapa ini gagal?

Ekspresi Reguler Tingkat Menengah di R

Latihan interaktif praktis