Intermediate Regular Expressions in R

Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.

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4 Hours14 Videos48 Exercises2,319 Learners
3650 XP

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Course Description

Analyzing data that comes in tables is fun. But what if the things that we find <em>most interesting</em> are not available as a neatly organized dataset but in plain text? Do not despair: In this course, you'll learn everything you need to know to <em>create powerful regular expressions</em> that will help you find all the information you need for your analyses from just a blob of text. But not only that. Using the concept of <em>string distances</em> you will learn to work even with text that contains typos or scanning errors, as you will be able to match them to their correct counterparts from other data sources (record linkage). As a learning material, we will analyze real documents about box office figures in Swiss cinemas.

  1. 1

    Regular Expressions: Writing custom patterns

    Free

    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.

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    Welcome
    50 xp
    Starts with, ends with
    100 xp
    If you don't know what you're looking for
    100 xp
    Character classes and repetitions
    50 xp
    Digits, words and spaces
    100 xp
    Match repetitions
    100 xp
    Which special character did what again?
    100 xp
    The pipe and the question mark
    50 xp
    This or that
    100 xp
    The question mark and its two meanings
    100 xp
    You can now read this!
    50 xp
  2. 3

    Extracting structured data from text

    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.

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  3. 4

    Similarities between strings

    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.

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Collaborators

Adel NehmeAmy Peterson

Prerequisites

Introduction to RIntroduction to the TidyverseString Manipulation with stringr in R
Angelo Zehr Headshot

Angelo Zehr

Data Journalist

Angelo Zehr is working as a data journalist at SRF, the Swiss Public Broadcaster. In his work, he is regularly confronted with large amounts of messy text that he needs to search and make sense of. In addition to his work, he teaches data journalism at the University of Applied Sciences in Chur and other courses at the Swiss School of Journalism.
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