String Manipulation with stringr in R

Learn how to pull character strings apart, put them back together and use the stringr package.
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Clock4 HoursPlay17 VideosCode60 ExercisesGroup20,371 Learners
Database5150 XP

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Course Description

Character strings can turn up in all stages of a data science project. You might have to clean messy string input before analysis, extract data that is embedded in text or automatically turn numeric results into a sentence to include in a report. Perhaps the strings themselves are the data of interest, and you need to detect and match patterns within them. This course will help you master these tasks by teaching you how to pull strings apart, put them back together and use stringr to detect, extract, match and split strings using regular expressions, a powerful way to express patterns.

  1. 1

    String basics

    Free
    You'll start with some basics: how to enter strings in R, how to control how numbers are transformed to strings, and finally how to combine strings together to produce output that combines text and nicely formatted numbers.
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  2. 2

    Introduction to stringr

    Time to meet stringr! You'll start by learning about some stringr functions that are very similar to some base R functions, then how to detect specific patterns in strings, how to split strings apart and how to find and replace parts of strings.
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  3. 3

    Pattern matching with regular expressions

    In this chapter you'll learn about regular expressions, a language for describing patterns in strings. By combining regular expressions with the stringr functions you'll greatly increase your power to manipulate strings.
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  4. 4

    More advanced matching and manipulation

    Now for two advanced ways to use regular expressions along with stringr: selecting parts of a match (a.k.a capturing) and referring back to parts of a match (a.k.a back-referencing). You'll also learn to deal with and strings or patterns that contain Unicode characters (e.g. é).
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  5. 5

    Case studies

    Practice your string manipulation skills on a couple of case studies. You'll also learn a few new skills, reading strings into R and handling problems of case (e.g. A versus a).
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In the following tracks
R ProgrammerText Mining
Collaborators
Tom JeonRichie Cotton
Prerequisites
Intermediate R
Charlotte Wickham Headshot

Charlotte Wickham

Assistant Professor at Oregon State University
Charlotte is an Assistant Professor in the Department of Statistics at Oregon State University and an avid R programmer with a passion for teaching. Her interests lie in spatiotemporal data, statistical graphics and computing, and environmental statistics.
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Decision Science Analytics, USAA

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