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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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4 Hours17 Videos60 Exercises
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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.
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In the following Tracks

R Developer

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Text Mining with R

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  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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    Welcome!
    50 xp
    Quotes
    100 xp
    What you see isn't always what you have
    100 xp
    Escape sequences
    100 xp
    Turning numbers into strings
    50 xp
    Using format() with numbers
    100 xp
    Controlling other aspects of the string
    100 xp
    formatC()
    100 xp
    Putting strings together
    50 xp
    Annotation of numbers
    100 xp
    A very simple table
    100 xp
    Let's order pizza!
    100 xp
  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 Developer

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Text Mining with R

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Datasets

DNA sequences from the genome of Yersinia pestisNarrativesAdverbsImportance of being earnestCat-related accidents

Collaborators

Collaborator's avatar
Tom Jeon
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Richie Cotton

Prerequisites

Intermediate R
Charlotte Wickham HeadshotCharlotte 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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