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Course

String Manipulation with stringr in R

IntermediateSkill Level

4.7+

Updated 11/2022

Learn how to pull character strings apart, put them back together and use the stringr package.

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RProgramming4 hr17 videos60 Exercises5,150 XP32,893Statement of Accomplishment

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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.

Prerequisites

1

String basics

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.

What you see isn't always what you have

Escape sequences

Turning numbers into strings

Using format() with numbers

Controlling other aspects of the string

Putting strings together

Annotation of numbers

A very simple table

Let's order pizza!

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.

Introducing stringr

Putting strings together with stringr

String length

Extracting substrings

Hunting for matches

Detecting matches

Subsetting strings based on match

Counting matches

Splitting strings

Parsing strings into variables

Some simple text statistics

Replacing matches in strings

Replacing to tidy strings

Final challenges

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.

Regular expressions

Matching the start or end of the string

Matching any character

Combining with stringr functions

More regular expressions

Alternation

Character classes

Hunting for phone numbers

Extracting age and gender from accident narratives

Parsing age and gender into pieces

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).

Capturing parts of a pattern

Pulling out parts of a phone number

Extracting age and gender again

Backreferences

Using backreferences in patterns

Replacing with regular expressions

Replacing with backreferences

Unicode and pattern matching

Matching a specific code point or code groups

Matching a single grapheme

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).

Getting the play into R

Identifying the lines, take 1

Identifying the lines, take 2

A case study on case

Changing case to ease matching

Ignoring case when matching

Fixing case problems

Wrapping up

An interview with Hadley Wickham

String Manipulation with stringr in R

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FAQs

Is this course suitable for someone new to working with text data in R?

Yes. It is a beginner-level course that starts with string basics and progressively introduces pattern matching, so no prior text processing experience is needed.

What is the stringr package and why learn it over base R string functions?

The stringr package provides a consistent, readable set of functions for string manipulation in R. It simplifies tasks like detecting, extracting, and replacing text compared to base R.

Will I learn regular expressions in this course?

Yes. Two full chapters are dedicated to regular expressions, covering how to build patterns to detect, extract, match, and split strings effectively.

What practical tasks will I be able to do after completing this course?

You will be able to clean messy string inputs, extract data embedded in text, format numeric output as sentences, and match complex patterns using regular expressions.

How is the course structured across its five chapters?

It moves from string basics and formatting, through core stringr functions, into regular expressions for pattern matching, and finishes with advanced string manipulation techniques.

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