Lab 10 : Detecting Patterns with Regular Expressions

Learning objectives

  • How to use regular expressions in R

Load libaries

R code 
library(tidyverse)

What Are Regular Expressions?

Regular expressions are sequences of characters that define search patterns. In R, they are commonly used for:

  • Searching text
  • Extracting substrings
  • Replacing patterns
  • Validating formats (e.g., emails, dates)

Key Functions in R for Regex

R provides several base functions that support regular expressions:

1. grep() / grepl()

  • grep() returns indices of matches.
  • grepl() returns a logical vector indicating matches.
R code 
grepl("cat", c("cat", "dog", "catalog"))
[1]  TRUE FALSE  TRUE

2. sub() / gsub()

  • sub() replaces the first match.
  • gsub() replaces all matches.
R code 
gsub("dog", "cat", "dog and dog")
[1] "cat and cat"

3. regexpr() / gregexpr()

  • Return the position of the first/all matches and their lengths.

4. regmatches()

  • Extracts matched substrings based on regexpr() or gregexpr().

Regex Syntax Basics

Here are some commonly used regex symbols:

Symbol Meaning Example
. Any character except newline "a.b" matches “acb”, “a1b”
^ Start of string "^cat" matches “catfish”
$ End of string "cat$" matches “bobcat”
* 0 or more repetitions "ca*t" matches “ct”, “cat”, “caaaat”
+ 1 or more repetitions "ca+t" matches “cat”, “caaaat”
? 0 or 1 repetition "ca?t" matches “ct”, “cat”
[] Character class "[cd]og" matches “dog”, “cog”
| OR "cat|dog" matches either
() Grouping "(cat|dog)s?" matches “cat”, “cats”, “dog”, “dogs”
\\ Escape special characters "\\." matches a literal dot

Example Use Case

R code 
text <- c("apple", "banana", "cherry", "date")
grep("a", text, value = TRUE)  # Matches strings containing 'a'
[1] "apple"  "banana" "date"
R code 
grep("^a", text, value = TRUE)  # Matches strings starting with 'a'
[1] "apple"
R code 
grep("a$", text, value = TRUE)  # Matches strings ending with 'a'
[1] "banana"

Introduction to stringr

The base R regular expressions are great to use if you already use regular expressions in another programming language. Starting out the stringr package, part of the tidyverse, provides a cohesive and consistent set of functions for string manipulation. It simplifies working with regular expressions by offering:

  • Consistent function names (str_*)
  • Predictable argument order (string first, pattern second)
  • Built-in support for vectorized operations
  • Better integration with tidyverse workflows

Why Use stringr rather than base R regex functions?

  • Cleaner syntax: Easier to read and write than base R regex functions.
  • Tidyverse-friendly: Works well with dplyr, purrr, and other packages.
  • Consistent behavior: Avoids quirks of base R functions like grep() and sub().

Common stringr Functions for Regex

Here are some of the most useful functions when working with regular expressions:

1. str_detect()

Checks if a pattern exists in a string.

R code 
str_detect(c("apple", "banana", "cherry"), "^a")
[1]  TRUE FALSE FALSE

2. str_replace() / str_replace_all()

Replaces the first or all occurrences of a pattern.

R code 
str_replace("cat and dog", "dog", "mouse")
[1] "cat and mouse"
R code 
str_replace_all("cat and dog and dog", "dog", "mouse")
[1] "cat and mouse and mouse"

3. str_extract() / str_extract_all()

Extracts the first or all matches of a pattern.

R code 
str_extract("My email is test@example.com", "[a-z0-9._%+-]+@[a-z0-9.-]+\\.[a-z]{2,}")
[1] "test@example.com"

4. str_match() / str_match_all()

Extracts matched groups using parentheses.

R code 
str_match("Name: John", "Name: (\\w+)")
     [,1]         [,2]  
[1,] "Name: John" "John"

5. str_split()

Splits strings based on a pattern.

R code 
str_split("apple,banana,cherry", ",")
[[1]]
[1] "apple"  "banana" "cherry"

Regex Integration

All stringr functions accept regular expressions by default. You can use:

  • ^, $, ., *, +, ?, [], (), | — standard regex symbols
  • Escaped characters like \\d, \\s, \\w for digits, whitespace, and word characters

stringi

stringr is built on top of the stringi package. stringr is useful when you’re learning because it exposes a minimal set of functions, which have been carefully picked to handle the most common string manipulation functions. stringi, on the other hand, is designed to be comprehensive. It contains almost every function you might ever need: stringi has 250 functions to stringr’s 49.

Exercises

R for Data Science Chapter 15.

Today we will walk through Chapter 15 Regular expressions in R for Data Science. As we did last week, by putting the examples and exercises in our own Quarto Markdown file, we can create own personal path through the Chapter.

What to upload to Canvas

After you Render the qmd file to an html file, export the file to your computer and upload it to Canvas.