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Data Cleaning with R

Understanding and Testing Regular Expressions

This lesson is called Understanding and Testing Regular Expressions, part of the Data Cleaning with R course. This lesson is called Understanding and Testing Regular Expressions, part of the Data Cleaning with R course.

Transcript

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Your Turn

Solution

## %######################################################%##
#                                                          #
####   Understanding a regular expression - your turn   ####
#                                                          #
## %######################################################%##

# Write regular expressions to match:
# Test with stringr, regexplain, or a web-based regex tester

# "cute, cuuute, cuuuuuute, and cuuuuuuuuute"
# strings ending in a period
# lowercase strings
# exactly four digits
# strings without the letter s


# load packages -----------------------------------------------------------
library(stringr)
library(regexplain)


# regular expressions -----------------------------------------------------

# "cute, cuuute, cuuuuuute, and cuuuuuuuuute"
str_view(
  string = c("cute", "cuuute", "cuuuuuute", "cuuuuuuuuute"),
  pattern = "cu+te"
)

view_regex(
  text = c("cute", "cuuute", "cuuuuuute", "cuuuuuuuuute"),
  pattern = "cu+te"
)

# strings ending in a period
str_view(
  string = c("toy", "duck", "Small house", "This is cold.", "1998"),
  pattern = ".+\\.$"
)

# lowercase strings
str_view(
  string = c("toy", "WALRUS", "Small house", "This is cold.", "1998"),
  pattern = "^[a-z]+$"
)

# exactly four digits
str_view(
  string = c("toy", "10990", "34", "Small house", "This is cold.", "1998"),
  pattern = "^[0-9]{4}$"
)

# strings without the letter s
str_view(
  string = c("toy", "10990", "34", "Small house", "This is cold.", "1998"),
  pattern = "^[^s]+$"
)

Navigate to rubular, regex101, regexr, or regexpal and familiarize yourself with the user interface.

Input the following text into the test string field:

2 cups whole milk 1 cup white rice (uncooked) 1 pinch salt 1/2 cup water 1/4 cup dark brown sugar

Write regular expressions (one at a time) to match the wet ingredients and look at the match results.

Learn More

In addition to the regex testing websites above, you might be interested in the regexplain package.

Another approach to doing regex in R is to use the str_view() function from the stringr package, which shows you matches in a string.

Have any questions? Put them below and we will help you out!

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Course Content

32 Lessons

What is Data Cleaning?

Course Logistics and Materials

Data Organization Best Practices

Grouping and Indicator Variables

NA and Empty Values

Data Sharing Best Practices

Tidyverse Refresher

Working with Columns with across()

coalesce() and fill()

What are Regular Expressions?

Understanding and Testing Regular Expressions

Literal Characters and Metacharacters

Metacharacters: Quantifiers

Metacharacters: Alternation, Special Sequences, and Escapes

Combining Metacharacters

Regular Expressions and Data Cleaning, Part 1

Regular Expressions and Data Cleaning, Part 2

Common Issues in Data Cleaning

Unusable Variable Names

Missing, Implicit, or Misplaced Grouping Variables

Compound Values

Duplicated Values

Empty Rows and Columns

Parsing Numbers

Putting Everything Together

Wrapping Up Data Cleaning with R