Replacing with regular expressions
Now, you've mastered matching with backreferences, you'll build up to replacing with backreferences, but first let's review str_replace() now that you've got regular expressions under your belt.
Remember str_replace() takes three arguments, string a vector of strings to do the replacements in, pattern that identifies the parts of strings to replace and replacement the thing to use as a replacement.
replacement can be a vector, the same length as string, each element specifies the replacement to be used in each string. Let's practice by anonymizing some of the contact objects you've seen so far.
This is a part of the course
“String Manipulation with stringr in R”
Exercise instructions
Text containing phone numbers has been pre-defined in a variable named contact.
- Replace a digit in
contactwith"X"usingstr_replace(). - Replace all digits in
contactwith"X"usingstr_replace_all(). (str_replace()will only replace the first match to thepattern.str_replace_all()will replace all matches to the pattern.) - Replace all digits in
contactusingstr_replace_all(), but now specify the vectorc("X", ".", "*", "_")asreplacement. Notice how now each string uses a different replacement character.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# View text containing phone numbers
contact
# Replace digits with "X"
str_replace(contact, DGT, ___)
# Replace all digits with "X"
str_replace_all(contact, DGT, ___)
# Replace all digits with different symbol
str_replace_all(contact, DGT, ___)This exercise is part of the course
String Manipulation with stringr in R
Learn how to pull character strings apart, put them back together and use the stringr package.
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).
Exercise 1: CapturingExercise 2: Capturing parts of a patternExercise 3: Pulling out parts of a phone numberExercise 4: Extracting age and gender againExercise 5: BackreferencesExercise 6: Using backreferences in patternsExercise 7: Replacing with regular expressionsExercise 8: Replacing with backreferencesExercise 9: Unicode and pattern matchingExercise 10: Matching a specific code point or code groupsExercise 11: Matching a single graphemeWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.