How to Use str_remove in R (With Examples)

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The str_remove and str_remove_all functions in the stringr package in R offer a simple yet versatile approach to remove substrings from character strings, which is crucial for cleaning and preprocessing textual data. This article provides a detailed guide on how to use str_remove and str_remove_all in R with several examples to illustrate different use cases and applications.

Introduction to str_remove and str_remove_all Functions

  • str_remove: Removes the first occurrence of a pattern in a string.
  • str_remove_all: Removes all occurrences of a pattern in a string.

Basic Syntax:

str_remove(string, pattern)
str_remove_all(string, pattern)

Simple Example: Removing Substrings

Let’s consider a simple example where we want to remove the word “apple” from a string.

str_remove("apple pie is delicious", "apple")
# Output: " pie is delicious"

Using str_remove_all

In cases where you want to remove all occurrences of a pattern from a string, you can use str_remove_all.

str_remove_all("apple pie with apple slices", "apple")
# Output: " pie with  slices"

Regular Expression Patterns

str_remove and str_remove_all functions can utilize regular expressions (regex) to define complex patterns that should be removed from strings.

Removing Special Characters:

str_remove_all("This is a sentence! It has punctuation.", "[[:punct:]]")
# Output: "This is a sentence It has punctuation"

Application in Vector of Strings

str_remove and str_remove_all can be applied to vectors of strings.

Let’s say you have a character vector representing various food items, and you want to remove the names of all fruits from each string.

food_items <- c("apple pie", "cherry tart", "banana split", "orange juice", "grape jelly", "pineapple cake")

cleaned_food_items <- str_remove_all(food_items, "apple|cherry|banana|orange|grape|pineapple")


Working with Data Frames

You can use str_remove in conjunction with the dplyr package to modify columns in a data frame.

# Load the dplyr package

# Create a sample data frame
data <- data.frame(
  text = c("apple pie", "cherry tart", "banana split"),
  stringsAsFactors = FALSE

# Use str_remove with mutate to remove 'apple' from the text column
data <- data %>%
  mutate(text = str_remove(text, "apple"))

Advanced Examples

Example: Removing HTML Tags

Suppose you have a string containing HTML tags, and you wish to remove all the tags, keeping only the text content.

html_string <- "<p>This is a <b>paragraph</b> with <a href='#'>HTML tags</a>.</p>"
clean_string <- str_remove_all(html_string, "<[^>]+>")
# Output: "This is a paragraph with HTML tags."

Special Considerations

Escaping Special Characters: Special characters in regex patterns like ‘.’, ‘*’, ‘+’ etc., need to be escaped using two backslashes \\ to be considered literally.

Case Sensitivity: By default, the removal is case-sensitive, but it can be made case-insensitive using the regex function with the ignore_case parameter.

str_remove("Apple pie is delicious", regex("apple", ignore_case = TRUE))
# Output: " pie is delicious"


The str_remove and str_remove_all functions in R, furnished by the stringr package, serve as essential tools for anyone dealing with string manipulation. The utility of these functions is multifold, enabling the removal of simple to complex patterns, cleaning data, preprocessing text for analysis, and much more. By understanding the examples and methodologies shared in this article, users can leverage these functions to handle a wide array of string manipulation tasks effectively.

Whether it is about cleaning HTML tags from web-scraped data, getting rid of unwanted characters or whitespaces, or making modifications in a large text corpus, these functions allow extensive modifications and manipulations.

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