Stop Word Removal in R

4 min read · Feb 23, 2023

In addition to Python, R also has built-in functions and packages for stop word removal in text preprocessing. The tm (text mining) package is a popular package in R that provides a variety of functions for text preprocessing, including stop word removal.

Here’s an example of how to remove stop words from a text string in R using the tm package:

# install and load the tm package
install.packages("tm")
library(tm)

# define a text string
text <- "This is a sample text for stop word removal in R."

# convert the text to a corpus object
corpus <- Corpus(VectorSource(text))

# define a custom stop word list
custom_stopwords <- c("is", "a", "for", "in")

# remove the stop words
corpus_cleaned <- tm_map(corpus, removeWords, stopwords("english"))
corpus_cleaned <- tm_map(corpus_cleaned, removeWords, custom_stopwords)

# convert the cleaned corpus back to a text string
text_cleaned <- as.character(corpus_cleaned[[1]])

In this example, we first installed and loaded the tm package. We then defined a text string and converted it to a Corpus object using the VectorSource function. We also defined a custom stop word list containing words that we want to remove from the text.

We then used the tm_map function to remove stop words from the corpus. The removeWords function is used to remove both default English stop words as well as custom stop words that we defined. Finally, we converted the cleaned corpus back to a text string using the as.character function.

The resulting cleaned text string in this example would be:

[1] "sample text stop word removal R."

As we can see, the stop words “This”, “is”, “a”, “for”, and “in” have been removed from the text string. The tm package also provides other useful functions for text preprocessing, such as stemming, lemmatization, and case conversion.

Stop Words Removal in R with a Text File as Input

# install and load the tm package
install.packages("tm")
library(tm)

# read the text file
doc <- readLines("path/to/file.txt")

# convert the text to a corpus object
corpus <- Corpus(VectorSource(doc))

# remove the stop words
corpus_cleaned <- tm_map(corpus, removeWords, stopwords("english"))

# convert the cleaned corpus back to a text string
doc_cleaned <- as.character(corpus_cleaned[[1]])

In this example, we first read in a text file using the readLines function. We then converted the text to a Corpus object and removed default English stop words using the removeWords function with the stopwords("english") argument. Finally, we converted the cleaned corpus back to a text string using the as.character function.

The tm package provides a lot of flexibility for stop word removal and other text preprocessing tasks in R. It also allows for the creation of custom stop word lists, just like in Python’s NLTK package. Additionally, the tidytext package in R is another useful package for text preprocessing and analysis, which also includes functions for stop word removal.

Other R Packages for Stop Words Removal

There are several other packages in R that provide functions for stop word removal and text preprocessing, including:

1. tidytext: This package provides tools for converting text data into tidy data frames and includes functions for removing stop words.
2. quanteda: This package provides a suite of functions for text analysis and includes functions for stop word removal as well as stemming, lemmatization, and other text preprocessing tasks.
3. textstem: This package provides functions for stemming text data and includes a built-in stop word list for English.
4. SnowballC: This package provides functions for stemming text data using the Snowball algorithm and includes a built-in stop word list for several languages.
5. openNLP: This package provides functions for natural language processing and includes a function for removing stop words.

Each of these packages offers different features and capabilities for text preprocessing and analysis, so it is important to choose the package that best fits your needs and data. Additionally, custom stop word lists can be created using any of these packages by defining a character vector of stop words and passing it as an argument to the corresponding stop word removal function

Using ‘tidytext’ to Remove Stop Words

I am a fan of the tidyverse, a collection of R packages for data analysis and data science. Here’s an example and elegant way to remove stop words using the tidytext package in R:

# install and load the tidytext package
install.packages("tidytext")
library(tidytext)

# define a text string
text <- "This is a sample text for stop word removal with tidytext in R."

# convert the text to a tidy data frame
df <- data.frame(text)

# remove stop words
df_cleaned <- df %>%
  unnest_tokens(word, text) %>%
  anti_join(stop_words)

# convert the cleaned data frame back to a text string
text_cleaned <- paste(df_cleaned$word, collapse = " ")

(Note: you need to load the tidyverse before you can use the pipe operator %>%)

In this example, I first installed and loaded the tidytext package. I then defined a text string and converted it to a data frame using the data.frame function.

We then used the unnest_tokens function to split the text into individual words, and the anti_join function to remove default English stop words from the data frame. Finally, we converted the cleaned data frame back to a text string using the paste function.

The resulting cleaned text string in this example would be:

[1] "sample text stop word removal tidytext R."

As we can see, the stop words “This”, “is”, “a”, “for”, “with”, “in” have been removed from the text string.

The tidytext package is a popular package in R for text preprocessing and analysis, and it provides many other useful functions for working with text data. It is also compatible with the dplyr package, which provides a powerful set of tools for data manipulation and analysis.