Data Science – Data Cleaning R Commands for Text Classification Problems

This article represents concepts and related R command set used to clean the text in order to make it ready for text classification. The R command set belongs to tm package. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos.

Lets load a set of messages along with appropriate classification using following command.

messages <- read.table( file.choose(), sep="\t", stringsAsFactors=FALSE)

The messages data frame could have two features, such as type and text where each piece of text is associated with an appropriate type. Once done, lets go ahead and create a Corpus object out of all the message text. Following command helps to create a Corpus object. For those of you who are new to Corpus object, note that it comes as part of famous text mining R package named as “tm”.

corpus <- Corpus(VectorSource(messages$text))

Once Corpus object is created it is time to clean the text.


R Commands for Cleaning Text

Following is cleaned as part of text cleaning activity:

  • Change the case of all words to lowercase. In earlier versions of tm package, it was ok to use commands such as tm_map(corpus, tolower). However, if you have recently installed tm package, using the tm_map command as mentioned earlier would throw error such asĀ Error: inherits(doc, “TextDocument”) is not TRUE. Thus, with latest version of tm package, it is recommended to use following command for chaging to lower case. tm_map(corpus, content_transformer(tolower))
  • Remove numbers
  • Remove punctuation
  • Remove stop words. These are words such as to, and, or, but etc.
  • Strip whitespaces

Following is command set that achieves above objectives:

# Change all the words to lowercase
corpus_clean <- tm_map(corpus, content_transformer(tolower))

# Remove all the numbers
corpus_clean <- tm_map(corpus_clean, removeNumbers)

# Remove the stop words such as to, and, or etc.
corpus_clean <- tm_map(corpus_clean, removeWords, stopwords())

# Remove punctuation
corpus_clean <- tm_map(corpus_clean, removePunctuation)

# Remove whitespaces
corpus_clean <- tm_map(corpus_clean, stripWhitespace)


Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog,
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