Generating Word Cloud in R Programming

Word Cloud is a data visualization technique used for representing text data in which the size of each word indicates its frequency or importance. Significant textual data points can be highlighted using a word cloud. Word clouds are widely used for analyzing data from social network websites.
 

Why Word Cloud?

The reasons one should use word clouds to present the text data are:
 

• Word clouds add simplicity and clarity. The most used keywords stand out better in a word cloud
• Word clouds are a potent communication tool. They are easy to understand, to be shared, and are impactful.
• Word clouds are visually engaging than a table data.

Implementation in R

Here are steps to create a word cloud in R Programming.
 

Step 1: Create a Text File

Copy and paste the text in a plain text file (e.g:file.txt) and save the file.

Step 2: Install and Load the Required Packages

Step 3: Text Mining

• Load the Text: 
  The text is loaded using Corpus() function from text mining(tm) package. Corpus is a list of a document. 
  
  1. Start by importing text file created in step 1: 
     To import the file saved locally in your computer, type the following R code. You will be asked to choose the text file interactively. Python3

     text = readLines(file.choose())
     

  2. 
      Load the data as a corpus:
     Python3

     # VectorSource() function 
     # creates a corpus of 
     # character vectors
     docs = Corpus(VectorSource(text))   
     

  3. 
      Text transformation: 
     Transformation is performed using tm_map() function to replace, for example, special characters from the text like “@”, “#”, “/”.
     Python3

     toSpace = content_transformer
                  (function (x, pattern)
                   gsub(pattern, " ", x))
     docs1 = tm_map(docs, toSpace, "/")
     docs1 = tm_map(docs, toSpace, "@")
     docs1 = tm_map(docs, toSpace, "#")
     

• Cleaning the Text: 
  The tm_map() function is used to remove unnecessary white space, to convert the text to lower case, to remove common stopwords. Numbers can be removed using removeNumbers. 
    Python3

  # Convert the text to lower case
  docs1 = tm_map(docs1, 
          content_transformer(tolower))
  
  # Remove numbers
  docs1 = tm_map(docs1, removeNumbers)
  
  # Remove white spaces
  docs1 = tm_map(docs1, stripWhitespace)
  

Step 4: Build a term-document Matrix

Document matrix is a table containing the frequency of the words. Column names are words and row names are documents. The function TermDocumentMatrix() from text mining package can be used as follows. 
 

Step 5: Generate the Word Cloud

The importance of words can be illustrated as a word cloud as follows. 
 

The complete code for the word cloud in R is given below.
 

Output: 
 

Advantages of Word Clouds

• Analyzing customer and employee feedback.
• Identifying new SEO keywords to target.
• Word clouds are killer visualisation tools. They present text data in a simple and clear format
• Word clouds are great communication tools. They are incredibly handy for anyone wishing to communicate a basic insight

Drawbacks of Word Clouds

• Word Clouds are not perfect for every situation.
• Data should be optimized for context.
• Word clouds typically fail to give the actionable insights that needs to improve and grow the business.