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Generating Word Cloud in R Programming

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  • Last Updated : 29 Jul, 2021

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

Python3




# install the required packages
install.packages("tm")           # for text mining
install.packages("SnowballC")    # for text stemming
install.packages("wordcloud")    # word-cloud generator
install.packages("RColorBrewer") # color palettes
  
# load the packages
library("tm")
library("SnowballC")
library("wordcloud")
library("RColorBrewer")

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. 
 

Python3




dtm = TermDocumentMatrix(docs)
m = as.matrix(dtm)
v = sort(rowSums(m), decreasing = TRUE)
d = data.frame(word = names(v), freq = v)
head(d, 10)

Step 5: Generate the Word Cloud

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

Python3




wordcloud(words = d$word, 
          freq = d$freq,
          min.freq = 1
          max.words = 200,
          random.order = FALSE, 
          rot.per = 0.35
          colors = brewer.pal(8, "Dark2"))

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

Python3




# R program to illustrate
# Generating word cloud
  
# Install the required packages
install.packages("tm")           # for text mining
install.packages("SnowballC")    # for text stemming
install.packages("wordcloud")    # word-cloud generator
install.packages("RColorBrewer") # color palettes
   
# Load the packages
library("tm")
library("SnowballC")
library("wordcloud")
library("RColorBrewer")
  
# To choose the text file
text = readLines(file.choose())
  
# VectorSource() function 
# creates a corpus of 
# character vectors
docs = Corpus(VectorSource(text))   
  
# Text transformation
toSpace = content_transformer(
              function (x, pattern)
              gsub(pattern, " ", x))
docs1 = tm_map(docs, toSpace, "/")
docs1 = tm_map(docs, toSpace, "@")
docs1 = tm_map(docs, toSpace, "#")
strwrap(docs1)
  
# Cleaning the Text
docs1 = tm_map(docs1, content_transformer(tolower))
docs1 = tm_map(docs1, removeNumbers)
docs1 = tm_map(docs1, stripWhitespace)
  
# Build a term-document matrix
dtm = TermDocumentMatrix(docs)
m = as.matrix(dtm)
v = sort(rowSums(m), 
         decreasing = TRUE)
d = data.frame(word = names(v),
               freq = v)
head(d, 10)
  
# Generate the Word cloud
wordcloud(words = d$word, 
          freq = d$freq,
          min.freq = 1
          max.words = 200,
          random.order = FALSE, 
          rot.per = 0.35
          colors = brewer.pal(8, "Dark2"))

Output: 
 

output screen

output screen

 

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.

 


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