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

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:

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.