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

Python

# 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.

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. Python

</p><pre><code class="language-python3">text = readLines(file.choose())
</code></pre><p></p><p dir="ltr"><b><strong>Load the data as a corpus:</strong></b><br><gfg-tabs data-run-ide="false" data-mode="light"><gfg-tab slot="tab">Python

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

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

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.
Python

</p><pre><code class="language-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)
</code></pre><p></p><p dir="ltr"><b><strong>Step 4: Build a term-document Matrix</strong></b></p><p dir="ltr"><span>Document matrix is a table containing the frequency of the words. Column names are words and row names are documents. The function </span><b><strong>TermDocumentMatrix()</strong></b><span> from text mining package can be used as follows. </span><br><span> </span></p><gfg-tabs data-run-ide="false" data-mode="light"><gfg-tab slot="tab">Python</gfg-tab><gfg-panel slot="panel" data-code-lang="python3"><pre><code class="language-python3">dtm = TermDocumentMatrix(docs1)
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.

Python

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.

Python

# 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.

Generating Word Cloud in R Programming

Implementation in R

Drawbacks of Word Clouds

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