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Jupyter Cookbook

You're reading from   Jupyter Cookbook Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788839440
Length 238 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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 Toomey Toomey
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Toomey
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Toc

Table of Contents (17) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. Installation and Setting up the Environment FREE CHAPTER 2. Adding an Engine 3. Accessing and Retrieving Data 4. Visualizing Your Analytics 5. Working with Widgets 6. Jupyter Dashboards 7. Sharing Your Code 8. Multiuser Jupyter 9. Interacting with Big Data 10. Jupyter Security 11. Jupyter Labs Index

Producing a word cloud using R


While many of the visualizations portray numeric data, we canalso produce textual visualizations. In this case, we will produce a word cloud from data on a recent speech by president Trump.

How to do it...

We can use the following script:

library(tm)
#install.packages('wordcloud', repos='http://cran.us.r-project.org')
library(wordcloud)

#extracted from https://www.lifesitenews.com/news/jesus-birth-changed-the-course-of-human-history-trumps-extraordinary-2017-c
page <- readLines("trump-speech.txt")

# produce corpus of text
corpus <- Corpus(VectorSource(page))

# convert to lower case
corpus <- tm_map(corpus, tolower)
# remove punctuation
corpus <- tm_map(corpus, removePunctuation)
# remove numbers
corpus <- tm_map(corpus, removeNumbers)
# remove stop words
corpus <- tm_map(corpus, removeWords, stopwords("English"))

# reconfigure corpus as text document
#corpus <- tm_map(corpus, PlainTextDocument)
# create document term matrix from corpus...
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