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  • Book Overview & Buying R for Data Science
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R for Data Science

R for Data Science

By : Toomey
3.4 (5)
Buy this Book
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R for Data Science

R for Data Science

3.4 (5)
By: Toomey
Buy this Book

Overview of this book

If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.
Table of Contents (14 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Data Mining Patterns
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1. Data Mining Patterns
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Cluster analysis
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Anomaly detection
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Association rules
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Questions
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Summary
2
2. Data Mining Sequences
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2. Data Mining Sequences
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Patterns
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Questions
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Summary
3
3. Text Mining
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3. Text Mining
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Packages
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Questions
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Summary
4
4. Data Analysis – Regression Analysis
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4. Data Analysis – Regression Analysis
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Packages
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Questions
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Summary
5
5. Data Analysis – Correlation
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5. Data Analysis – Correlation
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Packages
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Questions
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Summary
6
6. Data Analysis – Clustering
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6. Data Analysis – Clustering
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Packages
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K-means clustering
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Questions
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Summary
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7. Data Visualization – R Graphics
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7. Data Visualization – R Graphics
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Packages
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Questions
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Summary
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8. Data Visualization – Plotting
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8. Data Visualization – Plotting
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Packages
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Scatter plots
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Bar charts and plots
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Questions
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Summary
9
9. Data Visualization – 3D
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9. Data Visualization – 3D
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Packages
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Generating 3D graphics
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Questions
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Summary
10
10. Machine Learning in Action
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10. Machine Learning in Action
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Packages
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Dataset
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Questions
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Summary
11
11. Predicting Events with Machine Learning
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11. Predicting Events with Machine Learning
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Automatic forecasting packages
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Questions
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Summary
12
12. Supervised and Unsupervised Learning
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12. Supervised and Unsupervised Learning
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Packages
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Questions
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Summary
13
Index
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Index

Packages

While the standard R system has a number of features and functions available, one of the better aspects of R is the use of packages to add functionalities. A package contains a number of functions (and sometimes sample data) that can be used to solve a particular problem in R. Packages are developed by interested groups for the general good of all R developers. In this chapter, we will be using the following packages:

  • tm: This contains text mining tools
  • XML: This contains XML processing tools

Text processing

R has built-in functions for manipulating text. These include adjustments to the text to make it more analyzable (such as using word stems or removing punctuation) and developing a document matrix showing usage of words throughout a document. Once these steps are done, we can then submit our documents to analysis and clustering.

Example

In this example, we will perform the following steps:

  1. We will take an HTML document from the Internet.
  2. We will clean up the document using text processing...
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Tech Concepts
36
Programming languages
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R for Data Science
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