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R for Data Science Cookbook (n)

You're reading from   R for Data Science Cookbook (n) Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

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Product type Paperback
Published in Jul 2016
Publisher
ISBN-13 9781784390815
Length 452 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Toc

Table of Contents (14) Chapters Close

Preface 1. Functions in R FREE CHAPTER 2. Data Extracting, Transforming, and Loading 3. Data Preprocessing and Preparation 4. Data Manipulation 5. Visualizing Data with ggplot2 6. Making Interactive Reports 7. Simulation from Probability Distributions 8. Statistical Inference in R 9. Rule and Pattern Mining with R 10. Time Series Mining with R 11. Supervised Machine Learning 12. Unsupervised Machine Learning Index

Introduction


Before using data to answer critical business questions, the most important thing is to prepare it. Data is normally archived in files, and using Excel or text editors allows it to be easily obtained. However, data can be located in a range of different sources, such as databases, websites, and various file formats. Being able to import data from these sources is crucial.

There are four main types of data. Data recorded in text format is the simplest. As some users require storing data in a structured format, files with a .tab or .csv extension can be used to arrange data in a fixed number of columns. For many years, Excel has had a leading role in the field of data processing, and this software uses the .xls and .xlsx formats. Knowing how to read and manipulate data from databases is another crucial skill. Moreover, as most data is not stored in a database, one must know how to use the web scraping technique to obtain data from the Internet. As part of this chapter, we introduce...

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