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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


The most prominent feature of R is that it implements a wide variety of statistical packages. Using these packages, it is easy to obtain descriptive statistics about a dataset or infer the distribution of a population from sample data. Moreover, with R's plotting capabilities, we can easily display data in a variety of charts.

To apply statistical methods in R, the user can categorize the method of implementation into descriptive statistics and inferential statistics, described as follows:

  • Descriptive statistics: These are used to summarize the characteristics of data. The user can use mean and standard deviation to describe numerical data, and they can use frequency and percentages to describe categorical data.

  • Inferential statistics: This is when, based on patterns within sample data, the user can infer the characteristics of the population. Methods relating to inferential statistics include hypothesis testing, data estimation, data correlation, and relationship modeling. Inference...

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