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Learning Bayesian Models with R

You're reading from   Learning Bayesian Models with R Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

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Product type Paperback
Published in Oct 2015
Publisher Packt
ISBN-13 9781783987603
Length 168 pages
Edition 1st Edition
Languages
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Author (1):
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Hari Manassery Koduvely Hari Manassery Koduvely
Author Profile Icon Hari Manassery Koduvely
Hari Manassery Koduvely
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Table of Contents (11) Chapters Close

Preface 1. Introducing the Probability Theory FREE CHAPTER 2. The R Environment 3. Introducing Bayesian Inference 4. Machine Learning Using Bayesian Inference 5. Bayesian Regression Models 6. Bayesian Classification Models 7. Bayesian Models for Unsupervised Learning 8. Bayesian Neural Networks 9. Bayesian Modeling at Big Data Scale Index

Managing data in R


Before we start any serious programming in R, we need to learn how to import data into an R environment and which data types R supports. Often, for a particular analysis, we will not use the entire dataset. Therefore, we need to also learn how to select a subset of the data for any analysis. This section will cover these aspects.

Data Types in R

R has five basic data types as follows:

  • Integer

  • Numeric (real)

  • Complex

  • Character

  • Logical (True/False)

The default representation of numbers in R is double precision real number (numeric). If you want an integer representation explicitly, you need to add the suffix L. For example, simply entering 1 on the command prompt will store 1 as a numeric object. To store 1 as an integer, you need to enter 1L. The command class(x) will give the class (type) of the object x. Therefore, entering class(1) on command prompt will give the answer numeric whereas entering class(1L) will give the answer integer.

R also has a special number Inf that represents...

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