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Learning R Programming

You're reading from   Learning R Programming Language, tools, and practical techniques

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Product type Paperback
Published in Oct 2016
Publisher Packt
ISBN-13 9781785889776
Length 582 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Kun Ren Kun Ren
Author Profile Icon Kun Ren
Kun Ren
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Table of Contents (16) Chapters Close

Preface 1. Quick Start FREE CHAPTER 2. Basic Objects 3. Managing Your Workspace 4. Basic Expressions 5. Working with Basic Objects 6. Working with Strings 7. Working with Data 8. Inside R 9. Metaprogramming 10. Object-Oriented Programming 11. Working with Databases 12. Data Manipulation 13. High-Performance Computing 14. Web Scraping 15. Boosting Productivity

Using statistical functions


R is highly productive in doing statistical computing and modeling since it provides a good variety of functions ranging from random sampling to statistical testing. The functions in the same category share a common interface. In this section, I will demonstrate a number of examples so that you can draw inferences about the usage of other similar functions.

Sampling from a vector

In statistics, the study of a population often begins with a random sample of it. The sample() function is designed for drawing a random sample from a given vector or list. In default, sample() draws a sample without replacement. For example, the following code draws a sample of five from a numeric vector without replacement:

sample(1:6, size = 5)
## [1] 2 6 3 1 4 

With replace = TRUE, the sampling is done with replacement:

sample(1:6, size = 5, replace = TRUE)
## [1] 3 5 3 4 2 

Although sample() is often used to draw samples from a numeric vector, it also works with other...

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