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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

Generating random samples


In this section, we will introduce how to generate random samples from a given population with the sample and sample.int functions.

Getting ready

In this recipe, you need to prepare your environment with R installed.

How to do it…

Please perform the following steps to generate random samples.

  1. First, generate random samples from 1 to 10:

    > sample(10)
    
  2. If you would like to reproduce the same samples, you can set the random seed beforehand:

    > set.seed(123)
    > sample(10)
     [1]  3  8  4  7  6  1 10  9  2  5
    
  3. You can then randomly choose two samples from 1 to 10:

    > sample(10,2)
    [1] 10  5
    
  4. If the population and sample size are required arguments, you can also use the sample.int function:

    > sample.int(10,size=2)
    [1] 7 6
    
  5. For example, one can simulate a lottery game and generate six random samples from a given population with a size of 42:

    > sample.int(42,6)
    [1]  5 37 10  2 13 36
    
  6. Alternatively, one can generate random samples with replacements by setting the replace...

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