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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

Understanding uniform distributions


If we roll a fair dice, the likelihood of drawing any side is equal. By plotting the samples in a bar plot, we can see bars with equal heights. This type of distribution is known as uniform distribution. In this recipe, we will introduce how to generate samples from a uniform distribution.

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 a sample from uniform distribution:

  1. First, we can create samples from uniform distribution by using the runif function:

    > set.seed(123)
    > uniform <- runif(n = 1000, min = 0, max = 1)
    
  2. We can then make a histogram plot out of the samples from the uniform distribution:

    > hist(uniform, main = "1,000 random variates from a uniform distribution")
    

    Figure 1: A histogram of 1,000 random variates from a uniform distribution

  3. For uniform distribution, we also can find the density at each point equals 1, and the density function...

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