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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 Poisson random variates


Poisson distribution is best to use when expressing the probability of events occurring with a fixed time interval. These events are assumed to happen with a known mean rate, λ, and the event of the time is independent of the last event. Poisson distribution can be applied to examples such as incoming calls to customer service. In this recipe, we will demonstrate how to generate samples from Poisson 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 sample data from Poisson distribution:

  1. Similar to normal distribution, we can use rpois to generate samples from Poisson distribution:

    > set.seed(123)
    > poisson <- rpois(1000, lambda=3)
    
  2. You can then plot sample data from a Poisson distribution into a histogram:

    > hist(poisson, main="A histogram of a Poisson distribution")
    

    Figure 5: A histogram of a Poisson distribution

  3. You can then obtain the height...

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