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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

Decomposing time series

A seasonal time series is made up of seasonal components, deterministic trend components, and irregular components. In this recipe, we introduce how to use the decompose function to destruct a time series into these three parts.

Getting ready

Ensure you have completed the previous recipe by generating a time series object and storing it in two variables: m and m_ts.

How to do it…

Please perform the following steps to decompose a time series:

  1. First, use the window function to construct a time series object, m.sub, from m:
    > m.sub = window(m, start=c(2012, 1), end=c(2014, 4)) 
    > m.sub
         Qtr1 Qtr2 Qtr3 Qtr4
    2012 1055 1281 1414 1313
    2013 1328 1559 1626 1458
    2014 1482 1830 2090 2225
    > plot(m.sub)
    
    How to do it…

    Figure 6: A time series plot in a quarter

  2. Use the decompose function to destruct the time series object m.sub:
    > components <- decompose(m.sub)
    
  3. We can then use the names function to list the attributes of components:
    > names(components)
    [1] "x"  ...
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