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R for Data Science Cookbook (n)

You're reading from  R for Data Science Cookbook (n)

Product type Book
Published in Jul 2016
Publisher
ISBN-13 9781784390815
Pages 452 pages
Edition 1st Edition
Languages
Author (1):
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Profile icon Yu-Wei, Chiu (David Chiu)

Table of Contents (19) Chapters

R for Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. Functions in R 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

Creating time series data


To begin time series analysis, we need to create a time series object from a numeric vector or matrix. In this recipe, we introduce how to create a time series object from the financial report of Taiwan Semiconductor (2330.TW) with the ts function.

Getting ready

Download the tw2330_finance.csv dataset from the following GitHub link:

https://github.com/ywchiu/rcookbook/raw/master/chapter10/tw2330_finance.csv

How to do it…

Please perform the following steps to create time series data:

  1. First, read Taiwan Semiconductor's financial report into an R session:

    > tw2330 = read.csv('tw2330_finance.csv', header=TRUE)
    > str(tw2330)
    'data.frame': 32 obs. of  5 variables:
     $ Time            : Factor w/ 32 levels "2008Q1","2008Q2",..: 1 2 3 4 5 6 7 8 9 10 ...
     $ Total.Income    : int  875 881 930 646 395 742 899 921 922 1050 ...
     $ Gross.Sales     : num  382 402 431 202 74.8 343 429 447 442 519 ...
     $ Operating.Income: num  291 304 329 120 12.1 251 320 336 341 405 ...
     $ EPS ...
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