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Data Analysis with R, Second Edition - Second Edition

You're reading from  Data Analysis with R, Second Edition - Second Edition

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Pages 570 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (24) Chapters close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. RefresheR 2. The Shape of Data 3. Describing Relationships 4. Probability 5. Using Data To Reason About The World 6. Testing Hypotheses 7. Bayesian Methods 8. The Bootstrap 9. Predicting Continuous Variables 10. Predicting Categorical Variables 11. Predicting Changes with Time 12. Sources of Data 13. Dealing with Missing Data 14. Dealing with Messy Data 15. Dealing with Large Data 16. Working with Popular R Packages 17. Reproducibility and Best Practices 1. Other Books You May Enjoy Index

Creating and plotting time series


A common approach in tutorials is to jump right into manipulating a plotting time series by using one of R's many built-in time series data sets. Here, I'd like to explain how to actually create a time series object (an R object of type ts) like you would if you were to have to perform forecasting on your own data.

I made available some special data sets that we'll use in this and future chapters by putting on a repository on the internet.

Let's first start by downloading one of these data sets and peering into its contents:

> download.file("https://raw.githubusercontent.com/tonyfischetti/dawr/master/forecasting/school-supplies.csv", "./school-supplies.csv")
> school <- read.csv("./school-supplies.csv")
> head(school)
thedate hits
1 2004-01 20
2 2004-02 24
3 2004-03 19
4 2004-04 26
5 2004-05 25
6 2004-06 24
> start(schoolts)
[1] 2004 1
> end(schoolts)
[1] 2018 3

This is a data set that contains the number of Google searches for school supplies...

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