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Mastering Data analysis with R

You're reading from   Mastering Data analysis with R Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781783982028
Length 396 pages
Edition 1st Edition
Languages
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Author (1):
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Gergely Daróczi Gergely Daróczi
Author Profile Icon Gergely Daróczi
Gergely Daróczi
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Table of Contents (17) Chapters Close

Preface 1. Hello, Data! 2. Getting Data from the Web FREE CHAPTER 3. Filtering and Summarizing Data 4. Restructuring Data 5. Building Models (authored by Renata Nemeth and Gergely Toth) 6. Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth) 7. Unstructured Data 8. Polishing Data 9. From Big to Small Data 10. Classification and Clustering 11. Social Network Analysis of the R Ecosystem 12. Analyzing Time-series 13. Data Around Us 14. Analyzing the R Community A. References Index

More complex time-series objects

The main limitation of the ts time-series R object class (besides the aforementioned x axis issue) is that it cannot deal with irregular time-series. To overcome this problem, we have several alternatives in R.

The zoo package and its reverse dependent xts packages are ts-compatible classes with tons of extremely useful methods. For a quick example, let's build a zoo object from our data, and see how it's represented by the default plot:

> library(zoo)
> zd <- zoo(daily[, -1, with = FALSE], daily[[1]])
> plot(zd)
More complex time-series objects

As we have defined the date column to act as the timestamp of the observations, it's not shown here. The x axis has a nice human-friendly date annotation, which is really pleasant after having checked a bunch of integer-annotated plots in the previous pages.

Of course, zoo supports most of the ts methods, such as diff, lag or cumulative sums; these can be very useful for visualizing data velocity:

> plot(cumsum(zd))
More complex time-series objects

Here...

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