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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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Toc

Table of Contents (17) Chapters Close

Preface 1. Hello, Data! FREE CHAPTER 2. Getting Data from the Web 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

Computing new variables


One of the most trivial actions we usually perform while restructuring a dataset is to create a new variable. For a traditional data.frame, it's as simple as assigning a vector to a new variable of the R object.

Well, this method also works with data.table, but the usage is deprecated due to the fact that there is a much more efficient way of creating one, or even multiple columns in the dataset:

> hflights_dt <- data.table(hflights)
> hflights_dt[, DistanceKMs := Distance / 0.62137]

We have just computed the distances, in kilometers, between the origin and destination airports with a simple division; although all the hardcore users can head for the udunits2 package, which includes a bunch of conversion tools based on Unidata's udunits library.

And as can be seen previously, data.table uses that special := assignment operator inside of the square brackets, which might seem strange at first glance, but you will love it!

Note

The := operator can be more than 500...

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