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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

Managing data with a data.table

The two major advantages of a data.table as compared to a data.frame are the speed and clearer syntax of the former. Similar to a data.frame, we can perform operations to slice and subset a data.table. Here, we introduce some operations that you can perform on data.table.

Getting ready

Ensure that you completed the Enhancing a data.frame with a data.table recipe to load purchase_view.tab and purchase_order.tab as both data.frame and data.table into your R environment.

How to do it…

Perform the following steps to perform data manipulation on data.table:

  1. First, use the head function to view the first three rows:
    > head(purchase.dt[1:3])
                      Time Action         User     Product
    1: 2015-07-01 00:00:01   view   U129297265 P0023468384
    2: 2015-07-01 00:00:03   view   U321001337 P0018926456
    3: 2015-07-01 00:00:05   view U10070718237 P0000063593
    
    > head(purchase[1:3])
                     Time Action         User
    1 2015-07-01 00:00:01   view   U129297265...
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