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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

Enhancing a data.frame with a data.table


When you process a dataset that is a Gigabyte or larger in size, you may find that a data.frame is rather inefficient. To address this issue, you can use the enhanced extension of data.framedata.table. In this recipe, we will show how to create a data.table in R.

Getting ready

Download the purchase_view.tab and purchase_order.tab datasets from the following GitHub links, respectively:

How to do it…

Perform the following steps to create a data.table:

  1. First, install and load the data.table package using the following commands:

    > install.packages("data.table")
    > library(data.table)
    
  2. Next, we can create an R data frame using read.table:

    > purchase <- read.table("purchase_view.tab", header=TRUE, sep='\t')
     [1] "data.frame"
    > dim(purchase)
    [1] 1191486       4
    
    > order <- read.table("purchase_order...
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