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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

Table of Contents (24) Chapters

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
RefresheR The Shape of Data Describing Relationships Probability Using Data To Reason About The World Testing Hypotheses Bayesian Methods The Bootstrap Predicting Continuous Variables Predicting Categorical Variables Predicting Changes with Time Sources of Data Dealing with Missing Data Dealing with Messy Data Dealing with Large Data Working with Popular R Packages Reproducibility and Best Practices Other Books You May Enjoy Index

The data.table package


One of the morals that I hope came across manifest in the previous chapter is that R is capable of much better performance than many expect. And, outside of calling low-level code and using parallelization, it is very widely agreed that there is no single best approach (taking personal preference and the largely subjective concept of readability out of the equation) to high-performance R than to learn how to properly wield data.table.

Many would be amazed at what previously intractable problems could be solved using this package. Often, problems that the uninitiated are keen to tackle with powerful remote servers, industrial databases, andtrendy tools can be handled quite easily and gracefully by data.table. And, with every update, data.table keeps getting faster and faster.

Before, we jump right into the code, it would behoove us to discuss what the data.table package gives us.

Note

Note that we will be using the made up variable, DT, to refer to a data.table in a generic...

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