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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

dplyr versus data.table


You might now be wondering, "which package should we use?"

The dplyr and data.table packages provide a spectacularly different syntax and a slightly less determinative difference in performance. Although data.table seems to be slightly more effective on larger datasets, there is no clear winner in this spectrum—except for doing aggregations on a high number of groups. And to be honest, the syntax of dplyr, provided by the magrittr package, can be also used by the data.table objects if needed.

Also, there is another R package that provides pipes in R, called the pipeR package, which claims to be a lot more effective on larger datasets than magrittr. This performance gain is due to the fact that the pipeR operators do not try to be smart like the F# language's |>-compatible operator in magrittr. Sometimes, this performance overhead is estimated to be 5-15 times more than the ones where no pipes are used at all.

One should take into account the community and support...

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