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Data Analysis with R, Second Edition

You're reading from   Data Analysis with R, Second Edition A comprehensive guide to manipulating, analyzing, and visualizing data in R

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Length 570 pages
Edition 2nd Edition
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Author (1):
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Tony Fischetti Tony Fischetti
Author Profile Icon Tony Fischetti
Tony Fischetti
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Table of Contents (19) Chapters Close

Preface 1. RefresheR FREE CHAPTER 2. The Shape of Data 3. Describing Relationships 4. Probability 5. Using Data To Reason About The World 6. Testing Hypotheses 7. Bayesian Methods 8. The Bootstrap 9. Predicting Continuous Variables 10. Predicting Categorical Variables 11. Predicting Changes with Time 12. Sources of Data 13. Dealing with Missing Data 14. Dealing with Messy Data 15. Dealing with Large Data 16. Working with Popular R Packages 17. Reproducibility and Best Practices 18. Other Books You May Enjoy

Using Rcpp

Contrary to what I sometimes like to believe, there are other computer programming languages than just R. R, and languages like Python, Perl, and Ruby, are considered high-level languages, because they offer a greater level of abstraction from computer representations and resource management than the lower-level languages. For example, in some lower-level languages, you must specify the data type of the variables you create and manage the allocation of RAM manually - C, C++, and Fortran are of this type.

The high level of abstraction R provides allows us to do amazing things very quickly, such as import a data set, run a linear model, and plot the data and regression line in no more than four lines of code! On the other hand, nothing quite beats the performance of carefully crafted lower-level code. Even so, it would take hundreds of lines of code to run a linear model...

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