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Data Analysis with R

You're reading from  Data Analysis with R

Product type Book
Published in Dec 2015
Publisher
ISBN-13 9781785288142
Pages 388 pages
Edition 1st Edition
Languages
Toc

Table of Contents (20) Chapters close

Data Analysis with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. RefresheR 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. Predicting Continuous Variables 9. Predicting Categorical Variables 10. Sources of Data 11. Dealing with Messy Data 12. Dealing with Large Data 13. Reproducibility and Best Practices Index

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—like import a data set, run a linear model, and plot the data and regression line in no more than 4 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 in a low-level language, so a language like that is inappropriate for agile analytics.

One solution is to use R abstractions...

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