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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

Exercises

Practice the following exercises to revise the concept of reproducibility learned in this chapter:

  • Review: When we created the data frame from nothing, we combined a vector of 1,000 binomially distributed random variables, 1,000 normally distributed random variables, and a vector of two colors, red and white. Since all the columns in a data frame have to be the same length, how did R allow this? What is the property of vectors that allows this?
  • Seek out, read, and attempt to understand the source code of some of your favorite R packages. What version control system is the author of the package using?
  • Carefully review the analysis that was used as an example in this chapter. In what manner can this analysis be improved upon? Look at the distribution of the combined SAT scores in NYC schools. Why was modeling the SAT scores with a Gaussian likelihood function a very bad...
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