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R Recipes

A Problem-Solution Approach

By Larry A. Pace

  • eBook Price: $27.99
  • Print Book Price: $39.99
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R Recipes is your handy problem-solution reference for learning and using the popular R programming language for statistics and other numerical analysis. Packed with hundreds of code and visual recipes, this book helps you to quickly learn the fundamentals and explore the frontiers of programming, analyzing and using R.

Full Description

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  • ISBN13: 978-1-484201-31-2
  • 264 Pages
  • User Level: Beginner to Intermediate
  • Publication Date: December 24, 2014
  • Available eBook Formats: EPUB, MOBI, PDF

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Full Description

R Recipes is your handy problem-solution reference for learning and using the popular R programming language for statistics and other numerical analysis. Packed with hundreds of code and visual recipes, this book helps you to quickly learn the fundamentals and explore the frontiers of programming, analyzing and using R.

R Recipes provides textual and visual recipes for easy and productive templates for use and re-use in your day-to-day R programming and data analysis practice. Whether you're in finance, cloud computing, big or small data analytics, or other applied computational and data science - R Recipes should be a staple for your code reference library.

What you’ll learn

  • Tips and tricks for making the migration to R smooth and seamless
  • Code recipes for I/O, data structures, transformations, strings, dates and more
  • How to use graphics and visualization in R
  • Using R for probability, statistics, hypothesis tests, linear regression time series and more
  • How to write practical code and templates for finance and big data analytics
  • Code for doing numerics or numerical analysis, beyond just statistical programming

Who this book is for

If you’re new to R, then R Recipes will help get you started. If you’re an experienced data programmer, then it will remind you as well as expand upon your knowledge base; so, you’ll get the job done faster and learn more about R in the process.

Table of Contents

Table of Contents

1. Migrating to R

2. Input and Output

3. Data Structures

4. Merging and Reshaping Datasets

5. Working with Dates and Strings

6. Working with Tabular Data

7. Working with Numerical Data

8. Graphics and Data Visualization

9. Probability Distributions

10. Tests of Differences

11. Tests of Relationships

12. Modern Robust Statistics

13. Writing Functions

14. Working with Financial Data

15. Dealing with Big Data

16. Introduction to Text and Data Mining

Errata

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