R Recipes: A Problem-Solution Approach Front Cover

R Recipes: A Problem-Solution Approach

  • Length: 264 pages
  • Edition: 1
  • Publisher:
  • Publication Date: 2014-12-19
  • ISBN-10: 1484201310
  • ISBN-13: 9781484201312
  • Sales Rank: #4844297 (See Top 100 Books)
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

Chapter 1. Migrating to R
Chapter 2. Input and Output
Chapter 3. Data Structures
Chapter 4. Merging and Reshaping Datasets
Chapter 5. Working with Dates and Strings
Chapter 6. Working with Tabular Data
Chapter 7. Working with Numerical Data
Chapter 8. Graphics and Data Visualization
Chapter 9. Probability Distributions
Chapter 10. Tests of Differences
Chapter 11. Tests of Relationships
Chapter 12. Modern Robust Statistics
Chapter 13. Writing Functions
Chapter 14. Working with Financial Data
Chapter 15. Dealing with Big Data
Chapter 16. Introduction to Text and Data Mining

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