Larry Pace

R Recipes (eBook, PDF)

A Problem-Solution Approach

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Apress
• Seitenzahl: 264
• Erscheinungstermin: 24. Dezember 2014
• Englisch
• ISBN-13: 9781484201305
• Artikelnr.: 43797433

Autorenporträt

Dr. Larry Pace is a statistics author and educator, as well as a consultant. He lives in the upstate area of South Carolina in the town of Anderson. He is a professor of statistics, mathematics, psychology, management, and leadership. He has programmed in a variety of languages and scripting languages including R, Visual Basic, JavaScript, C##, PHP, APL, and in a long-ago world, Fortran IV. He writes books and tutorials on statistics, computers, and technology. He has also published many academic papers, and made dozens of presentations and lectures. He has consulted with Compaq Computers, AT&T, Xerox Corporation, the U.S. Navy, and International Paper. He has taught at Keiser University, Argosy University, Capella University, Ashford University, Anderson University (where he was the chair of the behavioral sciences department), Clemson University, Louisiana Tech University, LSU in Shreveport, the University of Tennessee, Cornell University, Rochester Institute of Technology, Rensselaer Polytechnic Institute, and the University of Georgia.

Inhaltsangabe

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