Beginning R (eBook)
XXIII, 327 Seiten
Apress (Verlag)
978-1-4842-0373-6 (ISBN)
Beginning R, Second Edition is a hands-on book showing how to use the R language, write and save R scripts, read in data files, and write custom statistical functions as well as use built in functions. This book shows the use of R in specific cases such as one-way ANOVA analysis, linear and logistic regression, data visualization, parallel processing, bootstrapping, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. It has been completely re-written since the first edition to make use of the latest packages and features in R version 3.
R is a powerful open-source language and programming environment for statistics and has become the de facto standard for doing, teaching, and learning computational statistics. R is both an object-oriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets, with a constantly evolving ecosystem of packages providing new functionality for data analysis. R has also become popular in commercial use at companies such as Microsoft, Google, and Oracle. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for data analysis and research.
What You Will Learn:
- How to acquire and install R
- Hot to import and export data and scripts
- How to analyze data and generate graphics
- How to program in R to write custom functions
- Hot to use R for interactive statistical explorations
- How to conduct bootstrapping and other advanced techniques
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.
Beginning R, Second Edition is a hands-on book showing howto use the R language, write and save R scripts, read in data files, and writecustom statistical functions as well as use built in functions. This book showsthe use of R in specific cases such as one-way ANOVA analysis, linear andlogistic regression, data visualization, parallel processing, bootstrapping,and more. It takes a hands-on, example-based approach incorporating bestpractices with clear explanations of the statistics being done. It has beencompletely re-written since the first edition to make use of the latestpackages and features in R version 3.R is a powerful open-source language and programmingenvironment for statistics and has become the de facto standard for doing,teaching, and learning computational statistics. R is both an object-oriented language and afunctional language that is easy to learn, easy to use, and completely free. Alarge community of dedicated R users and programmers provides an excellentsource of R code, functions, and data sets, with a constantly evolvingecosystem of packages providing new functionality for data analysis. R has alsobecome popular in commercial use at companies such as Microsoft, Google, andOracle. Your investment in learning R is sure to pay off in the long term as Rcontinues to grow into the go to language for data analysis and research.What You Will Learn:How to acquire and install RHot to import and export data and scriptsHow to analyze data and generate graphicsHow to program in R to write custom functionsHot to use R for interactive statistical explorationsHow to conduct bootstrapping and other advancedtechniques
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.
Part I. Learning the R Language1. Getting Started2. Dealing with Dates, Strings, and Data Frames3. Input and Output4. Control StructuresPart II. Using R for Descriptive Statistics 5. Functional Programming6. Probability Distributions7. Working with TablesPart III. Using R for Inferential Statistics 8. Descriptive Statistics and Exploratory Data Analysis9. Working with Graphics10. Traditional Statistical Methods11. Modern Statistical Methods12. Analysis of Variance13. Correlation and Regression14. Multiple Regression15. Logistic Regression16. Modern Statistical Methods IIPart IV. Taking R to the Next Level 17. Data Visualization Cookbook18. High-performance Computing19. Text Mining
| Erscheint lt. Verlag | 23.10.2015 |
|---|---|
| Zusatzinfo | XXIII, 327 p. 168 illus. |
| Verlagsort | Berkeley |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Informatik ► Theorie / Studium ► Compilerbau | |
| Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra | |
| ISBN-10 | 1-4842-0373-9 / 1484203739 |
| ISBN-13 | 978-1-4842-0373-6 / 9781484203736 |
| Haben Sie eine Frage zum Produkt? |
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