Beginning R
An Introduction to Statistical Programming
(Sprache: Englisch)
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...
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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 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 advancedtechniques
Beginning R, Second Edition is a hands-on book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. This book shows the use of R in specific use cases such as simulations, discrete probability solutions, one-way ANOVA analysis, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. It has been updated to cover R version 3.
R is a powerful open-source implementation of the statistical language S, which was developed by AT&T. R has eclipsed S and the commercially-available S-Plus language, 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. R is also becoming adopted into commercial tools such as Oracle Database. 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 statistical exploration and research.
R is a powerful open-source implementation of the statistical language S, which was developed by AT&T. R has eclipsed S and the commercially-available S-Plus language, 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. R is also becoming adopted into commercial tools such as Oracle Database. 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 statistical exploration and research.
Inhaltsverzeichnis zu „Beginning R “
Part I. Learning the R Language1. Getting Started
2. Dealing with Dates, Strings, and Data Frames
3. Input and Output
4. Control Structures
Part II. Using R for Descriptive Statistics
5. Functional Programming
6. Probability Distributions
7. Working with Tables
Part III. Using R for Inferential Statistics
8. Descriptive Statistics and Exploratory Data Analysis
9. Working with Graphics
10. Traditional Statistical Methods
11. Modern Statistical Methods
12. Analysis of Variance
13. Correlation and Regression
14. Multiple Regression
15. Logistic Regression
16. Modern Statistical Methods II
Part IV. Taking R to the Next Level
17. Data Visualization Cookbook
18. High-performance Computing
19. Text Mining
Autoren-Porträt von Larry Pace, Joshua Wiley
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.
Bibliographische Angaben
- Autoren: Larry Pace , Joshua Wiley
- 2015, 2. Aufl., XXIII, 327 Seiten, Maße: 17,8 x 25,4 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1484203747
- ISBN-13: 9781484203743
- Erscheinungsdatum: 13.10.2015
Sprache:
Englisch
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