Beginning R: An Introduction to Statistical Programming, Second Edition

  • 5h 3m
  • Joshua F. Wiley, Larry A. Pace
  • Apress
  • 2015

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

About the Authors

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.

Joshua Wiley is a research fellow at the Mary MacKillop Institute for Health Research at the Australian Catholic University and a senior partner at Elkhart Group Limited, a statistical consultancy. He earned his Ph.D. from the University of California, Los Angeles. His research focuses on using advanced quantitative methods to understand the complex interplays of psychological, social, and physiological processes in relation to psychological and physical health. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. Through consulting at Elkhart Group Limited and his former work at the UCLA Statistical Consulting Group, Joshua has supported a wide array of clients ranging from graduate students to experienced researchers and biotechnology companies. He also develops or co-develops a number of R packages including varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.

In this Book

  • Getting Started
  • Dealing with Dates, Strings, and Data Frames
  • Input and Output
  • Control Structures
  • Functional Programming
  • Probability Distributions
  • Working with Tables
  • Descriptive Statistics and Exploratory Data Analysis
  • Working with Graphics
  • Traditional Statistical Methods
  • Modern Statistical Methods
  • Analysis of Variance
  • Correlation and Regression
  • Multiple Regression
  • Logistic Regression
  • Modern Statistical Methods II
  • Data Visualization Cookbook
  • High-Performance Computing
  • Text Mining