R by Example

Now in its second edition, R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data.

The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, it is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data, and this book is intended to be a useful resource for learning how to implement these procedures in R.

The new edition includes expanded coverage of ggplot2 graphics, as well as new chapters on importing data and multivariate data methods.

Introduces the R system, their capabilities required to perform basic numerical and graphical summaries of data Introduces using R for simulation including Monte Carlo experiments and Bayesian computation Features a new chapter on data frames, as well as new coverage on data mining, Rstudio, knitr, and dplyr

Autorentext

Maria Rizzo is professor of statistics at Bowling Green State University. Her recent book publications include Statistical Computing with R, 2e (2019) and Energy Statistics (forthcoming).

Jim Albert is professor of mathematics and statistics at Bowling Green State University. His recent book publications include Analyzing Baseball Data with R, 2e (with Max Marchi and Benjamin S. Baumer, 2018), Visualizing Baseball (2017), and Bayesian Computation with R (Springer 2009).

Inhalt

Introduction.- Quantitative Data.- Categorical Data.- Exploratory Data Analysis.- Presentation Graphics.- Importing Data.- Basic Inference Models.- Regression.- Analysis of Variance I.- Analysis of Variance II.- Randomization Tests.- Multivariate Data.- Simulation Experiments.- Bayesian Modeling.- Monte Carlo Methods.