Getting R and getting started -- Programming in R -- Writing reusable functions -- Summary statistics -- Creating tables and graphs -- Discrete probability distributions -- Computing normal probabilities -- Creating confidence intervals -- Performing t tests -- One-way analysis of variance -- Advanced analysis of variance -- Correlation and regression -- Multiple regression -- Logistic regression -- Chi-square tests -- Nonparametric tests -- Using R for simulation -- The "new" statistics: resampling and bootstrapping -- Making an R package -- The R Commander package.;Beginning R: An Introduction to Statistical Programming 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. 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. Covers the freely-available R language for statistics Shows the use of R in specific uses case such as simulations, discrete probability solutions, one-way ANOVA analysis, and more Takes a hands-on and example-based approach incorporating best practices with clear explanations of the statistics being done.