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Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for nonstatisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for all newcomers to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import and manipulation; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES ? Exploring R, RStudio, and R packages ? Using R for math: variable types, vectors, calling functions, and more ? Exploiting data structures, including data.frames, matrices, and lists ? Creating attractive, intuitive statistical graphics ? Writing user-defined functions ? Controlling program flow with if, ifelse, and complex checks ? Improving program efficiency with group manipulations ? Combining and reshaping multiple datasets ? Manipulating strings using R’s facilities and regular expressions ? Creating normal, binomial, and Poisson probability distributions ? Programming basic statistics: mean, standard deviation, and t-tests ? Building linear, generalized linear, and nonlinear models ? Assessing the quality of models and variable selection ? Preventing overfitting using the Elastic Net and Bayesian methods ? Analyzing univariate and multivariate time series data ? Grouping data via K-means, hierarchical clustering, and other techniques ? Preparing reports, slideshows, and web pages ? Building reusable R packages with devtools and Rcpp ? Getting involved with the R global community

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Preface 

Chapter 1: Getting R
Chapter 2: The R Environment
Chapter 3: R Packages
Chapter 4: Basics of R
Chapter 5: Advanced Data Structures
Chapter 6: Reading Data into R
Chapter 7: Statistical Graphics
Chapter 8: Writing R Functions
Chapter 9: Control Statements
Chapter 10: Loops, the Un-R way to Iterate
Chapter 11: Group Manipulation
Chapter 12: Data Reshaping
Chapter 13: Manipulating Strings
Chapter 14: Probability Distribution
Chapter 15: Basic Statistics
Chapter 16: Linear Models
Chapter 17: Generalized Linear Models
Chapter 18: Model Diagnostics
Chapter 19: Regularization and Shrinkage
Chapter 20: Nonlinear Models
Chapter 21: Time Series and Autocorrelation
Chapter 22: Clustering
Chapter 23: Reproducibility, Reports, and Slideshows with knitr
Chapter 24: Building R Packages
Appendix A: Real Life Resources 
List of Figures
List of Tables
Index 

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