R for Everyone: Advanced Analytics and Graphics, 2nd Edition

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 non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution.

Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new 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, manipulation, and visualization; 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. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny.

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

• Explore R, RStudio, and R packages
• Use R for math: variable types, vectors, calling functions, and more
• Exploit data structures, including data.frames, matrices, and lists
• Read many different types of data
• Create attractive, intuitive statistical graphics
• Write user-defined functions
• Control program flow with if, ifelse, and complex checks
• Improve program efficiency with group manipulations
• Combine and reshape multiple datasets
• Manipulate strings using R’s facilities and regular expressions
• Create normal, binomial, and Poisson probability distributions
• Build linear, generalized linear, and nonlinear models
• Program basic statistics: mean, standard deviation, and t-tests
• Train machine learning models
• Assess the quality of models and variable selection
• Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods
• Analyze univariate and multivariate time series data
• Group data via K-means and hierarchical clustering
• Prepare reports, slideshows, and web pages with knitr
• Display interactive data with RMarkdown and htmlwidgets
• Implement dashboards with Shiny
• Build reusable R packages with devtools and Rcpp

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Table of Contents

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 Faster Group Manipulation With Dplyr
Chapter 13 Iterating With Purrr
Chapter 14 Data Reshaping
Chapter 15 Reshaping Data In The Tidyverse
Chapter 16 Manipulating Strings
Chapter 17 Probability Distributions
Chapter 18 Basic Statistics
Chapter 19 Linear Models
Chapter 20 Generalized Linear Models
Chapter 21 Model Diagnostics
Chapter 22 Regularization And Shrinkage
Chapter 23 Nonlinear Models
Chapter 24 Time Series And Autocorrelation
Chapter 25 Clustering
Chapter 26 Model Fitting With Caret
Chapter 27 Reproducibility And Reports With Knitr
Chapter 28 Rich Documents With Rmarkdown
Chapter 29 Interactive Dashboards With Shiny
Chapter 30 Building R Packages
Appendix A Real-Life Resources
Appendix B Glossary