R for Everyone: Advanced Analytics and Graphics 2/e

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. Content: 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