R for Everyone: Advanced Analytics and Graphics

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 modelling. 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.

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.

Features Updated with New Chapters on the caret package, network analysis, and Shiny

New coverage of Broke, Plotly, json libraries, dplyr, tidyr, tests, reading Excel data package, and more

Packed with hands-on practice opportunities and realistic, downloadable code examples

By an author with unsurpassed experience teaching statistical programming and modelling to novices

For every potential R user: programmers, data scientists, DBAs, marketers, quants, scientists, policymakers, and many others

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 purr

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