R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data and Analytics) / Jared P. Lander

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 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, 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 and hierarchical clustering

Preparing reports, slideshows, and web pages with knitr

Building reusable R packages with devtools and Rcpp

Getting involved with the R global community