Cover art for R for Everyone
Addison-Wesley, June 2017
Softcover, 560 pages

R for Everyone Advanced Analytics and Graphics

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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. Organised 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 visualisation; 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, generalised 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

Analyse 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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