Includes indexes.

Getting R -- The R environment -- R packages -- Basics of R -- Advanced data structures -- Reading data into R -- Statistical graphics -- Writing R functions -- Control statements -- Loops, the Un-R way to iterate -- Group manipulation -- Faster group manipulation with dplyr -- Iterating with purrr -- Data reshaping -- Reshaping data in the Tidyverse -- Manipualting strings -- Probability distributions -- Basic statistics -- Linear models -- Generalized linear models -- Model diagnostics -- Regularization and shrinkage -- Nonlinear models -- Time series and autocorrelation -- Clustering -- Model fitting with Caret -- Reproducibility and reports with knitr -- Rich documents with RMarkdown -- Interactive dashboards with shiny -- Building R packages -- Real-life resources.

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 taken from publisher's site.