R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data & Analytics Series)

About this book

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 youll need to accomplish 80 percent of modern data tasks. Landers self-contained chapters start with the absolute basics offering extensive hands-on practice and sample code. Youll 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 youll construct several complete models both linear and nonlinear and use some data mining techniques. By the time youre done you wont just know how to write R programs youll 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 Rs 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