R For Everyone: Advanced Analytics And Graphics

Statistical Computation for Programmers Scientists Quants Excel Users and Other Professionals 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 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 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 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 packagesUse R for math variable types vectors calling functions and moreExploit data structures including dataframes matrices and listsRead many different types
of dataCreate attractive intuitive statistical graphicsWrite user-defined functionsControl program flow with if ifelse and complex checksImprove program efficiency with group manipulationsCombine and reshape multiple datasetsManipulate strings using R's facilities and regular expressionsCreate normal binomial and Poisson probability distributionsBuild linear generalized linear and nonlinear modelsProgram basic statistics mean standard deviation and t-testsTrain machine learning modelsAssess the quality of models and variable selectionPrevent overfitting and perform variable selection using the Elastic Net and Bayesian methodsAnalyze univariate and multivariate time series dataGroup data via K-means and hierarchical clusteringPrepare reports slideshows and web pages with knitrDisplay
interactive data with RMarkdown and htmlwidgetsImplement dashboards with ShinyBuild reusable R packages with devtools and Rcpp Register your product at informitcomregister for convenient access to downloads updates and corrections as they become available