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 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, manipulation, and visualization; 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. After all this youll make your code reproducible with LaTeX, RMarkdown, and Shiny.   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 Explore R, RStudio, and R packagesUse R for math: variable types, vectors, calling functions, and moreExploit data structures, including data.frames, 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 Rs 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