{ "item_title" : "R for Everyone", "item_author" : [" Jared Lander "], "item_description" : "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 packagesUsing R for math: variable types, vectors, calling functions, and moreExploiting data structures, including data.frames, matrices, and listsCreating attractive, intuitive statistical graphicsWriting user-defined functionsControlling program flow with if, ifelse, and complex checksImproving program efficiency with group manipulationsCombining and reshaping multiple datasetsManipulating strings using R's facilities and regular expressionsCreating normal, binomial, and Poisson probability distributionsProgramming basic statistics: mean, standard deviation, and t-testsBuilding linear, generalized linear, and nonlinear modelsAssessing the quality of models and variable selectionPreventing overfitting, using the Elastic Net and Bayesian methodsAnalyzing univariate and multivariate time series dataGrouping data via K-means and hierarchical clusteringPreparing reports, slideshows, and web pages with knitrBuilding reusable R packages with devtools and RcppGetting involved with the R global community", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/0/13/454/692/013454692X_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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