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 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 youall need to accomplish 80 percent of modern data tasks. Landeras self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. Youall 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, youall construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time youare done, you wonat just know how to write R programs, youall be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES ac Exploring R, RStudio, and R packages ac Using R for math: variable types, vectors, calling functions, and more ac Exploiting data structures, including data.frames, matrices, and lists ac Creating attractive, intuitive statistical graphics ac Writing user-defined functions ac Controlling program flow with if, ifelse, and complex checks ac Improving program efficiency with group manipulations ac Combining and reshaping multiple datasets ac Manipulating strings using Ras facilities and regular expressions ac Creating normal, binomial, and Poisson probability distributions ac Programming basic statistics: mean, standard deviation, and t-tests ac Building linear, generalized linear, and nonlinear models ac Assessing the quality of models and variable selection ac Preventing overfitting, using the Elastic Net and Bayesian methods ac Analyzing univariate and multivariate time series data ac Grouping data via K-means and hierarchical clustering ac Preparing reports, slideshows, and web pages with knitr ac Building reusable R packages with devtools and Rcpp ac Getting involved with the R global communityOrganized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality youall need to accomplish 80 percent of modern data tasks.
|Title||:||R for Everyone|
|Author||:||Jared P. Lander|
|Publisher||:||Addison-Wesley Professional - 2013-12-20|