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 youGÇÖll need to accomplish 80 percent of modern data tasks.
LanderGÇÖs self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. YouGÇÖll 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, youGÇÖll construct several complete models, both linear and nonlinear, and use some data mining techniques.
By the time youGÇÖre done, you wonGÇÖt just know how to write R programs, youGÇÖll be ready to tackle the statistical problems you care about most.
GÇó Exploring R, RStudio, and R packages
GÇó Using R for math: variable types, vectors, calling functions, and more
GÇó Exploiting data structures, including data.frames, matrices, and lists
GÇó Creating attractive, intuitive statistical graphics
GÇó Writing user-defined functions
GÇó Controlling program flow with if, ifelse, and complex checks
GÇó Improving program efficiency with group manipulations
GÇó Combining and reshaping multiple datasets
GÇó Manipulating strings using RGÇÖs facilities and regular expressions
GÇó Creating normal, binomial, and Poisson probability distributions
GÇó Programming basic statistics: mean, standard deviation, and t-tests
GÇó Building linear, generalized linear, and nonlinear models
GÇó Assessing the quality of models and variable selection
GÇó Preventing overfitting, using the Elastic Net and Bayesian methods
GÇó Analyzing univariate and multivariate time series data
GÇó Grouping data via K-means and hierarchical clustering
GÇó Preparing reports, slideshows, and web pages with knitr
GÇó Building reusable R packages with devtools and Rcpp
GÇó Getting involved with the R global community