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R for Everyone: Advanced Analytics and Graphics (2nd Edition) (Addison-Wesley Data & Analytics Series)

Amazon.com Price:  $41.04 (as of 05/05/2019 14:12 PST- Details)

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Description

Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals

 

Using the open source R language, you’ll be able to 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 specializes in the 20 percent of R functionality you’ll wish 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 regulate, 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. In the end 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 capable of tackle the statistical problems you care about most.

 

Coverage includes

  • Explore R, RStudio, and R packages
  • Use R for math: variable types, vectors, calling functions, and more
  • Exploit data structures, including data.frames, matrices, and lists
  • Read many several types of data
  • Create attractive, intuitive statistical graphics
  • Write user-defined functions
  • Control program waft with if, ifelse, and complex checks
  • Improve program efficiency with group manipulations
  • Combine and reshape multiple datasets
  • Manipulate strings the usage of R’s facilities and regular expressions
  • Create normal, binomial, and Poisson probability distributions
  • Build linear, generalized linear, and nonlinear models
  • Program basic statistics: mean, standard deviation, and t-tests
  • Train machine learning models
  • Assess the quality of models and variable selection
  • Prevent overfitting and perform variable selection, the usage of the Elastic Net and Bayesian methods
  • Analyze univariate and multivariate time series data
  • Group data via K-means and hierarchical clustering
  • Prepare reports, slideshows, and web pages with knitr
  • Display interactive data with RMarkdown and htmlwidgets
  • Implement dashboards with Shiny
  • Build reusable R packages with devtools and Rcpp

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