R has traditionally been seen as difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. This book avoids that error, aiming the material at users new to statistical programming and modeling. Author and professional data scientist Jared P. Lander focuses on the 20 percent of R functionality needed to accomplish 80 percent of modern data tasks.

<ASIN:013454692X>

The self-contained chapters start with the absolute basics, with hands-on practice and sample code. The book guides you through navigating and using the R environment as well as covering basic program control, data import, manipulation, and visualization. It also has walk throughs on several essential tests. The latter part of the book looks at constructing complete models, both linear and nonlinear, with some data mining techniques, and working with LaTeX, RMarkdown, and Shiny.

Author: Jared P. Lander
Publisher: Addison Wesley
Date: June 2017
Pages: 560
ISBN: 978-0134546926
Print: 013454692X
Kindle: B071X9KT1D
Audience: would-be R programmers
Level: introductory
Category: Other Languages

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 different types of data
• Create attractive, intuitive statistical graphics
• Write user-defined functions
• Control program flow with if, ifelse, and complex checks
• Improve program efficiency with group manipulations
• Combine and reshape multiple datasets
• Manipulate strings using 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, using 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

For recommended titles on Data Analysis see Reading Your Way Into Big Data in our Programmer's Bookshelf section.

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