R in a Nutshell: A Desktop Quick Reference (2nd Edition) by Joseph Adler

By Joseph Adler

If you're contemplating R for statistical computing and knowledge visualization, this ebook presents a brief and useful consultant to almost every thing you are able to do with the open resource R language and software program surroundings. You'll how one can write R capabilities and use R programs that can assist you arrange, visualize, and learn info. writer Joseph Adler illustrates each one strategy with a wealth of examples from drugs, enterprise, and sports.

Updated for R 2.14 and 2.15, this moment variation contains new and elevated chapters on R functionality, the ggplot2 info visualization package deal, and parallel R computing with Hadoop.

• start fast with an R instructional and countless numbers of examples
• discover R syntax, gadgets, and different language details
• locate hundreds of thousands of user-contributed R applications on-line, together with Bioconductor
• easy methods to use R to organize information for analysis
• Visualize your facts with R's images, lattice, and ggplot2 packages
• Use R to calculate statistical fests, healthy versions, and compute chance distributions
• accelerate in depth computations via writing parallel R courses for Hadoop
• Get an entire machine connection with R

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Some of these packages (like base, graphics, grDevices, methods, and utils) implement basic features of the R language or R environment. Other packages provide commonly used statistical modeling tools (like cluster, nnet, and stats). Other packages implement sophisticated graphics (grid and lattice), contain examples (datasets), or contain other frequently used functions. In many cases, you won’t need to get any other packages. 38 | Chapter 4: R Packages Table 4-1. Packages included with R Package name Loaded by default Description base ✓ Basic functions of the R language, including arithmetic, I/ O, programming support boot Bootstrap resampling class Classification algorithms, including nearest neighbors, self-organizing maps, and learning vector quantization cluster Clustering algorithms codetools Tools for analyzing R code compiler Byte code compiler for R datasets ✓ foreign Some famous data sets Tools for reading data from other formats, including Stata, SAS, and SPSS files ✓ Functions for base graphics grDevices ✓ Device support for base and grid graphics, including system-specific functions grid Tools for building more sophisticated graphics than the base graphics KernSmooth Functions for kernel smoothing lattice An implementation of Trellis graphics for R: prettier graphics than the default graphics MASS Functions and data used in the book Modern Applied Statistics with S by Venables and Ripley; contains a lot of useful statistics functions methods ✓ Implementation of formal methods and classes introduced in S version 4 (called S4 methods and classes) mgcv Functions for generalized additive modeling and generalized additive mixed modeling nlme Linear and nonlinear mixed-effects models nnet Feed-forward neural networks and multinomial log linear models parallel Support for parallel computation, including random-number generation.

These are called generic functions. Generic functions serve two purposes. First, they make it easy to guess the right function name for an unfamiliar class. Second, generic functions make it possible to use the same code for objects of different types. Objects and Classes | 27 A Short R Tutorial > nleast$l[nleast$teams=="FLA"] [1] 77 For example, + is a generic function for adding objects. You can add numbers together with the + operator: > 17 + 6 [1] 23 You might guess that the addition operator would work similarly with other types of objects.

After I finish working on a problem, I want a record of every step that I took. ) A command-line interface makes it very easy to keep a record of everything I do and then re-create it later if I need to. When you launch R, you will see a window with the R console. 0/x86_64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. 2. Incidentally, R has quite a bit in common with LISP: both languages allow you to compute expressions on the language itself, both languages use similar internal structures to hold data, and both languages use lots of parentheses.

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