R in a Nutshell

Listing Packages in Local Libraries

To get the list of packages loaded by default, you can use the getOption command to check the value of the defaultPackages value:

> getOption("defaultPackages")
[1] "datasets"  "utils"     "grDevices" "graphics"  "stats"    
[6] "methods"

This command omits the base package; the base package implements many key features of the R language and is always loaded.

If you would like to see the list of currently loaded packages, you can use the .packages command (note the parentheses around the outside):

> (.packages())
[1] "stats"     "graphics"  "grDevices" "utils"     "datasets"  "methods"  
[7] "base"

To show all packages available, you can use the all.available option with the packages command:

> (.packages(all.available=TRUE))
 [1] "KernSmooth" "MASS"       "base"       "bitops"     "boot"      
 [6] "class"      "cluster"    "codetools"  "datasets"   "foreign"   
[11] "grDevices"  "graphics"   "grid"       "hexbin"     "lattice"   
[16] "maps"       "methods"    "mgcv"       "nlme"       "nnet"      
[21] "rpart"      "spatial"    "splines"    "stats"      "stats4"    
[26] "survival"   "tcltk"      "tools"      "utils"

You can also enter the library() command with no arguments and a new window will pop up showing you the set of available packages.

Included Packages

R comes with a number of different packages (see Table 4-1 for a list). 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.

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
datasets	✓	Some famous data sets
foreign		Tools for reading data from other formats, including Stata, SAS, and SPSS files
graphics	✓	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
rpart		Tools for building recursive partitioning and regression tree models
spatial		Functions for Kriging and point pattern analysis
splines		Regression spline functions and classes
stats	✓	Functions for statistics calculations and random number generation; includes many common statistical tests, probability distributions, and modeling tools
stats4		Statistics functions as S4 methods and classes
survival		Survival analysis functions
tcltk		Interface to Tcl/Tk; used to create platform-independent UI tools
tools		Tools for developing packages
utils	✓	A variety of utility functions for R, including package management, file reading and writing, and editing