Tips for R Packages

Posted on Nov 23, 2012 in Programming

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Popular Repositories for R Packages

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CRAN
OmegaHat

install.packages("SVGAnnotation", repos = "http://www.omegahat.org/R")

Bioconductor

If the R package BiocInstaller (produced by Bioconductor to facilitate installing other packages on Bioconductor) has been installed, you can use the following code to install a package named pkgname on Bioconductor.

#load package BiocInstaller
library(BiocInstaller)
#install package 
biocLite('pkgname')

If the R package BiocInstaller has not been installed yet, you can use the following command to install it.

source("http://bioconductor.org/biocLite.R")

It will be automatically loaded after installation. Note that you do not have to install package BiocInstaller again next time when you want to install other packages on Bioconductor. All you need is just to load it and use the biocLite to install packages (see code shown before).

biocLite("limma")
biocLite("arrayQualityMetrics")
biocLite("GO.db")
install.packages('QuasiSeq')

functions for BioConductor objects, sometimes, it doesn't work, because you haven't load the package yet. This is misleading ....
Upgrade Bioconductor packages.

biocLite("Upgradable")

If you use R on a Linux server which you do not have root access, you have to create your own r library to install packages into. You can use any directory which you have read and write access as your own R library. For example, you can create a directory named .r-lib in your own home directory and to make R attach your library to the beginning of the search path automatically (so that you do not have to specify library path or to use .libPaths to attach it manually), you can put file named .Renviron in your home directory with the following content.

R_LIBS="/home/username/r-lib-PATH"

where username is your user name and r-lib-path is your personal R library. It is suggested that you create a personal R library even if you use R on your own Linux machine. So the above tips also apply if you run R on your own machine with Linux operating system.

library load a package into R workspace. If the package to be load is not in the search path, you can specify its path by the option lib.loc.
The .libPaths function gets/sets the library trees within which packages are looked for. For example, to add a library path to into the library trees, you can use .libPaths(c(.libPaths(), yourNewPath)). The library paths in the library trees are searched in order.
If you know the name of the R package that you want to install, then you can use install.packages to install the package for you. For example if you want to install R xlsx, you can use install.packages("xlsx"). You can also install a package from local disk use install.packages by specifying the option repos=FALSE or repos=NULL. For example, if file dclong.String_1.0-1.zip is a package in the working directory of R, you can install it using the following command. This is no longer required in the latest version of R.

install.packages("dclong.String_1.0-1.zip", repos=F)

To get all objects whose name do not start with a dot (Usually objects start with dots are not intended to be used by users.) in a package you can use ls(pos="package:pkgname"). If you want to see all objects in a package including these objects starting with a dot, you can use ls(pos="package:pkgname", all.names=T).
To get the the home package of a package, you can use .find.package. For example, to get the home folder of dclong.EasySave, you can use .find.package("dclong.EasySave"). Another more powerful way is to use system.file, which allows you both directories and files in a package. For example, to find the home directory of dclong.EasySave, you can use system.file(package = 'animation'). Instead of finding the home directory, you might want to know the path of folder history in dclong.EasySave. This can be done simply by using system.file('history',package='dclong.EasySave').
There are huge number of different packages in R and some of them are design for the same purpose, so it is inevitable that some functions in different packages have the same name. You should very careful when you are using these function. It is important to know what packages are load when R is started. To avoid confusion, we can always add package prefix to the functions that we want to use. For example, instead of using function dir in base package, we can use base::dir if there is another loaded package having function dir.
You can use functions in a package by its full name (i.e. pkgname::funName) even if the package is not loaded. For example, you can symbolToString in dclong.String by its full name dclong.String::symbolToString even if dclong.String is not loaded.
When you write a package, you might want to link some function in other packages. To do so, you can use

\code{\link[PkgName]{FunName}}

To load a package, you can use either library or require, and you do not have to pass the name of the package as character (i.e. you do not have to put the name of the package into two double/single quotation marks. Surely it works if you pass the name of the package as character.). The difference between library and require is that library returns all loaded packages while require return a boolean value indicate whether the loading succeed or not. If you want to unload a package pkgName from the workspace, you can use command detach(package="pkgName").
Package MASS has many useful functions such as ginv and mvrnorm which can be used to generate multivariate normal random variables.
Package Mvtnorm has functions which can generate multivariate t random variables and multivariate normal random variables.
Package LearnBayes and coda are very useful for Bayes. And there are many statistics distributions defined in this package LearnBayes.
Package xtable is useful for outputting objects in R in special format, e.g. html or latex.
tensor in package tensor can calculate tensor product of arrays.
Package Rweka supplies an R interface to Weka which is famous for machine learning.
genalg R based genetic algorithm for binary and floating point chromosomes.
Package e1071 : support vector maching
There are many useful functions in utils. If you do not know whether there is a function in R which can do your job, you can first check functions in utils. To see all functions in utils, you can use ls(package:utils).
For a package with a namespace, these objected not exported cannot be accessed directly by users. This is somehow similar to the concept of class in Java. Private methods and instances variables of class cannot be accessed by users directly. So you can use non-exported functions (similar to private/helper methods in Java) and non-exported variables (similar to private instances in Java) to help make coding easy.

Bioconductor

Bioconductor is a free, open source and open development software project for the analysis and comprehension of genomic data generated by wet lab experiments in molecular biology.
limma is a famous package among these packages in Bioconductor project. It performs linear model analysis for microarray data. But be careful that currently (version 2.8 or earlier) doesn't support mixed linear model, i.e. the covariance structure of the linear model must be \(\sigma^2 I_n\) if you want to use limma to do analysis on microarray data.
q.jabes of Nettleton's code is based on the method that Nettleton published in JABES (2006). It is optim... bh.. is relatively conservative. Benjamini H..
Important Packages and Functions in R

(1) About Optimization
Since we often use maximum likelihood method to solve problems, we have to do optimization very often. There are some functions in R which can help us:

(i) optimize/optimise{stats}

used for 1-dimensional optimization only.
a interval where the maximum/minimum is expected should be specified.

(ii) optim{stats}

can be applied to multiply dimensions.
includes many advanced algorithms.
initial values of the parameters are needed.
a hessian matrix can be returned if needed.

(iii) nlm{stats}

can be applied to multiply dimensions.
uses Newton-type algorithm, which means that smoothness is required.
initial values of the parameters are needed.

(iv) constrOptim{stats} This function is similar to the function optim except that it can handles linear constraints on parameters. Actually this function add a logarithmic barrier and calls function optim. So it has the following features:

can be applied to multiply dimensions.
can do optimization with linear constraints.
uses an adaptive barrier algorithm.
similar output as function optim.

(v) nlminb{stats} This function can do both unconstrained and constrained optimizations using PORT routines.

Comment: generally speaking, I think function optim in R is more useful for statisticians. However, for 1-dimensional optimization problem, function optimize/optimise is recommended because it is written specially for 1-dimensional problems and thus can give us more precise results. For constrained optimizations, function constrOptim and nlminb are recommended.

(2) About Linear Models

(i) lm{stats}

fits linear models including linear regression and analysis of variance.
can also fits general linear models for given transformations.
returns an object of class lm or for multiple responses of class c(mlm,lm).

(ii) nls{stats}

fits non-linear models.
returns an object of class nls.

(iii) lme{nlme}

fits linear mix-effect models.
returns an object of class lme.
can fit unequal variance model, use option {weights=varIdent(...)`

(iv) lmer{lme4}

fits linear mix-effect models.
returns an object of class mer.

(v) gls{nlme}

fits linear models using generalized least squares.
returns an object of class gls.

(vi) glm{stats}

fits generalized linear models.
returns an object of class inheriting from glm which inherits from class lm.

(vii) aov{stats}

(viii) anova{stats}

(ixiv) Other Miscellaneous Functions

Unit Test

testthat

Visualization

ggplot2
tableplot
rgl (3-D plot using OpenGL)
When you install a package, you might get a dependency error or error which says that a package does not exists (for your version of R). This typicially means that the R you installed is too old to use the package. Upgrade your R to the new version can usually solve the problem. Get clear about this problem ...
options(repos = c(CRAN = "http://cran.rstudio.com"))

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