Robert I. Kabacoff - R in action

appendix B Customizing the startup environment

One of the first things that programmers like to do is customize their startup environment to conform to their preferred way of working. Customizing the startup environment allows you to set R options, specify a working directory, load commonly used packages, load user-written functions, set a default CRAN download site, and perform any number of housekeeping tasks.

You can customize the R environment through either a site initialization file (Rprofile.site) or a directory initialization file (.Rprofile). These are text files containing R code to be executed at startup.

At startup, R will source the file Rprofile.site from the R_HOME/etc directory, where R_HOME is an environment value. It will then look for an .Rprofile file to source in the current working directory. If R doesn’t find this file, it will look for it in the user’s home directory. You can use Sys.getenv("R_HOME"), Sys. getenv("HOME"), and getwd() to identify the location of the R_HOME, HOME, and current working directory, respectively.

You can place two special functions in these files. The .First() function is executed at the start of each R session, and the .Last() function is executed at the end of each session. An example of an Rprofile.site file is shown in listing B.1.

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appendix C Exporting data from R

In chapter 2, we reviewed a wide range of methods for importing data into R. But there are times that you’ll want to go the other way—exporting data from R—so that data can be archived or imported into external applications. In this appendix, you’ll learn how to output an R object to a delimited text file, an Excel spreadsheet, or a statistical application (such as SPSS, SAS, or Stata).

C.1 Delimited text file

You can use the write.table() function to output an R object to a delimited text file. The format is

write.table(x, outfile, sep=delimiter, quote=TRUE, na="NA")

where x is the object and outfile is the target file. For example, the statement

write.table(mydata, "mydata.txt", sep=",")

would save the dataset mydata to a comma-delimited file named mydata.txt in the current working directory. Include a path (for example, “c:/myprojects/mydata. txt”) to save the output file elsewhere. Replacing sep="," with sep="\t" would save the data in a tab-delimited file. By default, strings are enclosed in quotes ("") and missing values are written as NA.

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appendix D Creating publication-quality output

Research doesn’t end when the last statistical analysis or graph is completed. We need to include the results in a report that effectively communicates these findings to a teacher, supervisor, client, government agency, or journal editor. Although R creates state-of-the-art graphics, its text output is woefully retro—tables of monospaced text with columns lined up using spaces.

There are two common approaches to creating publication quality reports in R: Sweave and odfWeave. The Sweave package allows you to embed R code and output in LaTeX documents, in order to produce high-end typeset reports in PDF, PostScript, and DVI formats. Sweave is an elegant, precise, and highly flexible system, but it requires the author to be conversant with LaTeX coding.

In a similar fashion, the odfWeave package provides a mechanism for embedding R code and output in documents that follow the Open Documents Format (ODF). These reports can be further edited via an ODF word processor, such as OpenOffice Writer, and saved in either ODF or Microsoft Word format. The process is not as flexible as the Sweave approach, but it eliminates the need to learn LaTeX. We’ll look at each approach in turn.

D.1 High-quality typesetting with Sweave (R + LaTeX)

LaTeX is a document preparation system for high-quality typesetting (http://www. latex-project.org) that’s freely available for Windows, Mac, and Linux platforms. An author creates a text document that includes markup code for formatting the

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content. The document is then processed through a LaTeX compiler, producing a finished document in PDF, PostScript, or DVI format.

The Sweave package allows you to embed R code and output (including graphs) within the LaTeX document. This is a multistep process:

1A special document called a noweb file (typically with the extension .Rnw) is created using any text editor. The file contains the written content, LaTeX markup code, and R code chunks. Each R code chunk starts with the delimiter <<>>= and ends with the delimiter @.

2The Sweave() function processes the noweb file and generates a LaTeX file. During this step, the R code chunks are processed, and depending on options, replaced with LaTeX-formatted R code and output. This step can be accomplished from within R or from the command line.

Within R, the format is

Sweave("infile.Rnw")

By default, Sweave("example.Rnw") would input the file example.Rnw from the current working directory and output the file example.tex to the same directory. Alternatively, use can use

Sweave("infile.Rnw", syntax="SweaveSyntaxNoweb")

Specifying this syntax option can help avoid some common parsing errors, as well as conflicts with the R2HTML package.

Execution from the command line will depend on the operating system. For example, on a Linux system, this might look like $ R CMD Sweave infile.Rnw

3The LaTeX file is then run through a LaTeX compiler, creating a PDF, PostScript, or DVI file. Popular LaTeX compilers include TeX Live for Linux, MacTeX for Mac, and proTeXt for Windows.

The complete process is outlined in figure D.1.

Figure D.1 Process for generating a publication-quality report using Sweave

As indicated earlier, each chunk of R code is surrounded by <<>>= and @. You can add options to each <<>>= delimiter in order to control the processing of the corresponding R code chunk. For example

<<echo=TRUE, results=HIDE>>= summary(lm(Y~X, data=mydata))

@

would output the code, but not the results, whereas

<<echo=FALSE, fig=TRUE>>= plot(A)

@

wouldn’t print the code but would include the graph in the output. Common delimiter options are described in table D.1.

Table D.1 Common options for R code chunks

By default, Sweave will add LaTeX markup code to attractively format data frames, matrices, and vectors. Additionally, R objects can be embedded inline using a \Sexpr{} statement. Note that lattice graphs must be embedded in a print() statement to be processed properly.

The xtable() function in the xtable package can be used to format data frames and matrices more precisely. In addition, it can be used to format other R objects, including those produced by lm(), glm(), aov(), table(), ts(), and coxph(). Use method(xtable) to view a comprehensive list. When formatting R output using xtable(), be sure to include the results=tex option in the code chunk delimiter.

It’s easier to see how this all works with an example. Consider the noweb file in listing D.1. This is a reworking of the one-way ANOVA example in section 8.3. LaTeX markup code begins with a backslash (\). The exception is \Sexpr{}, which is a Sweave addition. R related code is presented in bold italics.

Listing D.1 A sample noweb file (example.nrw)

\documentclass[12pt]{article} \title{Sample Report}

After processing the noweb file through the Sweave() function in R and processing the resulting TeX file through a LaTeX compiler, the PDF document in figures D.2 and D.3 is generated.