Robert I. Kabacoff - R in action

If you look at Figure D.4, you’ll note that the ANOVA table isn’t attractively formatted (as it was in Sweave). Rather, the table is in the standard monospaced font produced by R. This is because odfWeave doesn’t have a formatting function for the objects

My Sample Report

Robert I. Kabacoff, Ph.D.

<<echo=false, results=hide>>= library(multcomp) library(xtable) attach(cholesterol)

@

1 Results

Cholesterol reduction was assessed in a study that randomized \Sexpr{nrow(cholesterol)} patients to one of \Sexpr{length(unique(trt))} treatments. Summary statistics are provided in Table 1.

Table 1. Descriptive Statistics for each treatment group <<echo = false, results = xml>>=

descTable <- data.frame("Treatment" = sort(unique(trt)), "N" = as.vector(table(trt)),

"Mean" = tapply(response, list(trt), mean, na.rm=TRUE), "SD" = tapply(response, list(trt), sd, na.rm=TRUE)

)

odfTable(descTable)

@

The analysis of variance is provided Table 2.

Table 2. Analysis of Variance

<<echo=false>>=

fit <- aov(response ~ trt) summary(fit)

@

and group differences are plotted in Figure 1.

<<fig=TRUE,echo=FALSE>>=

par(mar=c(5,4,6,2))

tuk <- glht(fit, linfct=mcp(trt="Tukey"))

plot(cld(tuk, level=.05),col="lightgrey",xlab="Treatment", ylab="Response") box("figure")

@

Figure1. Distribution of response times and pair-wise comparisons.

Figure D.4 Initial noweb file (example.odt) to be processed through odfWeave

My Sample Report

Robert I. Kabacoff, Ph.D.

1 Results

Cholesterol reduction was assessed in a study that randomized 50 patients to one of 5 treatments. Summary statistics are provided in Table 1.

The analysis of variance is provided Table 2.

and group differences are plotted in Figure 1.

Figure D.5 Final report in ODF format (example-out.odt). Page 2 is similar to the second page of the Sweave output in figure D.2 and is omitted to save space

returned by lm(), glm(), and so forth. To properly format these results, we’d have to pull the components out of the object in question (fit in this case), and arrange them in a matrix or data frame.

Once you have your report in ODF format, you can continue to edit it, tighten up the formatting, and save the results to an ODT, HTML, DOC, or DOCX file format. To learn more, read the odfWeave manual and vignette.

D.3 Comments

There are several advantages to the Sweave and odfWeave approaches described here. By embedding the code needed to perform the statistical analyses directly into the final report, you document exactly how the results were calculated. Six months from now, you can easily see what was done. You can also modify the statistical analyses or add new data and immediately regenerate the report with minimum effort. Additionally, you avoid the need to cut and paste and reformat the results.

Unfortunately, you gain these advantages by putting in significantly more work at the front-end. There are other disadvantages as well. In the case of LaTeX, you need to learn a typesetting language. In the case of ODF, you need to use a program like OpenOffice that may not be standard in your work environment.

For good or ill, Microsoft Word and PowerPoint are the current report and presentation standards in the business world. The packages R2wd and R2PPT can be used to dynamically create Word and PowerPoint documents with inserted R output, but they are in their formative stages of development. I’m looking forward to seeing fully developed implementations.

Table E.1 R functions and operators for matrix algebra (continued )

There are several user-contributed packages that are particularly useful for matrix algebra. The matlab package contains wrapper functions and variables used to replicate MATLAB function calls as closely as possible. These functions can help port MATLAB applications and code to R. There’s also a useful cheat sheet for converting MATLAB statements to R statements at http://mathesaurus.sourceforge.net/octave-r.html.

The Matrix package contains functions that extend R in order to support highly dense or sparse matrices. It provides efficient access to BLAS (Basic Linear Algebra Subroutines), Lapack (dense matrix), TAUCS (sparse matrix), and UMFPACK (sparse matrix) routines.

Finally, the matrixStats package provides methods for operating on the rows and columns of matrices, including functions that calculate counts, sums, products, central tendency, dispersion, and more. Each is optimized for speed and efficient memory use.

Table F.1 Contributed packages used in this book (continued )

Table F.1 Contributed packages used in this book (continued )

Table F.1 Contributed packages used in this book (continued )

Table F.1 Contributed packages used in this book (continued )