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Dynamic reporting with R/Sweave tures are stored in so-called dataframes, which are A in fact lists of objects that may have different data and LTEX types. Various ways of creating and assigning vectors Uwe Ziegenhagen to variables are shown in Listing 1.2. Abstract 1 a <- 1:3 # store vector 1..3 in a R is a sophisticated statistical programming language 2 b = 2:4 # store 2..4 in b available for various platforms. Since its initial devel- 3 c(a,b) # [1] 1 2 3 2 3 4 # cat a & b opment in 1992 it has become the major open source 4 # generate sequence tool for statistical data analysis. For the integration 5 seq(1,2,by=0.1) [1] 1.1 1.2 1.3 ... A with LTEX it provides tools which allow the conve- 6 # repeat 1..4 twice nient dynamic creation of reports. In this article I 7 rep(1:4,2) # [1] 1 2 3 4 1 2 3 4 will give a very brief introduction to R and show how A Listing 1.2: Generating vectors in R R integrates in the LTEX workflow. 1 Introduction to R Our final R example in Listing 1.3 shows how a simple linear model can be computed with R. The vector of The history of R dates back to 1969 when John M. independent variables x contains the numbers 1 to Chambers from Bell Labs published the outlines of 10; for the vector of dependent variables y we just S, a programming language for statistics and data multiply the x-vector with a random factor taken analysis that was first implemented in 1975 for Hon- from a normal distribution. The linear model is then eywell computers. Starting in 1992 Ross Ihaka and calculated using the lm command which presents the Robert Gentleman from the University of Auckland coefficients of the linear model. in New Zealand took up the concepts underlying S to develop R, a free implementation of the language. 1 > x<-1:10 Today R is for many statisticians the tool of choice for 2 > y=rnorm(10)*x visualization and data analysis, covering all aspects 3 > lm(y~x)
of modern computer-based statistics. The R project 4
team has more than 500 members; more than 1,000 5 Call: packages are available on CRAN, the Comprehensive 6 lm(formula = y ~ x) R Archive Network. 7 8 Coefficients: 1.1 R as a calculator 9 (Intercept) x 10 0.1079 1.0697 Since the focus of this paper lies more on the interac- tion with LAT X this is not the place for a thorough E Listing 1.3: A linear model with R introduction to R . Interested readers may have a closer look in the bibliography of this article for suit- able materials. Nevertheless I would like to point out 1.2 R graphics some of the main features of the language. Listing 1.1 R manages its graphical output (see the basic exam- shows some of the operators for basic calculations. ple code in Listing 1.4 and its output in Figure 1) through graphics devices which take the graphics ob- 1 1+2 ject data and convert them into printable or viewable 2 1*2 form. The list of available graphics devices is exten- 3 1/2 sive; there are devices for PDF, PostScript, X11, Java 4 1-2 and SVG, to mention a few. Listing 1.5 shows for 5 2^2 6 sqrt(2) example how the PDF device can be used to produce 7 sin(pi) # cos, tan high-quality PDF files. 8 trunc(-pi) # -3 9 round(pi) # 3
Listing 1.1: Basic calculations with R 1 a<- c(1:10) 2 plot(a) The basic objects to store variables in R are vectors, matrices and dataframes. Vectors and matrices may Listing 1.4: The most basic R plot contain only a single data type; complex data struc-
Dynamic reporting with R/Sweave and LATEX 190 TUGboat, Volume 31 (2010), No. 2
G 1 % Created by tikzDevice 2 \begin{tikzpicture}[x=1pt,y=1pt] G 3 \draw[color=white,opacity=0] (0,0) 4 rectangle (505.89,505.89); G 5 \begin{scope} 6 \path[clip] ( 49.20, 61.20) rectangle (480.69,456.69); G 7 \definecolor[named]{drawColor}{rgb}{0.56,0.96,0.51} 8 \definecolor[named]{fillColor}{rgb}{0.13,0.09,0.52} G 9 \definecolor[named]{drawColor}{rgb}{0.00,0.00,0.00}
a 10 \draw[color=drawColor,line cap=round,line join=round, G 11 fill opacity=0.00,] ( 65.18, 75.85) circle ( 2.25); 12 \draw[color=drawColor,line cap=round,line join=round, G 13 fill opacity=0.00,] (109.57,116.54) circle ( 2.25); 14 \end{scope} G 15 \begin{scope}
G 2 4 6 8 10 Listing 1.7: Excerpt from the generated TikZ code G
2 4 6 8 10 2 Sweave and R Index 2.1 Introduction Figure 1: Graphics generated by Listing 1.4 In the second part of the article I want to introduce the Sweave package, developed by Friedrich Leisch. 1 pdf(file = "c:/punkte.pdf",width = 6, Sweave is part of the standard R installation so it 2 height = 6, onefile = FALSE, requires no additional effort to install. 3 family = "Helvetica", The package allows to include both R and LAT X 4 title = "R Graphics Output", E code in a single file. The R code is enclosed in “noweb” 5 fonts = NULL, version = "1.4", 6 paper = "special") tags, <<>>= for the beginning, @ for the end. Noweb
7 is a free tool implementing Donald Knuth’s approach
8 a<- c(1:10) of literate programming (for more details on this 9 plot(a) topic please see the articles in Wikipedia, et al.). 10 The noweb-file is then processed within R using the 11 # switch back to screen device command Sweave("
Uwe Ziegenhagen TUGboat, Volume 31 (2010), No. 2 191
1 \documentclass{article} 1 \documentclass[a4paper]{scrartcl} 2 \usepackage{Sweave} 2 \begin{document} 3 \begin{document} 3 4 \begin{Schunk} <
February 28, 2010 19 pch.vec <- c(16,2,3)[iris$Species] 20 col.vec <- c(16,2,3)[iris$Species]
> 1 + 1 21 plot(iris$Sepal.Width,iris$Petal.Length, 22 [1] 2 col = col.vec,pch=pch.vec) 23 @ 24 \caption{Plot of iris\$Petal.Length vs. iris Figure 2: PDF file generated by Listing 2.1 \$Sepal.Width} 25 \end{figure} 26 \end{document} output of the original R source, while results=hide suppresses the output of results. A combination of Listing 2.3: Sweave code to generate Figure 3 both options may not seem to make sense, but this can be used to load data in the beginning of the 2.3 The iris example analysis, set default values for variables, etc. Since there are R packages that directly create Listing 2.3 shows a brief example for a statistical valid LATEX source, Sweave supports results=tex, analysis of the well-known iris dataset, consisting of a mode that passes the generated output through to each 50 observations for three different species of iris LATEX without tampering with its content. flowers. In the first R code chunk the data is loaded, If images are created in an R code chunk the since this step may not be relevant for the reader we option fig=true needs to be set. The default setting omit both the output and the R code. is to create both PostScript and PDF versions of each To print the number of rows and columns for plot; the settings eps=true/false and pdf=true/ this dataset we use the \Sexpr() command in the A false adjust this, respectively. Finally, the size of LTEX text before displaying a small summary of the plot can be set using the width and height the data. Afterwards we have R compute the linear parameter specifying the width and height of each model for two variables and print the results using plot in inches. Options may also be set globally by R’s xtable command, which provides output in valid A \SweaveOpts; see the Sweave manual for details. LTEX syntax. Therefore we prevent Sweave from Sweave also implements the noweb way of re- displaying the code in verbatim mode by specifying using code chunks: a certain piece of code can be results=tex. Finally, the last code chunk plots named with << name , opt=...>>=; the user may a scatterplot for the variables Petal.length and then use these chunks with << name >>. Sepal.width; please note the fig=true statement For scalar results which can be, for example, here, specifying that the result is a picture. embedded in the running text, Sweave provides the \Sexpr( R-code ) command. The only requirement 2.4 Dynamic reports for the code is that the return value must be either a The final example shows how reports with dynamic string or an object that can be converted to a string. data sources can be created easily. Let’s suppose We will use this command in the example described we need a report on the USD/EURO exchange rates next, shown in Listing 2.3. on a frequent basis. The data can be downloaded
Dynamic reporting with R/Sweave and LATEX 192 TUGboat, Volume 31 (2010), No. 2
Der Datensatz hat 5 Spalten und 150 Zeilen. 1 \documentclass{scrartcl} Min. 1st Qu. Median Mean 3rd Qu. Max. 1.000 1.600 4.350 3.758 5.100 6.900 2 \begin{document}
Estimate Std. Error t value Pr(>|t|) 3 <
iris$Petal.Length 13 data= read.csv("eurofxref-hist.csv",sep=",", header=TRUE) G G G G G G GGGG G G G GGGGGG G G G G 14 @ G G G G G G G G GGG G G G G G G G 1 2 3 4 5 6 7 15 The data contains \Sexpr{nrow(data)} rates,
2.0 2.5 3.0 3.5 4.0 the latest rate (\Sexpr{data$Date[1]}) was
iris$Sepal.Width \Sexpr{data$USD[1]} 16 \centering Figure 1: Plot von iris$Petal.Length vs. iris$Sepal.Width 17 \begin{figure}[ht] Figure 3: Document generated by Listing 2.3 18 <
data$USD Springer-Verlag, 2008. [4] John Maindonald and John Brown, Data 0.8 1.0 1.2 1.4 1.6 0 500 1000 1500 2000 2500 Analysis and Graphics Using R. Cambridge Index 2858 Datensätze von 1999−01−04 bis 2010−03−02 University Press, 2010. [5] R Core Team, An Introduction to R. Figure 4: Output of exchange rate example, generated by Listing 2.4 http://cran.r-project.org/doc/manuals/ R-intro.pdf
3 Conclusion ⋄ Uwe Ziegenhagen R provides a vast set of functions for all aspects of Lokomotivstr. 9 data analysis. Together with Sweave, a user can 50733 Cologne, Germany easily generate dynamic reports holding the results ziegenhagen (at) gmail dot com and methods leading to them in one document. With http://www.uweziegenhagen.de
Uwe Ziegenhagen