reproducible research - r and latexjornsten/rjrepresearch.pdf · rebecka j ornsten reproducible...

OutlineIntroduction

R + LATEX= R-SweaveConclusions

Reproducible Research - R and LATEX

Rebecka Jornsten

Mathematical StatisticsChalmers University of Technology

University of Gothenburg

October 18, 2011

Rebecka Jornsten Reproducible Research - R and LATEX

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1 Introduction

2 R + LATEX= R-SweaveR-Sweave, the basicsGrabbing numbers into the textGraphics and TablescacheSweaveProducing R code

3 Conclusions

OutlineIntroduction

Piled Higher and Deeper by Jorge Cham www.phdcomics.com

title: "Research Diagram/Research Reality" - originally published 1/7/2008

Piled Higher and Deeper http://www.phdcomics.com/comics/archive_print.php?comicid=961

1 of 1 10/13/2011 1:43 PM

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What goes into writing a research papers?

1 Data handling

2 Data filtering

3 Data analysis

4 Generating results

5 Report writing

1 excel, file-merging, cut-and-paste,...

2 excel, cut-and-paste, R, ...

3 R, SPSS, matlab, excel,...

4 hard-copy, graphics, tables

5 word, LATEX, manual input

Months later... reviews come back and we are asked to submit amajor revision within X days...

Oh-oh!

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1 Data handling

2 Data filtering

3 Data analysis

5 Report writing

Oh-oh!

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1 Data handling

2 Data filtering

3 Data analysis

5 Report writing

Oh-oh!

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1 Data handling

2 Data filtering

3 Data analysis

5 Report writing

Oh-oh!

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1 Data handling

2 Data filtering

3 Data analysis

5 Report writing

Oh-oh!

OutlineIntroduction

1 Data handling

2 Data filtering

3 Data analysis

5 Report writing

Oh-oh!

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1 Data handling

2 Data filtering

3 Data analysis

5 Report writing

Oh-oh!

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What goes into writing a research report?

Some other scenarios

Find a bug in the code - want to rerun parts of the analysis

Additional data suddenly available, or only a subset of datato be used

A member of the lab left for another job and you need tobe able to continue/reproduce his/her work

A new member joins the lab - how can she/he most quicklystart working on a lab project?

When a project involves many steps, it is often very difficult toreproduce results exactly. Too many manual steps/human errorelements AND perhaps conflicting versions of codes, lack ofcomplete analysis protocol,...

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R-Sweave, the basicsGrabbing numbers into the textGraphics and TablescacheSweaveProducing R code

R + LATEX= R-Sweave

Sweave is a package in R - available with base installer

Allows you to run the analysis and generate LATEXcode atthe same time

Tables and Figures are generated in the report directly - nomanual input required

If you know some R and some LATEX, there are templatesfor generating both dynamically updated slides and articles

Truly reproducible research!

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R + LATEX= R-Sweave

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R + LATEX= R-Sweave

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R + LATEX= R-Sweave

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R + LATEX= R-Sweave

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R-Sweave

All editing is done is a .Rnw file which include both R andLATEXcode.

\documentclass[a4paper]{article}\title{A Simple Example}\author{Rebecka J\"ornsten}\begin{document}\maketitleHere is a simple example:Here is a simple example:<<>>=mydata <‐ read.table('datafile.dat')set.seed(5) ##reproducibilityi d t l ( (1 di ( d t )[1]) 25)indextouse <‐ sample(seq(1,dim(mydata)[1]),25)mydata.sub <‐ mydata[indextouse,] ##25 random genescormat <‐ cor(t(mydata.sub),use="complete") ##correlation matrix diag(cormat)<‐0 g( )print(apply(cormat,1,max)) ##top pairwise correlations@\end{document}Rebecka Jornsten Reproducible Research - R and LATEX

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R-Sweave

To generate your report, you issue this command at the RpromptSweave("Example1.Rnw")

which generates the fileExample1.tex.You process this file usingpdflatex Example1.tex

I use MiKTeX and WinEdt to do this all the editing andprocessing in one place...

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R-Sweave

which generates the fileExample1.tex.

You process this file usingpdflatex Example1.tex

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R-Sweave

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R-Sweave

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And the corresponding (cropped) output looks like...

A Simple Example

Rebecka Jornsten

October 13, 2011

Here is a simple example:

> mydata <- read.table("datafile.dat")

> set.seed(5)

> indextouse <- sample(seq(1, dim(mydata)[1]), 25)

> mydata.sub <- mydata[indextouse, ]

> cormat <- cor(t(mydata.sub), use = "complete")

> diag(cormat) <- 0

> print(apply(cormat, 1, max))

93 318 424 132 49 322 242 370

0.8476402 0.7263073 0.9952792 0.8918629 0.8918629 0.7694564 0.7545874 0.7753499

437 51 125 223 144 253 119 91

0.9952792 0.8024887 0.9369671 0.6034389 0.9035897 0.6587848 0.9369671 0.7452532

174 397 248 375 396 320 94 100

0.8873040 0.6373209 0.7694564 0.8330653 0.6190950 0.5021774 0.9035897 0.8030855

0.8476402

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Code chunks and Latex chunks

Code chunks are delimited by<<options>>=

options include echo, results, fig, cache.

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Code chunks

You can label your code chunks. This comes in handy whenyou’re bug hunting.Example:<<firstchunk,echo=FALSE>>=

mean(nodata)

Here I try to calculate the mean of a nonexisting data set. Theerror looks like thisError: chunk 1 (label=firstchunk)

Error in mean(nodata) : object 'nodata' not found

Execution halted

So.... we know to look in the chunk firstchunk for this error.

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Options

echo *default echo=TRUE means that the Rcommands will appear in the report

results *default is results=verbatim which meansthat the R return is included in the report. results=tex isuseful for table summaries as latex table code is generateddirectly for the report

fig *if the code chunk generates figures fig=TRUE

will save this figure output as .eps and .pdf

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Options

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Options

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Simple numbers can be plugged into the text with the \Sexpr

command.Example

<<echo=FALSE>>=

out <- lm(y ~ x + x2 + x3)

summary(out)

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lm(formula = y ~ x + x2 + x3)

Residuals:

Min 1Q Median 3Q Max

-2.15905 -0.55358 0.02413 0.67416 1.90415

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 1.8657 0.5223 3.572 0.011751 *

x 2.1900 0.3819 5.734 0.001222 **

x2 3.0942 0.4260 7.263 0.000346 ***

x3 -1.3803 0.7206 -1.916 0.103897

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.414 on 6 degrees of freedom

Multiple R-squared: 0.9328, Adjusted R-squared: 0.8992

F-statistic: 27.75 on 3 and 6 DF, p-value: 0.0006474

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I can extract the R summary output with the \Sexpr command:Example:The coefficient of x2 in the model above is β2 = 3.0942.

The actual command in the .Rnw file looks like this:$\beta_2 = \Sexpr{round(out3$coef[3], 4)}$

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Including figures

You can generate one figure per chunk of code

If fig=T and include=T a includegraphics() latexcommand is generated.

I prefer to putSweaveOptsprefix.string=FigsForRJ/plot in thedocument header...

...making R-sweave dump the figures into this directoryduring the run.

I prefer create a figure environment after the chunk to putfigures in, but there are many ways of doing this.

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Including figures

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Including figures

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Including figures

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Including figures

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Including figures

Like this...<<label=fig1plot,fig=TRUE,include=FALSE>>=

x<-rnorm(50)

y<-5*x+rnorm(50)

plot(x, y)

\begin{figure}

\begin{center}

\includegraphics[height=5cm,width=5cm]{FigsForRJ/plot-

fig1plot}

\caption{Scatter plot}

\label{fig:scatter}

\end{center}

\end{figure}

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Including figures

> x <- rnorm(10)

> y <- (-2) * x + rnorm(10)

> plot(x, y)

−1.5 −1.0 −0.5 0.0 0.5 1.0

Figure: Scatter plot

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Including tables

The R package xtable() makes it easy to generate latex codefor tables with the results updated automatically when yousweave your files.Here’s an example with a regression summary:

> library(xtable)

> x <- rnorm(50)

> y = 5 * x + rnorm(50)

> mymodel <- lm(y ~ x)

> xtable(summary(mymodel), caption = "Summary Table", label = "table:one")

Estimate Std. Error t value Pr(>|t|)(Intercept) 0.0013 0.1506 0.01 0.9933

x 5.0878 0.1689 30.12 0.0000

Table: Summary Table

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Including tables

The actual code in the .Rnw file was<<regsummary,results=tex>>=

library(xtable)

x<-rnorm(50); y=5*x+rnorm(50)

mymodel<-lm(y~x)

xtable(summary(mymodel),caption = "Summary Ta-

ble", label = "table:one")

Note, you can easily include captions and labels for laterreferencing to this table (in article mode).

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Including tables

You can easily opt to just see the table results, not the codeused to generate it, by using echo=FALSE in the code chunk.

<<regsummary,results=tex,echo=FALSE>>=

library(xtable)

x<-rnorm(50); y=5*x+rnorm(50)

mymodel<-lm(y~x)

xtable(summary(mymodel),caption = "Summary Ta-

ble", label = "table:two")

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Including tables

Estimate Std. Error t value Pr(>|t|)(Intercept) 0.0268 0.1272 0.21 0.8342

x 4.8546 0.1185 40.97 0.0000

Table: Summary Table

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Including tables

Another example<<tableex,results=tex,echo=F>>=

library(xtable)

x <- rbinom(100,1,.5); x<-

data.frame(matrix(table(x),1,2))

dimnames(x) <- list(c('Outcome'), c('Tails', 'Heads'))

xtable(x, dig-

its=c(0, 3, 3),caption="Coin tosses",label="table:three")

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Including tables

Another example

Tails Heads

Outcome 46 54

Table: Coin tosses

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Using cacheSweave for time consuming steps

If your project includes time consuming calculations, youdon’t really want to have to repeat those just to update alater part of the document.

You can use the cacheSweave package to deal with thesescenarios.

By including the option cache=TRUE in a chunk, you aretelling R-sweave to only run this chunk the first time. Forsubsequent sweaves of the document, the cached results areused.

Reality check: make sure that you run the whole reportwith no cached results before final submission!

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cacheSweave

Here’s an example:<<cachetry,echo=FALSE, cache=TRUE>>=

library(cacheSweave)

xc <- rnorm(10)

@ <<cacheout>>=

print(mean(xc))

> print(mean(xc))

[1] 0.2274117

On subsequent runs of sweave, mean of xc =0.2274 will appearunaltered in the document since the random number generationonly took place the first run.

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cacheSweave

Caution:

Include only computations in cached chunks, no figures oroutput you want to see.

If you change something in this chunk remember to runSweave not cacheSweave once

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cacheSweave

Caution:

Include only computations in cached chunks, no figures oroutput you want to see.

If you change something in this chunk remember to runSweave not cacheSweave once

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Stangle()

Another bonus: by running the command Stangle instead ofSweave you produce the R stand-alone code, neatly packaged inseparated chunks.

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Stangle()

###################################################

### chunk number 1:

###################################################

#line 8 "Example1.Rnw"

mydata <- read.table('datafile.dat')

set.seed(5)

indextouse <- sample(seq(1,dim(mydata)[1]),25)

mydata.sub <- mydata[indextouse,]

cormat <- cor(t(mydata.sub),use="complete")

diag(cormat)<-0

print(apply(cormat,1,max))

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R + LATEX= Dynamic report writing

If you know R and latex - easy to move to R-sweave

Keep track of relevant results, output, codes in one place

Generate figure folders, R scripts and manuscript files inone go!

Reproducible research - reduce the ”Ooops! factor

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Great online resources

Reproducible Research - Blogs and Resources

reproducibleresearch.net

CRAN - ReproducibleResearch

reproducibleresearch.org

Scientists for reproducible research

Sweave tutorials, demos and templates

The Sweave home page

Sweave templates

Nicola Sartori’s Sweave tutorial

OutlineIntroduction

Great online resources

Reproducible Research - Blogs and Resources

reproducibleresearch.net

CRAN - ReproducibleResearch

reproducibleresearch.org

Scientists for reproducible research

Sweave tutorials, demos and templates

The Sweave home page

Sweave templates

Nicola Sartori’s Sweave tutorial

reproducible research - r and latexjornsten/rjrepresearch.pdf · rebecka j ornsten reproducible...

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