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I wrote already about changing figure options mid-chunk in reproducible research. This can be important e.g. if you are looping through a dataset to produce a graphic for each variable but the figure width or height need to depend on properties of the variables, e.g. if you are producing histograms and want the figures to be a bit wider when there are more bins.

That previous post was about knitr, but at the moment I am using the pander package more than knitr because it makes some things simpler. Changing figure options is a case in point.

Here is the output:

# Varying widths for graphs in a loop using the pander package

### Results for: mpg

Anything you type here will be inside the same paragraph as the figure and so works like a pseudocaption

### Results for: cyl

Anything you type here will be inside the same paragraph as the figure and so works like a pseudocaption

And here is the code:

Varying widths for graphs in a loop using the pander package

================

<% for (varn in names(mtcars[,1:2)) { %><%=

var=mtcars[,varn]

pandoc.p.return(“”)

pandoc.header.return(paste(“Results for: “,varn,””),3)

pandoc.p.return(“”)

fac=(100*log(length(unique(var))))#calculate some factor to ensure somewhat wider graphs for more bins

%><% evals.option(“width”,50+fac) %><%= #have to break out of the BRCODES to change the height options for the next chunk

qplot(var,geom=”bar”)+xlab(varn)%>

Anything you type here will be inside the same paragraph as the figure and so works like a pseudocaption<%

# coord_flip()

%><% } %>

Oh, and to make it all happen:

library(pander)

library(ggplot)

Pandoc.brew(convert=”html”,output=”loop”,”nameOfFileContainingTheAboveScript.R”)

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