User:Timothee Flutre/Notebook/Postdoc/2011/11/07

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(About R: add rpubs)
(About R: mv custom heatmap)
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** make your own R package: [https://github.com/jtleek/rpackages policy] from Jeff Leek, [https://github.com/hadley/devtools devtools] from Hadley Wickham, [http://projecttemplate.net/ ProjectTemplate]
** make your own R package: [https://github.com/jtleek/rpackages policy] from Jeff Leek, [https://github.com/hadley/devtools devtools] from Hadley Wickham, [http://projecttemplate.net/ ProjectTemplate]
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* customize the built-in heatmap in R (inspired from [http://stackoverflow.com/questions/5687891/r-how-do-i-display-clustered-matrix-heatmap-similar-color-patterns-are-grouped/5694349 this]):
 
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S <- 3  # nb of subgroups
 
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V <- 7  # nb of observations
 
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z <- matrix(c(0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,1,1,1,0,0), nrow=V, ncol=S, byrow=TRUE)
 
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myheatmap <- function(z, out.file="") {
 
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  def.par <- par(no.readonly=TRUE)
 
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  par(mar=c(4,5,3,2), font=2, font.axis=2, font.lab=2, cex=1.5, lwd=2)
 
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  if (out.file != "")
 
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    pdf(out.file)
 
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  layout(mat=cbind(1, 2), width=c(7,1))  # plot +  legend
 
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  mycol <- rev(heat.colors(4))
 
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  image(x=1:NCOL(z), y=1:NROW(z), z=t(z),
 
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        xlim=0.5+c(0,NCOL(z)), ylim=0.5+c(0,NROW(z)),
 
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        xlab="", ylab="Observations sorted by cluster", main="Custom heatmap",
 
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        axes=FALSE, col=mycol)
 
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  axis(1, 1:NCOL(z), labels=paste("subgroup", 1:NCOL(z)), tick=0)
 
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  par(mar=c(0,0,0,0))
 
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  plot.new()
 
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  legend("center", legend=sprintf("%.2f", seq(from=min(z), to=max(z), length.out=5)[-1]),
 
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          fill=mycol, border=mycol, bty="n")
 
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  if (out.file != "")
 
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    dev.off()
 
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  par(def.par)
 
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  }
 
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myheatmap(mydata.sort)
 
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Revision as of 11:24, 22 May 2014

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About R

  • Motivation: when analyzing data for any research project, it's essential to be able to quickly clean the raw data, transform them, plot intermediary results, calculate summary statistics, try various more-or-less sophisticated models, etc. This must be easily doable with small as well as large data sets, interactively or not. Several tools exist to fill exactly this need, and R is only one of them, but I especially recommend it because it is build by statisticians (this means that the implemented models are numerous and state-of-the-art). Moreover, it's open-source (and even free software), platform-independent, full of packages, with well-documented resources, etc, so give it a try!



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