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

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• 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!
• Documentation:
• Tips:
• explicit policy for packages development by Jeff Leek in his lab
• procedure for publication-quality plots
• tutorial to debug within Emacs some C/C++ code called by R

• customize the built-in heatmap in R (inspired from this):
```S <- 3  # nb of subgroups
V <- 7  # nb of observations
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)

myheatmap <- function(z, out.file="") {
par(mar=c(4,5,3,2), font=2, font.axis=2, font.lab=2, cex=1.5, lwd=2)
if (out.file != "")
pdf(out.file)
layout(mat=cbind(1, 2), width=c(7,1))  # plot +  legend
mycol <- rev(heat.colors(4))
image(x=1:NCOL(z), y=1:NROW(z), z=t(z),
xlim=0.5+c(0,NCOL(z)), ylim=0.5+c(0,NROW(z)),
xlab="", ylab="Observations sorted by cluster", main="Custom heatmap",
axes=FALSE, col=mycol)
axis(1, 1:NCOL(z), labels=paste("subgroup", 1:NCOL(z)), tick=0)
par(mar=c(0,0,0,0))
plot.new()
legend("center", legend=sprintf("%.2f", seq(from=min(z), to=max(z), length.out=5)[-1]),
fill=mycol, border=mycol, bty="n")
if (out.file != "")
dev.off()
par(def.par)
}

myheatmap(mydata.sort)
```