User:Timothee Flutre/Notebook/Postdoc/2011/11/07: Difference between revisions
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==About R== | ==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 [http://en.wikipedia.org/wiki/R_%28programming_language%29 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 [http://cran.r-project.org/sources.html open-source], platform-independent, full of [http://cran.r-project.org/web/packages/available_packages_by_name.html packages], with [http://cran.r-project.org/web/views/ well-documented] resources, etc, so give it a try! | * '''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 [http://en.wikipedia.org/wiki/R_%28programming_language%29 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 [http://cran.r-project.org/sources.html open-source] (and even [http://www.r-project.org/about.html free software]), platform-independent, full of [http://cran.r-project.org/web/packages/available_packages_by_name.html packages], with [http://cran.r-project.org/web/views/ well-documented] resources, etc, so give it a try! | ||
* '''Documentation''': | * '''Documentation''': |
Revision as of 13:50, 15 November 2013
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About R
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="") { def.par <- par(no.readonly=TRUE) 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) |