plotpcs.r

library(ggplot2)

plotPCs <-  function(data=pcaOnIndividualQueenSamples, pcs=c(1,2), outputToScreen=TRUE) {
    print(paste("running on:", deparse(substitute(pcaOnIndividualQueenSamples))))
    library(ggplot2) 
    if (length(pcs) != 2) {
        warning('must know which PCs to plot - give me 2!')
    }
 #   if (outputToScreen) {
 #       yw$createDisplayDeviceIfNoneExists()
#    }
    
    ## determine % variance
    variance                    <- (data$sdev)^2
    percentageExplainedVariance <- 100 * round(variance/sum(variance), 5)

    ##plot    
    p <-  qplot(  x=data$x[,pcs[1]]
                , xlab=paste("pc:", pcs[1], '  % variance:', percentageExplainedVariance[ pcs[1]])
                , y=data$x[,pcs[2]]
                , ylab=paste("pc:", pcs[2], '  % variance:', percentageExplainedVariance[ pcs[2]])
                , label = data$colony
                , face = "bold"
                , size = 3
                , alpha=0.95
                , geom = 'text'
                , colour= data$category
                )
    
    p +opts(legend.position="none")  + scale_colour_brewer(palette="Set1") 
}



pca$x        <- pca$loadings  #pca$pcaResult@loadings
pca$category <- gsub(pattern = ".*shifted" ,      replacement="shiftedtoP", x = rownames(pca$x))
pca$category <- gsub(pattern = "_.*" ,      replacement="", x = pca$category)
pca$colony   <- gsub(pattern = "(P|M)_([0-9]*)_.*" ,      replacement="\\1\\2", x = rownames(pca$x))


plotPCs(data=pca, pcs=1:2) #2:3
plotPCs(data=pca, pcs=2:3) #2:3  PC2 
plotPCs(data=pca, pcs=3:4) #2:3

plotPCs(data=pca, pcs=c(2,7))   # PC7 also stands out...