rppa.proteinConc.overview <-
function(data.protein.conc, title="", subset.sample=NA)
{
require(manipulate)
manipulate({
data.protein.conc.copy <- data.protein.conc
normalizeFill <- function(data.protein.conc){
ddply(data.protein.conc, .(Fill, A, B), transform,
concentrations = concentrations / mean(concentrations, na.rm=T),
upper = upper / mean(concentrations, na.rm=T),
lower = lower / mean(concentrations, na.rm=T))
}
data.protein.conc.copy <- ddply(normalizeFill(data.protein.conc), .(Sample, Slide, A, B), summarise,
concentrations = mean(concentrations, na.rm=T),
upper = max(upper, na.rm=T),
lower = min(lower, na.rm=T))
each.A <- F
each.B <- F
specific.A.copy <- NULL
specific.B.copy <- NULL
each.fill <- T
fill.legend <- T
if(duplicate.na)
{
data.protein.conc.copy <- rppa.duplicate.nas(data.protein.conc.copy)
}
if(!normalize.to.ref.sample) reference <- NA
rppa.proteinConc.plot(data.protein.conc.copy, title, swap, horizontal.line, fill.legend, error.bars, scales.free, subset.sample, reference, slideAsFill=T, each.A, each.B, specific.A.copy, specific.B.copy, each.fill)
}, swap = checkbox(FALSE, "Swap category orientation"),
horizontal.line = checkbox(FALSE, "Draw horizontal line through 1"),
error.bars = checkbox(TRUE, "Plot error bars"),
duplicate.na = checkbox(TRUE, "Duplicate NA values"),
normalize.to.ref.sample=checkbox(FALSE, "Normalize to reference"),
reference=picker(as.list(if(!is.na(subset.sample)[1]) subset.sample else c(levels(data.protein.conc$Sample), NA))),
scales.free=picker("fixed", "free_y", "free_x", "free")
)
}
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