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2013-03-05T21:20:35.980
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Here's a hack using <code>ggplot_build</code>. The idea is to first get your old/original plot: <pre><code>p <- ggplot(data = X, aes(x=C)) + geom_histogram() </code></pre> stored in <code>p</code>. Then, use <code>ggplot_build(p)$data[[1]]</code> to extract the data, specifically, the columns <code>xmin</code> and <code>xmax</code> (to get the same breaks/binwidths of histogram) and <code>count</code> column (to normalize the percentage by <code>count</code>. Here's the code: <pre><code># get old plot p <- ggplot(data = X, aes(x=C)) + geom_histogram() # get data of old plot: cols = count, xmin and xmax d <- ggplot_build(p)$data[[1]][c("count", "xmin", "xmax")] # add a id colum for ddply d$id <- seq(nrow(d)) </code></pre> How to generate data now? What I understand from your post is this. Take for example the first bar in your plot. It has a count of 2 and it extends from <code>xmin = 147</code> to <code>xmax = 156.8</code>. When we check <code>X</code> for these values: <pre><code>X[X$C >= 147 & X$C <= 156.8, ] # count = 2 as shown below # C1 C2 C # 19 91 63 154 # 75 86 70 156 </code></pre> Here, I compute <code>(91+86)/(154+156)*(count=2) = 1.141935</code> and <code>(63+70)/(154+156) * (count=2) = 0.8580645</code> as the two normalised values for each bar we'll generate. <pre><code>require(plyr) dd <- ddply(d, .(id), function(x) { t <- X[X$C >= x$xmin & X$C <= x$xmax, ] if(nrow(t) == 0) return(c(0,0)) p <- colSums(t)[1:2]/colSums(t)[3] * x$count }) # then, it just normal plotting require(reshape2) dd <- melt(dd, id.var="id") ggplot(data = dd, aes(x=id, y=value)) + geom_bar(aes(fill=variable), stat="identity", group=1) </code></pre> And this is the original plot: <img src="https://i.stack.imgur.com/d6aer.png" alt="original_ggplot2_plot"> And this is what I get: <img src="https://i.stack.imgur.com/WTS3I.png" alt="ggplot2_weird_histogram_plot"> Edit: If you also want to get the breaks proper, then, you can get the corresponding <code>x</code> coordinates from the old plot and use it here instead of <code>id</code>: <pre><code>p <- ggplot(data = X, aes(x=C)) + geom_histogram() d <- ggplot_build(p)$data[[1]][c("count", "x", "xmin", "xmax")] d$id <- seq(nrow(d)) require(plyr) dd <- ddply(d, .(id), function(x) { t <- X[X$C >= x$xmin & X$C <= x$xmax, ] if(nrow(t) == 0) return(c(x$x,0,0)) p <- c(x=x$x, colSums(t)[1:2]/colSums(t)[3] * x$count) }) require(reshape2) dd.m <- melt(dd, id.var="V1", measure.var=c("V2", "V3")) ggplot(data = dd.m, aes(x=V1, y=value)) + geom_bar(aes(fill=variable), stat="identity", group=1) </code></pre> <img src="https://i.stack.imgur.com/ap7zP.png" alt="enter image description here">
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1. POStacked histogram from already summarized counts using ggplot2
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1. COWhat is your solution doing that `require(reshape2);ggplot(melt(X,id.vars="C"),aes(x=C,fill=variable)) + geom_histogram()` does not do?
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