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POColour Density plots in ggplot2 by cluster groups
 

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  1. POColour Density plots in ggplot2 by cluster groups
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    <p>I have been clustering some of my values and then grouping them. I then plot some density plots using <code>ggplot2</code> and overlay the clusters. An example image is below: <img src="https://i.stack.imgur.com/hNkm4.png" alt="enter image description here"></p> <p>For each group in the cluster I plot a density plot and overlay them. The colouring in the density plots corresponds to the groupings in the clustering. </p> <p>My problem is, I have split the data manually based on the groupings and put them in their own individual text table (see code below). This is very inefficient and can be come very tedious for large data sets. How can I dynamically plot the density plots in <code>ggplot2</code> without separating the clusters into their own individual text tables?</p> <p>The original input tables looks like this before it was split:</p> <pre><code>scores &lt;- read.table(textConnection(" file max min avg lowest 132 5112.0 6520.0 5728.0 5699.0 133 4720.0 6064.0 5299.0 5277.0 5 4617.0 5936.0 5185.0 5165.0 1 4384.0 5613.0 4917.0 4895.0 1010 5008.0 6291.0 5591.0 5545.0 104 4329.0 5554.0 4858.0 4838.0 105 4636.0 5905.0 5193.0 5165.0 35 4304.0 5578.0 4842.0 4831.0 36 4360.0 5580.0 4891.0 4867.0 37 4444.0 5663.0 4979.0 4952.0 31 4328.0 5559.0 4858.0 4839.0 39 4486.0 5736.0 5031.0 5006.0 32 4334.0 5558.0 4864.0 4843.0 "), header=TRUE) </code></pre> <p>The code I used to generate the plot: Please note combining the base graphics with grid is still not working correctly</p> <pre><code>library(ggplot2) library(grid) layout(matrix(c(1,2,3,1,4,5), 2, 3, byrow = TRUE)) # define function to create multi-plot setup (nrow, ncol) vp.setup &lt;- function(x,y){ grid.newpage() pushViewport(viewport(layout = grid.layout(x,y))) } # define function to easily access layout (row, col) vp.layout &lt;- function(x,y){ viewport(layout.pos.row=x, layout.pos.col=y) } vp.setup(2,3) file_vals &lt;- read.table(textConnection(" file avg_vals 133 1.5923 132 1.6351 1010 1.6532 104 1.6824 105 1.6087 39 1.8694 32 1.9934 31 1.9919 37 1.8638 36 1.9691 35 1.9802 1 1.7283 5 1.7637 "), header=TRUE) red &lt;- read.table(textConnection(" file max min avg lowest 31 4328.0 5559.0 4858.0 4839.0 32 4334.0 5558.0 4864.0 4843.0 36 4360.0 5580.0 4891.0 4867.0 35 4304.0 5578.0 4842.0 4831.0 "), header=TRUE) blue &lt;- read.table(textConnection(" file max min avg lowest 133 4720.0 6064.0 5299.0 5277.0 105 4636.0 5905.0 5193.0 5165.0 104 4329.0 5554.0 4858.0 4838.0 132 5112.0 6520.0 5728.0 5699.0 1010 5008.0 6291.0 5591.0 5545.0 "), header=TRUE) green &lt;- read.table(textConnection(" file max min avg lowest 39 4486.0 5736.0 5031.0 5006.0 37 4444.0 5663.0 4979.0 4952.0 5 4617.0 5936.0 5185.0 5165.0 1 4384.0 5613.0 4917.0 4895.0 "), header=TRUE) # Perform Cluster d &lt;- dist(file_vals$avg_vals, method = "euclidean") fit &lt;- hclust(d, method="ward") plot(fit, labels=file_vals$file) groups &lt;- cutree(fit, k=3) cols = c('red', 'blue', 'green', 'purple', 'orange', 'magenta', 'brown', 'chartreuse4','darkgray','cyan1') rect.hclust(fit, k=3, border=cols) # Desnity plots dat = rbind(data.frame(Cluster='Red', max_vals = red$max), data.frame(Cluster='Blue', max_vals = blue$max), data.frame(Cluster='Green', max_vals = green$max)) max = (ggplot(dat,aes(x=max_vals))) max = max + geom_density(aes(fill=factor(Cluster)), alpha=.3) + xlim(c(3500, 5500)) + scale_fill_manual(values=c("red",'blue',"green")) max = max + labs(fill = 'Clusters') print(max, vp=vp.layout(1,2)) dat = rbind(data.frame(Cluster='Red', min_vals = red$min), data.frame(Cluster='Blue', min_vals = blue$min), data.frame(Cluster='Green', min_vals = green$min)) min = (ggplot(dat,aes(x=min_vals))) min = min + geom_density(aes(fill=factor(Cluster)), alpha=.3) + xlim(c(5000, 7000)) + scale_fill_manual(values=c("red",'blue',"green")) min = min + labs(fill = 'Clusters') print(min, vp=vp.layout(1,3)) dat = rbind(data.frame(Cluster='Red', avg_vals = red$avg), data.frame(Cluster='Blue', avg_vals = blue$avg), data.frame(Cluster='Green', avg_vals = green$avg)) avg = (ggplot(dat,aes(x=avg_vals))) avg = avg + geom_density(aes(fill=factor(Cluster)), alpha=.3) + xlim(c(4000, 6000)) + scale_fill_manual(values=c("red",'blue',"green")) avg = avg + labs(fill = 'Clusters') print(avg, vp=vp.layout(2,2)) dat = rbind(data.frame(Cluster='Red', lowest_vals = red$lowest), data.frame(Cluster='Blue', lowest_vals = blue$lowest), data.frame(Cluster='Green', lowest_vals = green$lowest)) lowest = (ggplot(dat,aes(x=lowest_vals))) lowest = lowest + geom_density(aes(fill=factor(Cluster)), alpha=.3) + xlim(c(4000, 6000)) + scale_fill_manual(values=c("red",'blue',"green")) lowest = lowest + labs(fill = 'Clusters') print(lowest, vp=vp.layout(2,3)) </code></pre>
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