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Editorial Team
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Asked: 2026-06-15T21:45:43+00:00

I am trying to organize the labels of my ggplot scatterplot so that the

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I am trying to organize the labels of my ggplot scatterplot so that the labels don’t overlap with one another. For this purpose, I am trying to use the direct labels library but I cannot get it to work. When I tried the code:

mytable <- read.csv('http://www.fileden.com/files/2012/12/10/3375236/My%20Documents/CF1_deNovoAssembly.csv', sep=",",  header=TRUE)

mytable$Consensus.length <- log(mytable$Consensus.length)

mytable$Average.coverage <-log(mytable$Average.coverage)

mytable$Name <- do.call(rbind,strsplit(as.character(mytable$Name), " ", '['))[,3]

ggplot(mytable, aes(x=Consensus.length, y=Average.coverage, label=Name)) + geom_point() + ylab("Contig Average Coverage (log)") + xlab("Contig Consensus Length (log)") + opts(title="Contig Coverage vs Length") + geom_text(hjust=0, vjust=-0.2, size=4)
direct.label(p, "first.qp")

I got this error:

Error in direct.label.ggplot(p, "first.qp") : 
  Need colour aesthetic to infer default direct labels.

So I changed the plotting script by adding aes to the geom_point()

ggplot(mytable, aes(x=Consensus.length, y=Average.coverage, label=Name)) + geom_point(aes(colour=Average.coverage)) + ylab("Contig Average Coverage (log)") + xlab("Contig Consensus Length (log)") + opts(title="Contig Coverage vs Length") + geom_text(hjust=0, vjust=-0.2, size=4)

And now I get the following error

Error in order.labels(d) : labels are not aligned

I found this thread in which they suggest either placing the labels manually if only a few data points or not at all if too many data points. I agree with this but I will be generating this graph with many different data sets and I do need the data labels. So far this is how the graph looks
enter image description here

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1 Answer

  1. Editorial Team

    From your comments, it sounds a bit more like a clustering exercise. So, let’s go ahead and actually do so: 

    set.seed(9234970)
d <- data.frame(Name=mytable$Name, 
x=mytable$Consensus.length, 
y=mytable$Average.coverage)
d$kmeans <- as.factor(kmeans(d[-1],20)$cluster)
ggplot(d, aes(x, y, color=kmeans)) + 
geom_point() + 
theme(legend.position="bottom")

    kmeans clusters
    ggplot(d, aes(x, x, label=Name)) +
    geom_text(aes(x,y)) +
    facet_wrap(~kmeans, scales=”free”)

    Cluster Breakout

    I chose 20 clusters at random

    You could also use heirarchical clustering to see a dendogram.

    plot(hclust(dist(d[-3]))) # -3 drops kmeans column

    I’d recommend playing around with the cluster package in general as it may provide a more useful solution to your problem.

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