[R] Graphics question: How to create a changing "smudge factor" for overlapping lines?

[Paul Hiemstra]

Hi Tal,

I you use ggplot you can use the alpha command to make lines 
transparent. The nice thing is that when they overlap, the transparency 
adds up. I use this a lot to visualize outcomes from ensemble modelling 
(e.g. time series of RMSE).

A small example:

library(ggplot2)
dat = data.frame(x = rep(1:100, 100),
                               y = rep(1:100, 100),
                               grp = rep(sapply(1:100, function(x) 
sprintf("line%s", x)), each = 100))
dat$y = dat$y + rnorm(length(dat$y), 3, 3)
# Without alpha
ggplot(aes(x = x, y = y, group = grp), data = dat) + geom_line()
# With alpha
ggplot(aes(x = x, y = y, group = grp), data = dat) + geom_line(alpha = 
0.04, size = 2)

cheers,
Paul

On 06/15/2010 12:57 PM, Tal Galili wrote:
> Hello all,
>
> I am trying to create a Clustergram in R.
> (More about it here: http://www.schonlau.net/clustergram.html)
>
> And to produce a picture similar to what is seen here:
> http://www.schonlau.net/images/clustergramexample.gif
>
> I was able (more or less) to write the R code for creating the image, but
> there is one thing I can't seem to figure out, that is the
> *changing*"smudge factor" of the lines.
> I want the overlapping lines to "jitter" a tiny bit so they will give a
> sense of thickness to the line (according to how many observations are
> present in that cluster).
> My current solution is to use a constant jitter (based on "seq") on all the
> k number of clusters, but that causes glitches in the produced image (run my
> code to see).
>
> Here is a simple self reproducible code to create the image I was able to
> make:
>
>
>
> # ------------------------------------
>
> set.seed(100)
> Data<- rbind(matrix(rnorm(100, sd = 0.3), ncol = 2),
>             matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2))
> colnames(x)<- c("x", "y")
>
> # noise<- runif(100,0,.05)
> noise<- seq(0,.3, length.out = 100)
> Y<- NULL
> X<- NULL
> k.range<- 2:10
> for(k in k.range)
> {
>   cl<- kmeans(Data, k)
> y<- apply(cl$centers,1, mean)[cl$cluster] + noise
>   Y<- cbind(Y, y)
> x<- rep(k, length(y))
> X<- cbind(X, x)
>   points(y ~ x)
> }
>
> require(colorspace)
> COL<- rainbow_hcl(100)
> plot(0,0, col = "white", xlim = c(1,10), ylim = c(-.5,1.6),
>   xlab = "Number of clusters", ylab = "Clusters means", main = "(Basic)
> Clustergram")
> axis(side =1, at = k.range)
> abline(v = k.range, col = "grey")
> matlines(t(X), t(Y), pch = 19, col = COL, lty = 1, lwd = 1.5)
>
> # The next step would be to create a method for different cluster objects,
> but thats for another day...
>
>
> #--------------------------------------------
>
> Any suggestions on how to do this ?
>
> Thanks,
> Tal
>
>
>
> ----------------Contact
> Details:-------------------------------------------------------
> Contact me: Tal.Galili at gmail.com |  972-52-7275845
> Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
> www.r-statistics.com (English)
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>    


-- 
Drs. Paul Hiemstra
Department of Physical Geography
Faculty of Geosciences
University of Utrecht
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