[R-sig-Geo] Plotting Kernel results with ggplot2

[Barry Rowlingson]

I think you will have to take off the ggplot2 training wheels and do
it another way. Here's the outline:

1. Use kde2d to compute your kernel on a grid - you'll have to choose
the bandwidth and grid size, ggplot2 usually makes those decisions for
you. Something like:

k = kde2d(xy.df$x, xy.df$y,h=0.3,n=200)


 2. Convert the gridded density to contour lines with

cc = contourLines(k,levels=c(.25,.75))

 with whatever levels you want. Now I'm not sure what you mean by
"50%" and "75%" - do you mean contours at that fraction of the peak
value? Find the max of k$z if you do and work with that. The absolute
values of k$z are dependent on the cell size since it should integrate
to 1 over the area. Or do you want contours that contain 50% and 75%
of the points? That's not quite so simple.

 3. Convert the `cc` output to something ggplot can handle.
`contourLines` returns a list with one element per contour line (loop
or segment) with the level, x, and y coordinates. Suspect you need to
`cbind` all the coordinates with the level and the index to get
something like:

 index level x y
  1      0.75 0.123 0.545
 [etc for contour 1]
  2     0.5 .5345 .9123
  [etc for contour 2]

and then you can feed this to geom_path, grouped by index and coloured
by level.... There's probably a solution for this already somewhere...

Barry



On Mon, Feb 29, 2016 at 7:34 AM, Diego Pavon
<diego.pavonjordan at gmail.com> wrote:
> Hello list
>
> I am trying to do a Kernel analysis of bird ring recoveries and I would
> like to plot the kernel contour in ggplot. I have found that the command
> 'stat_density_2d()' can do this kernels. However, when I plot it, it draws
> all the kernels from 10%... 90%.  See example below:
>
> # Simulate data
> xy <- data.frame(x = runif(30), y = runif(30))
> coordinates(xy) <- ~ x + y
> plot(xy)
> class(xy)
>
> # Transform SpatialPoints into a data.frame to be used in ggplot2
> xy.df <- as.data.frame(xy)
> head(xy.df)
> str(xy.df)
>
> # Make the plot
> p <- ggplot(data=xy.df, aes(background="white"))
> p <- p + geom_point(aes(x=x, y=y),
>                     data = xy.df,
>                     size = 4)
> p <- p + xlab("X") + ylab("Y")
> p <- p + theme(text = element_text(size = 15))
> p
>
> # This makes the kernels
> p2 <- p + stat_density_2d(data = xy.df, aes(x=x, y=y), col = 'red', alpha =
> 0.6)
> p2
>
>
> However, I am only interested in drawing the lines for the kernel 50% and
> 75% and not all of them.
>
> Does anyone has an idea how to select only those lines (in my case 50 and
> 75%) to be plotted and omit the rest?
>
> Thank you very much in advance for you time and help.
>
> Best
>
> Diego
>
>
> --
> *Diego Pavón Jordán*
>
> *Finnish Museum of Natural History*
> *PO BOX 17 *
>
> *Helsinki. Finland*
>
>
>
> *0445061210https://tuhat.halvi.helsinki.fi/portal/en/persons/diego-pavon-jordan%288d5db37c-eddd-4fca-92cd-9c9956a42b4a%29.html
> <https://tuhat.halvi.helsinki.fi/portal/en/persons/diego-pavon-jordan%288d5db37c-eddd-4fca-92cd-9c9956a42b4a%29.html>http://www.linkedin.com/profile/view?id=170617924&trk=nav_responsive_tab_profile
> <http://www.linkedin.com/profile/view?id=170617924&trk=nav_responsive_tab_profile>https://helsinki.academia.edu/DiegoPavon
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>
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