Try this.
Jean
D <- structure(list(
id = structure(1:6, .Label = c("O13297", "O13329", "O13525",
"O13539", "O13541", "O13547"), class = "factor"),
X = c(44.444444, 31.272085, 6.865672, 14.176245, 73.275862,
28.991597),
Y = c(21.6122, 4.0159, 2.43884, 7.81217, 3.59012, 258.999)),
.Names = c("id", "X", "Y"), class = "data.frame",
row.names = c("1", "2", "3", "4", "5", "6"))
# define the number of colors
ncol <- 100
# define the radius of the neighborhood
distcut <- 30
pal <- colorRampPalette(c("blue", "yellow", "red"))(ncol)
# calculate the euclidean distance between all pairs of points, based on X,
Y coordinates
Ddist <- with(D, as.matrix(dist(cbind(X, Y), diag=TRUE, upper=TRUE)))
# count up the number of neighbors within distcut distance of each point
D$C <- apply(Ddist<distcut, 2, sum)
# use this count to define the levels (which will be then used to color
points in the plot
D$Clevels <- with(D,
cut(C, breaks=seq(min(C), max(C), length.out=ncol+1),
labels=FALSE, include.lowest=TRUE))
# plot the data
with(D, plot(X, Y, col=pal[Clevels], log="y", pch=16))
On Tue, Jun 2, 2015 at 5:37 AM, Benjamin Dubreuil <
benjamin.dubre...@weizmann.ac.il> wrote:
> Hello everyone,
>
> I have a data frame D with 4 columns id,X,Y,C.
> I want to plot a simple scatter plot of D$X vs. D$Y and using D$C values
> as a color. (id is just a text string not used for the plot)
>
> But actually, I don't want to use the raw values of D$C, I would prefer to
> calculate the average values of D$C according to the density of points in a
> fixed neighborhood.
> In other words, I would like to smooth the colors according to the density
> of points.
>
> I am looking for any function,package that could solve this.
> So far, I've been looking at library MASS and the function kde2d which can
> calculate the density of points in 2 directions, but I don't see how I
> could then use this information to recalculate my D$C values.
>
> Here is a piece of the matrix :
> > head(D)
> id X Y C
> 1 O13297 44.444444 21.61220 -0.136651639
> 2 O13329 31.272085 4.01590 -0.117016949
> 3 O13525 6.865672 2.43884 -0.161173913
> 4 O13539 14.176245 7.81217 -0.075756757
> 5 O13541 73.275862 3.59012 -0.006988235
> 6 O13547 28.991597 258.99900 -0.013985507
>
> > dim(D)
> [1] 3616 4
>
> > apply(D[,-1],2,range)
> X Y C
> [1,] 0.3378378 0.0003 -0.7382222
> [2,] 100.0000000 24556.4000 0.5582500
> (Y is not linear, so I use log='y' in the plot function)
>
> I used a palette of 100 colors ranging from Blue to Yellow to red.
> >pal = colorRampPalette(c("blue","yellow","red"))(100)
>
> To make D$C values correspond to a color, I used a cut with the following
> breaks (101 breaks from -1.2 to 1.2):
> > BREAKS
> [1] -1.2000 -0.8000 -0.4000 -0.3600 -0.3200 -0.2800 -0.2400 -0.2000
> -0.1925
> [10] -0.1850 -0.1775 -0.1700 -0.1625 -0.1550 -0.1475 -0.1400 -0.1368
> -0.1336
> [19] -0.1304 -0.1272 -0.1240 -0.1208 -0.1176 -0.1144 -0.1112 -0.1080
> -0.1048
> [28] -0.1016 -0.0984 -0.0952 -0.0920 -0.0888 -0.0856 -0.0824 -0.0792
> -0.0760
> [37] -0.0728 -0.0696 -0.0664 -0.0632 -0.0600 -0.0568 -0.0536 -0.0504
> -0.0472
> [46] -0.0440 -0.0408 -0.0376 -0.0344 -0.0312 -0.0280 -0.0248 -0.0216
> -0.0184
> [55] -0.0152 -0.0120 -0.0088 -0.0056 -0.0024 0.0008 0.0040 0.0072
> 0.0104
> [64] 0.0136 0.0168 0.0200 0.0232 0.0264 0.0296 0.0328 0.0360
> 0.0392
> [73] 0.0424 0.0456 0.0488 0.0520 0.0552 0.0584 0.0616 0.0648
> 0.0680
> [82] 0.0712 0.0744 0.0776 0.0808 0.0840 0.0872 0.0904 0.0936
> 0.0968
> [91] 0.1000 0.1250 0.1500 0.1750 0.2000 0.2250 0.2500 0.4875
> 0.7250
> [100] 0.9625 1.2000
> > C.levels = as.numeric(cut(D$C,breaks=BREAKS))
> >length(C.levels)
> [1] 3616
>
> C.levels ranges from 2 to 98 and then to plot the colors I used
> pal[C.levels].
> > plot( x=D$x, y=D$Y, col=pal[ C.levels ],log='y')
>
>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
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> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
[[alternative HTML version deleted]]
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