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Asked: 2026-05-26T22:17:40+00:00

Ok guys, as requested, I will add more info so that you understand why

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Ok guys, as requested, I will add more info so that you understand why a simple vector operation is not possible. It’s not easy to explain in few words but let’s see. I have a huge amount of points over a 2D space.
I divide my space in a grid with a given resolution,say, 100m. The main loop that I am not sure if it’s mandatory or not (any alternative is welcomed) is to go through EACH cell/pixel that contains at least 2 points (right now I am using the method quadratcount within the package spatstat).
Inside this loop, thus for each one of this non empty cells, I have to find and keep only a maximum of 10 Male-Female pairs that are within 3 meters from each other. The 3-meter buffer can be done using the “disc” function within spatstat. To select points falling inside a buffer you can use the method pnt.in.poly within the SDMTools package. All that because pixels have a maximum capacity that cannot be exceeded. Since in each cell there can be hundreds or thousands of points I am trying to find a smart way to use another loop/similar method to:
1)go trough each point at a time 2)create a buffer a select points with different sex 3)Save the closest Male-Female (0-1) pair in another dataframe (called new_colonies) 4)Remove those points from the dataframe so that it shrinks and I don’t have to consider them anymore 5) as soon as that new dataframe reaches 10 rows stop everything and go to the next cell (thus skipping all remaining points. Here is the code that I developed to be run within each cell (right now it takes too long):

head(df,20):

 X       Y Sex ID
2  583058.2 2882774   1  1
3  582915.6 2883378   0  2
4  582592.8 2883297   1  3
5  582793.0 2883410   1  4
6  582925.7 2883397   1  5
7  582934.2 2883277   0  6
8  582874.7 2883336   0  7
9  583135.9 2882773   1  8
10 582955.5 2883306   1  9
11 583090.2 2883331   0 10
12 582855.3 2883358   1 11
13 582908.9 2883035   1 12
14 582608.8 2883715   0 13
15 582946.7 2883488   1 14
16 582749.8 2883062   0 15
17 582906.4 2883317   0 16
18 582598.9 2883390   0 17
19 582890.2 2883413   0 18
20 582752.8 2883361   0 19
21 582953.1 2883230   1 20

Inside each cell I must run something according to what I explained above..

for(i in 1:dim(df)[1]){

new_colonies <- data.frame(ID1=0,ID2=0,X=0,Y=0) 

discbuff <- disc(radius, centre=c(df$X[i], df$Y[i])) 

#define the points and polygon
pnts = cbind(df$X[-i],df$Y[-i])
polypnts = cbind(x = discbuff$bdry[[1]]$x, y = discbuff$bdry[[1]]$y)
out = pnt.in.poly(pnts,polypnts)
out$ID <- df$ID[-i]

if (any(out$pip == 1)) {

pnt.inBuffID <- out$ID[which(out$pip == 1)] 
cond <- df$Sex[i] != df$Sex[pnt.inBuffID]

if (any(cond)){

eucdist <- sqrt((df$X[i] - df$X[pnt.inBuffID][cond])^2 + (df$Y[i] - df$Y[pnt.inBuffID][cond])^2)

IDvect <- pnt.inBuffID[cond]
new_colonies_temp <- data.frame(ID1=df$ID[i], ID2=IDvect[which(eucdist==min(eucdist))], 
                 X=(df$X[i] + df$X[pnt.inBuffID][cond][which(eucdist==min(eucdist))]) / 2, 
                 Y=(df$Y[i] + df$Y[pnt.inBuffID][cond][which(eucdist==min(eucdist))]) / 2)

new_colonies <- rbind(new_colonies,new_colonies_temp)

if (dim(new_colonies)[1] == maxdensity) break

}
}
}

new_colonies <- new_colonies[-1,]

Any help appreciated!
Thanks
Francesco

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1 Answer

  1. Editorial Team

    In your case I wouldn’t worry about deleting the points as you go, skipping is the critical thing. I also wouldn’t make up a new data.frame piece by piece like you seem to be doing. Both of those things slow you down a lot. Having a selection vector is much more efficient (perhaps part of the data.frame, that you set to FALSE beforehand).

    df$sel <- FALSE

    Now, when you go through you set df$sel to TRUE for each item you want to keep. Just skip to the next cell when you find your 10. Deleting values as you go will be time consuming and memory intensive, as will slowly growing a new data.frame. When you’re all done going through them then you can just select your data based on the selection column.

    df <- df[ df$sel, ]

    (or maybe make a copy of the data.frame at that point)

    You also might want to use the dist function to calculate a matrix of distances.

    from ?dist

    “This function computes and returns the distance matrix computed by using the specified distance measure to compute the distances between the rows of a data matrix.”

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