R help - Feb 2008 - shortest distance between two point pattern

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Try using dist:

ix <- 1:nrow(df.1)
m <- as.matrix(dist(rbind(df.1, df.2)))[ix, -ix]
which(min(m) == m, arr = TRUE)



On Feb 8, 2008 11:01 PM, Milton Cezar Ribeiro <milton_ruser at
yahoo.com.br> wrote:
> Hi R-experts.
>
> I am working in a R-code where I have two datasets with x and y coordinates
on each dataset.
> I intent to identify the shortest distance between this two datasets. I
wrote a short code to do that.
> But when I join the datasets to compute the distances, the merge function
run so slowly.
> I need only to identify the index of rows from each dataset related to the
shortest distance.
>
> x0<-rnorm(n=500,mean=1,sd=runif(1))
> y0<-rnorm(n=500,mean=3,sd=runif(1))
> x1<-rnorm(n=700,mean=8,sd=runif(1))
> y1<-rnorm(n=700,mean=5,sd=runif(1))
> df.0<-cbind(x0,y0)
> df.1<-cbind(x1,y1)
> plot(df.0,xlim=range(c(x0,x1)),ylim=range(c(y0,y1)))
> points(df.1,col=2)
> rm(x0,x1,y0,y1)
>
> #merge two data.frames of points
> #### IT SPEND many time
> df.merge<-merge(df.0,df.1,all=T)
> #compute distances between each pair of points
> attach(df.merge)
> df.merge$distance<-((x0-x1)^2+(y0-y1)^2)^0.5
> detach(df.merge)
> #identify the minimum distance
> df.merge.distance.min<-min(df.merge$distance)
> #select the pair of points (x0,y0,x1,y1) with shortest distance
>
df.merge.distance.min.subset<-subset(df.merge,df.merge$distance<=df.merge.distance.min)
> #trace a arrow between the points with shortest distance
>
arrows(df.merge.distance.min.subset[1,1],df.merge.distance.min.subset[1,2],df.merge.distance.min.subset[1,3],df.merge.distance.min.subset[1,4],code=0,col=3,lwd=2,lty=1)
>
> Any help are welcome
>
> Miltinho
> Brazil.
>
>
>
>  para armazenamento!
>
>        [[alternative HTML version deleted]]
>
> ______________________________________________
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.
>

Dear Miltinho,

there is also the function "matchpt" in the Biobase package (in 
Bioconductor) that seems to do what you want (in n dimensions).

It's written in C, and the implementation is simple and of complexity 
n*m. (For larger problems, there are more efficient nearest neighbor 
search algorithms, but I am not aware whether or where in R.). This one 
works well for medium sized problem as in your example.

Best wishes
  Wolfgang

------------------------------------------------------------------
Wolfgang Huber  EBI/EMBL  Cambridge UK  http://www.ebi.ac.uk/huber



Milton Cezar Ribeiro scripsit:
> Hi R-experts.
> 
> I am working in a R-code where I have two datasets with x and y coordinates
on each dataset.
> I intent to identify the shortest distance between this two datasets. I
wrote a short code to do that.
> But when I join the datasets to compute the distances, the merge function
run so slowly.
> I need only to identify the index of rows from each dataset related to the
shortest distance.
> 
> x0<-rnorm(n=500,mean=1,sd=runif(1))
> y0<-rnorm(n=500,mean=3,sd=runif(1))
> x1<-rnorm(n=700,mean=8,sd=runif(1))
> y1<-rnorm(n=700,mean=5,sd=runif(1))
> df.0<-cbind(x0,y0)
> df.1<-cbind(x1,y1)
> plot(df.0,xlim=range(c(x0,x1)),ylim=range(c(y0,y1)))
> points(df.1,col=2)
> rm(x0,x1,y0,y1)
> 
> #merge two data.frames of points
> #### IT SPEND many time
> df.merge<-merge(df.0,df.1,all=T)
> #compute distances between each pair of points
> attach(df.merge)
> df.merge$distance<-((x0-x1)^2+(y0-y1)^2)^0.5
> detach(df.merge)
> #identify the minimum distance
> df.merge.distance.min<-min(df.merge$distance)
> #select the pair of points (x0,y0,x1,y1) with shortest distance
>
df.merge.distance.min.subset<-subset(df.merge,df.merge$distance<=df.merge.distance.min)
> #trace a arrow between the points with shortest distance
>
arrows(df.merge.distance.min.subset[1,1],df.merge.distance.min.subset[1,2],df.merge.distance.min.subset[1,3],df.merge.distance.min.subset[1,4],code=0,col=3,lwd=2,lty=1)
> 
> Any help are welcome
> 
> Miltinho
> Brazil.
> 
>