Celine wrote on 11/07/2011 02:50:55 PM:
>
> Hi R user,
>
> I have two dataframe with different variables and coordinates :
> X Y sp bio3 bio5 bio6 bio13 bio14
> 1 -70.91667 -45.08333 0 47 194 -27 47 12
> 2 -86.58333 66.25000 0 16 119 -345 42 3
> 3 -62.58333 -17.91667 0 68 334 152 144 28
> 4 -68.91667 -31.25000 0 54 235 -45 25 7
> 5 55.58333 48.41667 0 23 319 -172 23 14
> 6 66.25000 37.75000 0 34 363 -18 49 0
>
> and this one :
>
> X Y LU1 LU2 LU3 LU4 LU5 LU6 LU7 LU8 LU9 LU10 LU11 LU12 LU13 LU14
> LU15 LU16 LU17 LU18
> 1 -36.5 84 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0.000000 0 0 0 0
> 2 -36.0 84 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0.000000 0 0 0 0
> 3 -35.5 84 0 0 0 0 0 0 0 0 0 0 0 0 0
> 26.085468 0 0 0 0
> 4 -35.0 84 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0.000000 0 0 0 0
> 5 -34.5 84 0 0 0 0 0 0 0 0 0 0 0 0 0
> 5.267761 0 0 0 0
> 6 -34.0 84 0 0 0 0 0 0 0 0 0 0 0 0 0
> 105.371069 0 0 0 0
>
> I wouldlike to add to my first dataframe the value of the LU variables
at
> the coordinates of the first dataframe. Of course, the coordinates are
not
> at the same resolution and are different, this is the problem.
> I wouldlike to decrease the resolution of the first one because the
second
> dataframe have a coarser resolution and obtain something like that :
>
> X Y sp bio3 bio5 bio6 bio13 bio14 LU1 LU2 LU3 LU4 ...
> 1 -70.91667 -45.08333 0 47 194 -27 47 12 0 22.08 76.9
> 2 -86.58333 66.25000 0 16 119 -345 42 3 0 22.08 76.9
> 3 -62.58333 -17.91667 0 68 334 152 144 28 0 22.08 76.9
> 4 -68.91667 -31.25000 0 54 235 -45 25 7 0 22.08 76.9
> 5 55.58333 48.41667 0 23 319 -172 23 14 0 22.08 76.9
> 6 66.25000 37.75000 0 34 363 -18 49 0 0 22.08 76.9
>
> Do someone know a function or a way to do obtain that ?
>
> Thanks in advance for the help,
> Céline
You could use 1-nearest neighbor classification to find the closest set of
coordinates in the second data frame (df2) to each row of coordinates in
the first data frame (df1). The function knn1() is in the r package
"class". For example:
library(class)
train <- df2[, c("X", "Y")]
test <- df1[, c("X", "Y")]
cl <- 1:dim(train)[1]
rowz <- knn1(train, test, cl)
data.frame(df1, df2[rowz, ])
Jean
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