R – Finding closest neighboring point and number of neighbors within a given radius, coordinates lat-long

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Best option is to use libraries sp and rgeos, which enable you to construct spatial classes and perform geoprocessing.

library(sp)
library(rgeos)

Read the data and transform them to spatial objects:

mydata <- read.delim('d:/temp/testfile.txt', header=T)

sp.mydata <- mydata
coordinates(sp.mydata) <- ~long+lat

class(sp.mydata)
[1] "SpatialPointsDataFrame"
attr(,"package")
[1] "sp"

Now calculate pairwise distances between points

d <- gDistance(sp.mydata, byid=T)

Find second shortest distance (closest distance is of point to itself, therefore use second shortest)

min.d <- apply(d, 1, function(x) order(x, decreasing=F)[2])

Construct new data frame with desired variables

newdata <- cbind(mydata, mydata[min.d,], apply(d, 1, function(x) sort(x, decreasing=F)[2]))

colnames(newdata) <- c(colnames(mydata), 'neighbor', 'n.lat', 'n.long', 'n.area', 'n.canopy', 'n.avg.depth', 'distance')

newdata
            pond      lat      long area canopy avg.depth     neighbor    n.lat    n.long n.area n.canopy n.avg.depth
6            A10 41.95928 -72.14605 1500     66  60.61538 Borrow.Pit.3 41.95546 -72.15375      0        0    29.22222
3          AA006 41.96431 -72.12100  250      0  57.77778   Blacksmith 41.95508 -72.12380    361       77    71.31250
2     Blacksmith 41.95508 -72.12380  361     77  71.31250        AA006 41.96431 -72.12100    250        0    57.77778
5   Borrow.Pit.1 41.95601 -72.15419    0      0  41.44444 Borrow.Pit.2 41.95571 -72.15413      0        0    37.70000
4   Borrow.Pit.2 41.95571 -72.15413    0      0  37.70000 Borrow.Pit.1 41.95601 -72.15419      0        0    41.44444
5.1 Borrow.Pit.3 41.95546 -72.15375    0      0  29.22222 Borrow.Pit.2 41.95571 -72.15413      0        0    37.70000
6.1      Boulder 41.91822 -72.14978 1392     98  43.53333 Borrow.Pit.3 41.95546 -72.15375      0        0    29.22222
        distance
6   0.0085954872
3   0.0096462277
2   0.0096462277
5   0.0003059412
4   0.0003059412
5.1 0.0004548626
6.1 0.0374480316

Edit: if coordinates are in degrees and you would like to calculate distance in kilometers, use package geosphere

library(geosphere)

d <- distm(sp.mydata)

# rest is the same

This should provide better results, if the points are scattered across the globe and coordinates are in degrees

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