I have a data set of people's geographic coordinates, which looks like this:
Person Latitude Longitude
1 46.0614 -23.9386
2 48.1792 63.1136
3 59.9289 66.3883
4 42.8167 58.3167
5 43.1167 63.25
I am planning on calculating geographic proximity at the dyadic level, using the geosphere package in R. In order to accomplish that, I need to create a data set that looks like this:
Person1 Person2 LatitudeP1 LongitudeP1 LatitudeP2 LongitudeP2
1 2 46.0614 -23.9386 48.1792 63.1136
1 3 46.0614 -23.9386 59.9289 66.3883
1 4 46.0614 -23.9386 42.8167 58.3167
1 5 46.0614 -23.9386 43.1167 63.25
2 3 48.1792 63.1136 59.9289 66.3883
2 4 48.1792 63.1136 42.8167 58.3167
2 5 48.1792 63.1136 43.1167 63.25
3 4 59.9289 66.3883 42.8167 58.3167
3 5 59.9289 66.3883 43.1167 63.25
4 5 42.8167 58.3167 43.1167 63.25
Thus, the resulting data has a row for each possible dyad in the data set, and includes the coordinates of both individuals in the dyad. "LatitudeP1" and "LongitudeP1" are the coordinates for "Person1" in the dyad, and "LatitudeP2" and "LongitudeP2" are the coordinates for "Person2" in the dyad. Also, it doesn't matter which ID is listed as Person1 versus Person2, since geographic distance is not a directed relationship.
Just taking the possible combinations (combn) of Person 1 thru 5, and subsetting the Lat/long from your original data:
dat <- read.table(header = TRUE, text="Person Latitude Longitude
1 46.0614 -23.9386
2 48.1792 63.1136
3 59.9289 66.3883
4 42.8167 58.3167
5 43.1167 63.25")
tmp <- t(combn(nrow(dat),2))
# [,1] [,2]
# [1,] 1 2
# [2,] 1 3
# [3,] 1 4
# [4,] 1 5
# [5,] 2 3
# [6,] 2 4
# [7,] 2 5
# [8,] 3 4
# [9,] 3 5
# [10,] 4 5
res <- cbind(tmp,
do.call('cbind', lapply(1:2, function(x)
mapply(`[`, dat[, 2:3], MoreArgs = list(i=tmp[, x])))))
colnames(res) <- c('Person1','Person2','LatitudeP1','LongitudeP1',
'LatitudeP2','LongitudeP2')
data.frame(res)
# Person1 Person2 LatitudeP1 LongitudeP1 LatitudeP2 LongitudeP2
# 1 1 2 46.0614 -23.9386 48.1792 63.1136
# 2 1 3 46.0614 -23.9386 59.9289 66.3883
# 3 1 4 46.0614 -23.9386 42.8167 58.3167
# 4 1 5 46.0614 -23.9386 43.1167 63.2500
# 5 2 3 48.1792 63.1136 59.9289 66.3883
# 6 2 4 48.1792 63.1136 42.8167 58.3167
# 7 2 5 48.1792 63.1136 43.1167 63.2500
# 8 3 4 59.9289 66.3883 42.8167 58.3167
# 9 3 5 59.9289 66.3883 43.1167 63.2500
# 10 4 5 42.8167 58.3167 43.1167 63.2500