Finding adjacent polygons in R (neighbors)

I think you're looking for gTouches:

library(rgeos)library(rgdal)# using http://data.biogeo.ucdavis.edu/data/diva/adm/GHA_adm.zipghana <- readOGR("GHA_adm", "GHA_adm1")gTouches(ghana, byid=TRUE)##       0     1     2     3     4     5     6     7     8     9## 0 FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE## 1  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE## 2  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE## 3  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE## 4 FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE## 5 FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE## 6 FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE## 7 FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE## 8 FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE## 9  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

On a quick glance, it looks right:

I'm not sure which Ghana administrative district file you are using, so that was a guess and those are in polygon order, so you'll need to poke at ghana@data and map the entries to admin district names.

Please do not abuse rgeos. This is not a satisfactory answer - no district names. The original question was in any case trivial, as spdep::nb2mat() does this (with a little care with matrix row and column names):

library(spdep)library(sp)ghana <- readRDS("GHA_adm1.rds")row.names(ghana) <- as.character(ghana$NAME_1)nb <- poly2nb(ghana)mat <- nb2mat(nb, style="B")colnames(mat) <- rownames(mat)mat

Using rgeos::gTouches() can work, especially if the returnDense=FALSE argument is used (ie. don't return a matrix), but can fail if snapping is needed. spdep::poly2nb() is the preferred route, because it makes snapping possible, and because the nb S3 class is much more flexible than matrices.

It is possible to prime spdep::poly2nb() with the output of rgeos::gUnarySTRtreeQuery(), which provides lists of polygons with overlapping bounding boxes as candidate neighbours. This handles really large data sets fast.

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