How to attach a simple data.frame to a spatialpolygondataframe in R?

Question

Hi, I have (again) a problem with combining data frames in R. But this time, one is a spatial.polygon.data.frame(SPDF) and the other one is usual data.frame (DF). The SPDF has around 1000 rows the DF only 400. Both have a common column, QDGC

Now, I tried

oo <- merge(SPDF,DF, by="QDGC", all=T)

but this only results in a normal data.frame, not a spatial polygon data frame any more. I read somewhere else, that this does not work, but I did not understand what to do in such a case (has to do something with the ID columns, merge uses)

oooh such a hard question, I quess...

Thanks! Jens

Answer 1

merge can produce a dataframe with more rows than the originals if there's not a simple 1-1 mapping of the two dataframes. In which case, it would have to copy all the geometry and create multiple polygons, which is probably not a good thing.

If you have a dataframe which is the same number of rows as a SpatialPointsDataFrame, then you can just directly replace the @data slot.

library(sp)
example(overlay) # to get the srdf object
srdf@data
spplot(srdf)
srdf@data=data.frame(x=runif(3),xx=rep(0,3))
spplot(srdf)

if you get the number of rows wrong:

srdf@data=data.frame(x=runif(2),xx=rep(0,2))
spplot(srdf)
Error in data.frame(..., check.names = FALSE) : 
  arguments imply differing number of rows: 3, 2

Answer 2

Maybe the function joinCountryData2Map in the rworldmap package can give inspiration. (But I may be wrong, as I was last time.)

Answer 3

Let df = data frame, sp = spatial polygon object and by = name or column number of common column. You can then merge the data frame into the sp object using the following line of code

sp@data = data.frame(sp@data, df[match(sp@data[,by], df[,by]),])

Here is how the code works. The match function inside aligns the columns so that order is preserved. So when we merge it with sp@data, order is correctly preserved. A quick check to see if the code has worked is to inspect the two columns corresponding to the common column and see if they are identical (the common columns get duplicated and it is easy to remove the copy, but i keep it as it is a good check)