Milu,
Perhaps an approach like this would work. In the example below, I
calculate the mean GDP for each 1 degree by 1 degree.
temp$long1 <- floor(temp$longitude)
temp$lat1 <- floor(temp$latitude)
temp1 <- aggregate(GDP ~ long1 + lat1, temp, mean)
long1 lat1 GDP
1 -69 -55 0.90268640
2 -68 -55 0.09831317
3 -72 -54 0.22379000
4 -71 -54 0.14067290
5 -70 -54 0.00300380
6 -69 -54 0.00574220
Jean
On Thu, Jul 21, 2016 at 3:57 PM, Miluji Sb <miluj...@gmail.com> wrote:
> Dear all,
>
> I have the following GDP data by latitude and longitude at 0.5 degree by
> 0.5 degree.
>
> temp <- dput(head(ptsDF,10))
> structure(list(longitude = c(-68.25, -67.75, -67.25, -68.25,
> -67.75, -67.25, -71.25, -70.75, -69.25, -68.75), latitude = c(-54.75,
> -54.75, -54.75, -54.25, -54.25, -54.25, -53.75, -53.75, -53.75,
> -53.75), GDP = c(1.683046, 0.3212307, 0.0486207, 0.1223268, 0.0171909,
> 0.0062104, 0.22379, 0.1406729, 0.0030038, 0.0057422)), .Names =
> c("longitude",
> "latitude", "GDP"), row.names = c(4L, 17L, 30L, 43L, 56L, 69L,
> 82L, 95L, 108L, 121L), class = "data.frame")
>
> I would like to aggregate the data 1 degree by 1 degree. I understand that
> the first step is to convert to raster. I have tried:
>
> rasterDF <- rasterFromXYZ(temp)
> r <- aggregate(rasterDF,fact=2, fun=sum)
>
> But this does not seem to work. Could anyone help me out please? Thank you
> in advance.
>
> Sincerely,
>
> Milu
>
> [[alternative HTML version deleted]]
>
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