Вот решение для этого
library(raster)
library(sp)
library(rgeos)
library(rgdal)
library(sf)
r<- getData('CMIP5', var='tmin', res=10, rcp=45, model='HE', year=70)
#Using Zonal statistics
poly <- shapefile("Provide_your_drive_name" e.g. "F:\\Kriging in R\\India Shape files\\2011_Dist.shp")
plot(poly)
#This will take considerable time
ex <- extract(r, poly, fun='mean', na.rm=TRUE, df=TRUE, weights = TRUE)
write.csv(cbind(poly$DISTRICT,ex),"Worldclim.csv", row.names = F)
# using centroids
nc <- st_read(dsn="Provide_your_drive_name" e.g. "F:\\Kriging in R\\India Shape files", layer="2011_Dist")
# just view the attributes & first 6 attribute values of the data
head(nc)
sp_cent <- gCentroid(as(nc, "Spatial"), byid = TRUE)
values <- extract(r,sp_cent)
write.csv(cbind(as.data.frame(nc$DISTRICT),as.data.frame(values)),"Worldclim_centroids.csv", row.names = F)