Here’s a script that loops over climate NetCDF bricks in a folder and extracts the values for each layer in the brick of each file, in this case averaged over polygons in a shape file.
rm(list = ls())
library(raster)
library(rgdal)
library(ncdf4)
library(reshape2)
library(stringr)
library(data.table)
setwd("where your climate files are")
your_shapefile <- readOGR("path to your shapefile","the layer name for your shapefile")
climate <- rbindlist(lapply(list.files(pattern="nc"), function(climate_file)
{
#in my case, the weather variable is in the filename
climate_var = ifelse(grepl("pr",climate_file),"pr",ifelse(grepl("tmax",climate_file),"tmax","tmin"))
print(climate_var)
climate_variable <- brick(climate_file,varname=climate_var,stopIfNotEqualSpaced=FALSE)
#I needed to shift the x-axis to be on a -180 to 180 scale
extent(climate_variable)=c(xmin(climate_variable)-360, xmax(climate_variable)-360, ymin(climate_variable), ymax(climate_variable))
blocks <- spTransform(blocks,projection(climate_variable))
r.vals <- extract(climate_variable, blocks, fun=mean,na.rm=TRUE,df=TRUE,layer=1,nl=1680)
r.vals <- melt(r.vals, id.vars = c("ID"),variable.name = "date")
r.vals$climate <- climate_var
#depending on the filename convention of your files, get a variable for your climate model and name it "modelname"
r.vals$model <- modelname
climate_variables[[length(climate_variables)+1]] <- r.vals
}))
climate <- rbindlist(climate_variables)
At this point, you have a data frame called “climate” that has all your data long-form. You can cast this as you see appropriate depending on your needs!