Resample and matching raster stack using loop in R

Question

I aim to combine biodiversity data with land cover information (rasters and vectors). However, I need to match the resolution, extent, CRS, and dimensions of each raster (predictor variables) with my biodiversity data (answer variables). I had succeed to do it individually but there are six rasters. Although, when I try a loop for the raster stack. I got some errors.

library(terra)
library(raster)
#Create a raster stack with land cover predictors:
CDI_stack<-raster::stack(list.files(path = dir_Proj1, pattern='.tif', full.names=T))
#Convert to cylindrical equal area projection
equalareaproj<-"+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"
crs(CDI_stack, warn=FALSE)<-equalareaproj
#Raster with standard dimension, resolution, extention and CRS
standard<-terra::subset(study_area, 2) 
#Loop for the raster stack
for(i in 1:length(CDI_stack@layers)){
  #Creating a single raster with each layer to maintain values
  CDI_layer<-terra::rast(terra::subset(CDI_stack, i)) 
  #Matching a raster extention individually
  CDI_layer<-ext(standard) 
  #Cropping it with standard raster to reduce matching error
  raster::crop(CDI_layer[i],standard) 
  #Resample resolution 
  terra::resample(CDI_layer[i], standard, method= "near", threads= T) 
  #Write the raster:
  return(writeRaster(Resampled_layer, 
                     filename=paste0("~/Land use/Chronic_Anthropogenic_Disturbance_Surface/", CDI_layer[i]),
                     format="GTiff", overwrite=TRUE))
  }

I found these errors:

Error in h(simpleError(msg, call)) : 
 error evaluating argument 'x' in method selection for function 'crop': 'this S4 class is not subsettable
Error in (function (classes, fdef, mtable)  : 
  unable to find an inherited method for function ‘crop’ for signature ‘"numeric"’

I would like to know if there's any complication to use raster stack or whether I am doing any code step wrongly. I expect to found the correction on the code or of the use of class object.

Please, I hope for your support. Thank you. G.

Answer 1

It should be easy enough to find out what is going wrong when you run the code line by line; including inside the for-loop (set i to 1 or whatever the valye is when the error occurs).

You will see that this fails:

CDI_layer <- ext(standard) 
raster::crop(CDI_layer[i],standard) 

Because CDI_layer[i] is a single number.

There are other things that are awkward. Especially, just use "terra", do not also use "raster" at the same time to avoid confusion.

Answer 2

Thank you for your advices, Mr. Hijimanns. I found too many errors. I preferred to work with a list from the raster stack than the stack, it worked better in the loop. Also, I used a vector to crop the raster, it preserved raster values (avoiding return NA).

#Create a list from the stack with land cover predictors:
CDI_list<-terra::as.list(CDI_stack)  

rm(study_area,CDI_stack)

#Create a list to store the results
results <- list()

#Loop for each SpatRaster from the list
for(i in 1:length(CDI_list)) {
  r<-rast(CDI_list[[i]]) # create a raster for each layer
  ext(r) <-ext(standard)  # redefine extension for each layer
  #Crop rasters using the vector to avoid 'NA' values
  rc <- terra::crop(r, standard_vec)
  #Resample rasters following standard parameters
  rc <- terra::resample(rc, standard, method= "bilinear", threads= T)
  #Rewrite the list layers with the result     
  results[[i]] <- rc 
}

#Check the values
results[[4]]
#Rasterize the list to save it as a data frame
resampled<-rast(results)
df<-as.data.frame(resampled)
summary(df)
#Save the data frame in the project directory
data.table::fwrite(df, "~/Land use/DATASETS/resampled.csv")