tidy things

1- DataPreparationJRC.R

+library(sf)
+library(terra)
+library(ggplot2)
+library(dplyr)
+library(tidyr) 
+library(clhs) 
+library(viridis)
+library(tmap)
+
+# 1 Data preparation -----
+
+setwd("~/serena/data/sml/europe/")
+stacka <- rast(c("FR_OC.tif", "FR_pH.tif" , "FR_TXT.tif","FR_N.tif","FR_P.tif",
+                 "FR_CEC.tif","FR_BD010.tif") ) 
+
+stackb <- rast(c("FR_CLC.tif",  "FR_NUTS2.tif","FR_NUTS1.tif")) 
+stack = c(stacka,stackb)
+
+change = FALSE # set true to resample rasters
+
+if(change) {
+  # Change resolution
+  stac500a <- terra::aggregate(stacka, 5, fun=mean)
+  stac500b <- terra::aggregate(stackb, 5, fun=modal)
+  stack500 = c(stac500a,stac500b)
+  
+  terra::writeRaster(stack500,filename = "~/serena/data/sml/europe/stack500.tiff",
+                     overwrite = TRUE)
+  
+} else {
+  stack500 = rast("~/serena/data/sml/europe/stack500.tiff")
+  
+}
+
+

README.md

@@ -4,7 +4,8 @@ this project to share the statistical work on sampling exercise
 
 # code
 
-use smltest.R to run the optimisation algorithm
+use 2-SMLFilltableJRCTests.R to run different tests required.
+use 1-DataPreparationJRC.R to prepare the rasters from JRC sharepoint
 
 # Questions