fix(ci): add pre-commit to check R/ path and apply

.github/workflows/ci.yaml

@@ -14,11 +14,11 @@ env:
 
 on:
   pull_request:
-    branches: ['main', 'docker-config']
+    branches: ['main', 'ruth-notebook-for-workshop']
     paths-ignore: ['docs/**']
 
   push:
-    branches: ['main', 'docker-config']
+    branches: ['main', 'ruth-notebook-for-workshop']
     paths-ignore: ['docs/**']
 
 concurrency:

.pre-commit-config.yaml

@@ -1,4 +1,4 @@
-exclude: "R|data"
+exclude: "data"
 repos:
   - repo: https://github.com/psf/black
     rev: "23.9.1"

R/LCAT/Data_Processing_todf.R

@@ -1,12 +1,12 @@
-## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## 
+## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
 ## Data Processing UK HadsGrid CPM data from raster to data.frame
 
 
 ## 0. About
 
-# Many of the methods packages for applying bias correction to climate date take as input vector, matrix or data.frame, 
+# Many of the methods packages for applying bias correction to climate date take as input vector, matrix or data.frame,
 # rather than a spatial file or raster
-# This may not be the most effective way of analysising this data 
+# This may not be the most effective way of analysising this data
 
 
 rm(list=ls())
@@ -47,62 +47,59 @@ cores <- detectCores()
 cl <- makeCluster(cores[1]-1)
 registerDoParallel(cl)
 
-    foreach(x = x, 
+    foreach(x = x,
          .packages = c("terra", "tidyverse"),
-             .errorhandling = 'pass') %dopar% { 
+             .errorhandling = 'pass') %dopar% {
 
                f <- paste0(dd,'shapefiles/NUTS_Level_1_January_2018_FCB_in_the_United_Kingdom_2022_7279368953270783580/NUTS_Level_1_January_2018_FCB_in_the_United_Kingdom.shp')
                UK.shape <-vect(f)
                crop.area <- UK.shape[which(UK.shape$nuts118cd==x)]
-                       
+
               var <- c("rainfall", "tasmax", "tasmin", "tas")
-              
+
               hads19802010_read_crop_df_write(var = var,
                                               fp = paste0(dd,  "Processed/HadsUKgrid/resampled_2.2km/"),
                                               name1 = "HadsUK",
                                               crop=T,
                                               crop.area=crop.area,
                                               cropname=x,
                                               rd=rd)
-                      
+
                      }
-  
-  stopCluster(cl) 
+
+  stopCluster(cl)
   gc()
 
 
 ### Processing CPM
-  
+
   x <- regioncd
-  
+
 
   cores <- detectCores()
   cl <- makeCluster(cores[1]-1)
   registerDoParallel(cl)
-  
-  foreach(x = x, 
+
+  foreach(x = x,
           .packages = c("terra", "tidyverse"),
-          .errorhandling = 'pass') %dopar% { 
-            
+          .errorhandling = 'pass') %dopar% {
+
             f <- paste0(dd,'shapefiles/NUTS_Level_1_January_2018_FCB_in_the_United_Kingdom_2022_7279368953270783580/NUTS_Level_1_January_2018_FCB_in_the_United_Kingdom.shp')
             UK.shape <-vect(f)
             crop.area <- UK.shape[which(UK.shape$nuts118cd==x)]
-            
+
             runs <- c("05", "07", "08", "06")
             var <- c("tasmax", "tasmin","pr", "tas")
             rd <- paste0(dd, "Interim/CPM/Data_as_df")
-            
+
             cpm_read_crop_df_write(runs=runs, var=var, fp=paste0(dd, "Reprojected/UKCP2.2/"),
-                               year1=2060, year2=2080, #Ran sep for each year segment 
-                               name1="CPM", crop = T, 
+                               year1=2060, year2=2080, #Ran sep for each year segment
+                               name1="CPM", crop = T,
                                crop.area = crop.area, cropname = x,
                                rd=paste0(dd, "Interim/CPM/Data_as_df"))
-            
-            
+
+
           }
-  
-  stopCluster(cl) 
+
+  stopCluster(cl)
   gc()
-
-
-