diff --git a/dev/harmonize.R b/dev/harmonize.R
index d5419fa..626546d 100644
--- a/dev/harmonize.R
+++ b/dev/harmonize.R
@@ -17,28 +17,101 @@ librarian::shelf(devtools, tidyverse)
 # Create data objects to harmonize
 df1 <- data.frame("x" = c(1:3),
                   "y" = letters[1:3])
-df2 <- data.frame("NUMBERS" = c(4:6),
-                  "LETTERS" = letters[4:6])
+df2 <- data.frame("LETTERS" = letters[4:6],
+                  "NUMBERS" = c(4:6))
 
 # Export both of these for later re-use
 write.csv(df1, file = file.path("dev", "test_df1.csv"), na = "", row.names = F)
 write.csv(df2, file = file.path("dev", "test_df2.csv"), na = "", row.names = F)
 
 # Create data key
-key <- data.frame("source" = c("test_df1.csv", "test_df1.csv", "test_df2.csv", "test_df2.csv"),
+key <- data.frame("source" = c("test_df1.csv", "test_df1.csv", 
+                               "test_df2.csv", "test_df2.csv"),
                   "raw_name" = c("x", "y", "NUMBERS", "LETTERS"),
                   "harmony_name" = c("numbers", "letters", "numbers", "letters"))
 
-
 ## ----------------------------------- ##
-# Script Variant ----
+          # Script Variant ----
 ## ----------------------------------- ##
+# Data key harmonization _in script format_
+
+# Make an empty list
+file_list <- list()
+
+# Loop across files named in the data key
+for(file in unique(key$source)){
+  ## file <- "test_df1.csv"
+  
+  # Prepare the data key
+  key_sub <- key %>% 
+    # Subset to just this file
+    dplyr::filter(source == file) %>% 
+    # Drop any instances where the harmonized name is absent (i.e., the raw column is unwanted)
+    dplyr::filter(is.na(harmony_name) != TRUE & nchar(harmony_name) != 0)
+  
+  # Read in file
+  df_v1 <- read.csv(file = file.path("dev", file))
+  
+  # Prepare the data for harmonization via data key
+  df_v2 <- df_v1 %>% 
+    # Add a row number column to preserve original rows
+    ## (name is bizarre to avoid overwriting extant column name)
+    dplyr::mutate(xxxx_row_num = 1:nrow(.),
+                  source = file) %>% 
+    # Make all columns characters
+    dplyr::mutate(dplyr::across(.cols = dplyr::everything(),
+                                .fns = as.character)) %>% 
+    # Pivot the data into 'ultimate' long format
+    tidyr::pivot_longer(cols = -xxxx_row_num:-source,
+                        names_to = "raw_name",
+                        values_to = "values")
+  
+  # Identify any columns in the data key but apparently not in the data key
+  missing_cols <- setdiff(x = unique(key_sub$raw_name),
+                          y = unique(df_v2$raw_name))
+  
+  # Warn the user if any are found
+  if(length(missing_cols) != 0){
+    message("Following columns in ", file, " _not_ found in data key")
+    print(missing_cols) }
+  
+  # Drop the missing cols object for the next iteration of the loop
+  missing_cols <- NULL
+  
+  # Perform actual harmonization
+  df_v3 <- df_v2 %>% 
+    # Attach the data key to the data
+    dplyr::left_join(y = key_sub, by = c("source", "raw_name")) %>% 
+    # Drop any columns without a standardized name
+    dplyr::filter(is.na(harmony_name) != TRUE & nchar(harmony_name) != 0) %>% 
+    # Trash the old column names
+    dplyr::select(-raw_name) %>% 
+    # Pivot back to original format
+    tidyr::pivot_wider(names_from = harmony_name, values_from = values) %>% 
+    # Trash row number column created in loop
+    dplyr::select(-xxxx_row_num)
+  
+  # Add to list
+  file_list[[file]] <- df_v3 }
 
+# Unlist the list
+harmonized_df <- purrr::list_rbind(x = file_list)
+
+# Check that out
+harmonized_df
 
 
 ## ----------------------------------- ##
 # Function Variant ----
 ## ----------------------------------- ##
+# Data key harmonization _in function format_
+
+
+
+
+
+
+
 
 # Clear environment / collect garbage
 rm(list = ls()); gc()