diff --git a/ML/0-data_prep.R b/ML/0-data_prep.R
index 9b180a6..ad4a7d2 100644
--- a/ML/0-data_prep.R
+++ b/ML/0-data_prep.R
@@ -24,25 +24,6 @@ db <- dbConnect(
 
 #item list shows two different numbers for a few tests, second set of items do not have
 # any results that are on the same samples as TSH and Free T4
-test_list_cmp <- c(
-  50862 #Albumin
-  ,50863	#Alkaline Phosphatase
-  ,50861	#Alanine Aminotransferase (ALT)
-  ,50878	#Asparate Aminotransferase (AST)
-  ,51006	#Urea Nitrogen
-  ,50893	#Calcium, Total
-  ,50882	#Bicarbonate
-  ,50902	#Chloride
-  ,50912	#Creatinine
-  ,50931	#Glucose
-  ,50971	#Potassium
-  ,50983	#Sodium
-  ,50885	#Bilirubin, Total
-  ,50976	#Protein, Total
-  ,50993	#Thyroid Stimulating Hormone
-  ,50995	#Thyroxine (T4), Free
-)
-
 # 51301 and 51300 looks like test name may have changed
 test_list_bmp <- c(
   51006	#Urea Nitrogen
@@ -63,6 +44,24 @@ test_list_bmp <- c(
   ,51265	#Platelet Count
   )
 
+test_list_names <- c(
+  "BUN"   = "51006"
+  ,"CA"   = "50893"
+  ,"CO2"  = "50882"
+  ,"CL"   = "50902"
+  ,"CREA" = "50912"
+  ,"GLU"  = "50931"
+  ,"K"    = "50971"
+  ,"NA"   = "50983"
+  ,"TSH"  = "50993"
+  ,"FT4"  = "50995"
+  ,"RBC"  = "51279"
+  ,"WBC"  = "51300"
+  ,"HCT"  = "51221"
+  ,"HGB"  = "51222"
+  ,"PLT"  = "51265"
+)
+
 # TSH Ref Range from File 0.27 - 4.2 uIU/mL
 # Free T4 Ref Range from File 0.93 - 1.7 ng/dL
 
@@ -73,38 +72,8 @@ patients <- dplyr$tbl(db, "patients") %>%
   dplyr$select(-anchor_year, -anchor_year_group, -dod) %>%
   dplyr$collect()
 
-# first is using specimen id, usable data set is using chart time as it appears
+# usable data set is using chart time as it appears
 # LIS uses different id's for groups of tests
-#
-# ds_cmp <- dplyr$tbl(db, "labevents") %>%
-#   dplyr$filter(itemid %in% test_list_cmp) %>%
-#   dplyr$select(-charttime,-storetime) %>%
-#   tidyr$pivot_wider(
-#     id_cols = c(subject_id,specimen_id)
-#     ,names_from = itemid
-#     ,values_from = valuenum
-#     ) %>%
-#   dplyr$filter(!is.na(`50993`) & !is.na(`50995`)) %>%
-#   dplyr$filter(dplyr$across(where(is.numeric), ~!is.na(.x))) %>%
-#   dplyr$collect()
-#No longer using this, but saving incase
-# ds_cmp <- dplyr$tbl(db, "labevents") %>%
-#   dplyr$filter(itemid %in% test_list_cmp) %>%
-#   dplyr$select(-storetime) %>%
-#   tidyr$pivot_wider(
-#     id_cols = c(subject_id,charttime)
-#     ,names_from = itemid
-#     ,values_from = valuenum
-#   ) %>%
-#   dplyr$filter(!is.na(`50993`) & !is.na(`50995`)) %>%
-#   dplyr$collect()
-#
-# #this keeps failing if run as part of the above query.  Moving here to keep going
-# # keeps only rows that have no more then three NA's
-# ds_cmp <- patients %>%
-#   dplyr$left_join(ds_cmp, by = c("subject_id" = "subject_id")) %>%
-#   dplyr$filter(rowSums(is.na(.)) <= 3)
-
 # BMP and CBC Results together
 
 ds_bmp <- dplyr$tbl(db, "labevents") %>%
@@ -115,32 +84,34 @@ ds_bmp <- dplyr$tbl(db, "labevents") %>%
     ,names_from = itemid
     ,values_from = valuenum
   ) %>%
-  dplyr$filter(!is.na(`50993`) & !is.na(`50995`)) %>%
   dplyr$collect()
 
 
-ds_bmp <- ds_bmp %>%
+ds1 <- ds_bmp %>%
+  dplyr$filter(!is.na(`50993`) & !is.na(`50995`)) %>%
   dplyr$left_join(patients, by = c("subject_id" = "subject_id")) %>%
   dplyr$mutate(dplyr$across(`51300`, ~dplyr$if_else(!is.na(.),`51300`,`51301`))) %>%
   dplyr$select(-`51301`) %>%
   # dplyr$filter(dplyr$if_all(.fns = ~!is.na(.)))
-  dplyr$filter(rowSums(is.na(.)) <= 2)  #allows for 2 missing test
+  dplyr$filter(rowSums(is.na(.)) <= 3)  #allows for 3 missing test
 
+ds_final <- ds1 %>%
+  dplyr$mutate(
+    ft4_dia = dplyr$case_when(
+      `50993` > 4.2 & `50995` < 0.93 ~ "Hypo"
+      ,`50993` > 4.2 & `50995` > 0.93 ~ "Non-Hypo"
+      ,`50993` < 0.27 & `50995` > 1.7  ~ "Hyper"
+      ,`50993` < 0.27 & `50995` < 1.7  ~ "Non-Hyper"
+      ,TRUE ~ "Normal TSH"
+    )
+  ) %>%
+  dplyr$rename(!!!test_list_names) %>%
+  dplyr$relocate(gender, anchor_age)
 
 # save data ---------------------------------------------------------------
 
+ds_final %>% readr$write_rds(here("ML","data-unshared","ds_final.RDS"))
 
-ds_high_tsh <- ds_bmp %>%
-  dplyr$filter(`50993` > 4.2) %>%
-  readr$write_rds(
-    here("ML","data-unshared","ds_high_tsh.RDS")
-  )
-
-ds_low_tsh <- ds_bmp %>%
-  dplyr$filter(`50993` < 0.27) %>%
-  readr$write_rds(
-    here("ML","data-unshared","ds_low_tsh.RDS")
-  )
 
 # close database ----------------------------------------------------------