diff --git a/ID_column_creation.Rmd b/ID_column_creation.Rmd
new file mode 100644
index 00000000..1bf4e007
--- /dev/null
+++ b/ID_column_creation.Rmd
@@ -0,0 +1,25 @@
+---
+title: "Id_column_creation"
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+
+## Id column creation
+
+cchsflow dev currently contains updated variables and variable details. These both have the new rows needed for id creation allowing for the same function call
+
+
+
+```{r}
+library(cchsflow)
+library(bllflow)
+
+
+rec_data <- rec_with_table(cchs2001_p, variables = variables, variable_details = variable_details)
+
+rec_data$id_year
+```
diff --git a/R/bll-flow-constructor-utility.R b/R/bll-flow-constructor-utility.R
index e729f776..45a1a7f8 100644
--- a/R/bll-flow-constructor-utility.R
+++ b/R/bll-flow-constructor-utility.R
@@ -89,7 +89,8 @@ bllflow_config_rec_data <- function(bllflow_object, config_env_name = NULL) {
       base::get(data_name),
       variables = bllflow_object$variables,
       variable_details = bllflow_object$variable_details,
-      database_name = data_name)
+      database_name = data_name,
+      attach_data_name = TRUE, id_role_name = list(var_name = "row_ID", feeder_vars="ADM_RNO"))
     assign(data_name, tmp_rec_data)
     save(list = data_name,
          file = file.path(config$data_dir,
@@ -121,6 +122,12 @@ bllflow_config_combine_data <- function(bllflow_object, config_env_name = NULL)
     load(file.path(config$data_dir, paste0(data_name, "_recoded", ".RData")))
     tmp_mod_data <- base::get(data_name)
     tmp_mod_data[["data_name"]] <- data_name
+    # Create unique row id 
+    if(config$unique_id){
+      id_cols <- select_vars_by_role("ID", bllflow_object$variables)
+      
+      print("potato")
+    }
     if (is.null(tmp_working_data)) {
       tmp_working_data <- tmp_mod_data
     } else {
diff --git a/R/default_step_parsing.R b/R/default_step_parsing.R
index f1ebd609..a980f12c 100644
--- a/R/default_step_parsing.R
+++ b/R/default_step_parsing.R
@@ -221,10 +221,8 @@ create_recipe <-
              variables) {
     # Check variable roles for the recipe creation
     # TODO bllflow support make this add to overall recipe
-    outcome_variable <-
-      as.character(variables[grepl("outcome,", 
-                                   variables[[pkg.globals$argument.Role]]), 
-                             pkg.globals$argument.Variables])
+    outcome_variable <- select_vars_by_role("outcome", variables)
+    
     # TODO add a function for creating a proper formula
     recipe_formula <- paste(outcome_variable, "~ .")
 
diff --git a/R/label-utils.R b/R/label-utils.R
index 2a2d6204..d4851d40 100644
--- a/R/label-utils.R
+++ b/R/label-utils.R
@@ -252,8 +252,11 @@ label_data <- function(label_list, data_to_label) {
           as.numeric(levels(data_to_label[[variable_name]])
           [data_to_label[[variable_name]]])
       } else {
+        tmp <- as.numeric(data_to_label[[variable_name]])
+        if(sum(is.na(tmp))!= length(tmp)){
         data_to_label[[variable_name]] <-
           as.numeric(data_to_label[[variable_name]])
+        }
       }
     }
     sjlabelled::set_label(data_to_label[[variable_name]]) <-
diff --git a/R/module-instruction-parsing.R b/R/module-instruction-parsing.R
deleted file mode 100644
index a7bbd114..00000000
--- a/R/module-instruction-parsing.R
+++ /dev/null
@@ -1,174 +0,0 @@
-#' Verify data sequence match
-#'
-#' This functions verifies that the data is from the previous step in
-#' the module sequence
-#'
-#' @param module_sequence_number the sequence number being checked
-#' @param data the data to check
-verify_data_and_sequence_match <-
-  function(module_sequence_number, data) {
-    if (module_sequence_number[[1]] == 1 &&
-      attr(data, pkg.globals$bllFlowContent.Sequence) != 0) {
-      stop(
-        "Working data was passed where sequence is at step 1.
-        Make sure to pass the starting data.
-        Aborting operation!"
-      )
-    } else if (attr(data, pkg.globals$bllFlowContent.Sequence) == 0) {
-      if (module_sequence_number[[1]] != 1) {
-        stop(
-          paste(
-            "Non-working data was passed to sequence greater then step 1. 
-            Please make sure that you are passing working data that is the result of the
-            module sequence before",
-            module_sequence_number,
-            "
-            Aborting operation!"
-          )
-        )
-      }
-    } else if (attr(data,pkg.globals$bllFlowContent.Sequence) + 1 !=
-               module_sequence_number[[1]]) {
-      stop(
-        paste(
-          "The working data passed is not from the previous module please
-          verify that the data passed is from module",
-          module_sequence_number - 1,
-          "
-            Aborting operation!"
-        )
-      )
-    }
-  }
-#' Run Module
-#'
-#' Runs modules on x using the module_sequence_numbers. When not using bllflow
-#' additional params must be passed.
-#' Modules must be ran in correct order ex: module 3 can not be ran until
-#' 1 and 2 were ran in that order
-#'
-#' @param x bllflow object or variables that contain variables alongside
-#' variable specific module functions
-#' @param ... Used for generic function consistency
-#'
-#' @return when bllflow object is used a bllflow object is returned with latest
-#' module data stored in working data and previous module
-#' data stored in previous_module_data
-#' @export
-run_module <- function(x, ...) {
-  UseMethod("run_module", x)
-}
-
-#' Bllflow variant of run module
-#'
-#' Uses bllflow object instead of passing many arguments all of them are
-#' retrieved from the bllflow object
-#'
-#' @param x bllflow object containing module instruction and data to run it on
-#' @param module_sequence_number a number specifying the module to run
-#' or a numeric range ex: 1 OR 1:3
-#' @param ... Used for generic function consistency
-#'
-#' @export
-run_module.BLLFlow <- function(x, module_sequence_number, ...) {
-  bll_model <- x
-  # pkg.globals$bllFlowContent.PreviousData
-
-  processed_data <-
-    run_module.default(
-      x = bll_model$variables,
-      modules = bll_model$modules,
-      data = bll_model$working_data,
-      module_sequence_number = module_sequence_number,
-      variable_details = bll_model$variable_details
-    )
-  processed_data[[1]] <- as.data.frame(processed_data[[1]])
-  processed_data[[1]] <- set_data_labels(processed_data[[1]],
-                                         bll_model$variable_details,
-                                         bll_model$variables)
-  bll_model[[pkg.globals$bllFlowContent.PreviousData]] <- processed_data[[2]]
-  bll_model[[pkg.globals$bllFlowContent.WorkingData]] <- processed_data[[1]]
-  return(bll_model)
-}
-#' Non bllflow run module option
-#'
-#' This is mainly an internal function however for those that dont wish to use
-#' bllflow object this is an alternative
-#'
-#' @param x variables data.frame containing variable specific module
-#' instructions
-#' @param modules data.frame containing module instructions
-#' @param data data.frame to apply the module transformations onto
-#' @param module_sequence_number a number specifying the module to run or
-#' a numeric range ex: 1 OR 1:3 or an allowed string: "all"
-#' @param variable_details = NULL optional param can be used
-#' to attach variable category labels
-#' @param ... Used for generic function consistency
-#'
-#' @importFrom recipes bake
-#' @importFrom recipes prep
-#' @importFrom recipes tidy
-#' @export
-run_module.default <-
-  function(x,
-             modules,
-             data,
-             module_sequence_number,
-             variable_details = NULL, ...) {
-    variables <- x
-    # Standardize module_sequence_number
-    if (module_sequence_number[[1]] == "all") {
-      module_order <- modules[, pkg.globals$Modules.DefaultOrder]
-      # Create module_sequence_number out of all default modules
-      module_sequence_number <-
-        min(module_order, na.rm = TRUE):max(module_order, na.rm = TRUE)
-    } else if (!is.numeric(module_sequence_number)) {
-      stop(
-        "Invalid module_sequence_number passed. Please make sure
-        its either the word \"all\" or numeric.
-        Aborting operation!",
-        call. = FALSE
-      )
-    }
-
-    verify_data_and_sequence_match(module_sequence_number, data)
-
-    processed_data <- data
-    previous_data <- NULL
-    # Find type of module and execute the right call
-    for (sequence_number in module_sequence_number) {
-      previous_data <- processed_data
-      type_of_module <-
-        modules[modules[[pkg.globals$Modules.DefaultOrder]] == sequence_number,
-                pkg.globals$Modules.OperationsType]
-      if (type_of_module == pkg.globals$ModuleTypes.DefaultStep) {
-        processed_data <- parse_default_step(
-          processed_data,
-          sequence_number,
-          modules,
-          variables,
-          variable_details
-        )
-      } else if (type_of_module == pkg.globals$ModuleTypes.FormulaStep) {
-        processed_data <- parse_formula_step(
-          processed_data,
-          sequence_number,
-          modules,
-          variables,
-          variable_details
-        )
-      } else if (type_of_module == pkg.globals$ModuleTypes.Function) {
-        processed_data <- parse_function(
-          processed_data,
-          sequence_number,
-          modules,
-          variables,
-          variable_details
-        )
-      }
-      attr(processed_data, pkg.globals$bllFlowContent.Sequence) <-
-        sequence_number
-    }
-
-    return(list(processed_data, previous_data))
-  }
diff --git a/R/module-parsing.R b/R/module-parsing.R
new file mode 100644
index 00000000..46d61887
--- /dev/null
+++ b/R/module-parsing.R
@@ -0,0 +1,323 @@
+#' Run Module
+#'
+#' Runs modules on x using the module_sequence_numbers. When not using bllflow
+#' additional params must be passed.
+#' Modules must be ran in correct order ex: module 3 can not be ran until
+#' 1 and 2 were ran in that order
+#'
+#' @param x bllflow object or variables that contain variables alongside
+#' variable specific module functions
+#' @param ... Used for generic function consistency
+#'
+#' @return when bllflow object is used a bllflow object is returned with latest
+#' module data stored in working data and previous module
+#' data stored in previous_module_data
+#' @export
+run_module <- function(x, ...) {
+  UseMethod("run_module", x)
+}
+
+#' Bllflow variant of run module
+#'
+#' Uses bllflow object instead of passing many arguments all of them are
+#' retrieved from the bllflow object
+#'
+#' @param x bllflow object containing module instruction and data to run it on
+#' @param module_sequence_number a number specifying the module to run
+#' or a numeric range ex: 1 OR 1:3
+#' @param ... Used for generic function consistency
+#'
+#' @export
+run_module.BLLFlow <- function(x, module_sequence_number, ...) {
+  bll_model <- x
+  # pkg.globals$bllFlowContent.PreviousData
+  
+  processed_data <-
+    run_module.default(
+      x = bll_model$variables,
+      modules = bll_model$modules,
+      data = bll_model$working_data,
+      module_sequence_number = module_sequence_number,
+      variable_details = bll_model$variable_details
+    )
+  processed_data[[1]] <- as.data.frame(processed_data[[1]])
+  processed_data[[1]] <- set_data_labels(processed_data[[1]],
+                                         bll_model$variable_details,
+                                         bll_model$variables)
+  # Append changed data to data @RUSTY
+  bll_model[[pkg.globals$bllFlowContent.PreviousData]] <-
+    processed_data[[2]]
+  bll_model[[pkg.globals$bllFlowContent.WorkingData]] <-
+    processed_data[[1]]
+  return(bll_model)
+}
+#' Non bllflow run module option
+#'
+#' This is mainly an internal function however for those that dont wish to use
+#' bllflow object this is an alternative
+#'
+#' @param x variables data.frame containing variable specific module
+#' instructions
+#' @param modules data.frame containing module instructions
+#' @param data data.frame to apply the module transformations onto
+#' @param module_sequence_number a number specifying the module to run or
+#' a numeric range ex: 1 OR 1:3 or an allowed string: "all"
+#' @param variable_details = NULL optional param can be used
+#' to attach variable category labels
+#' @param ... Used for generic function consistency
+#'
+#' @importFrom recipes bake
+#' @importFrom recipes prep
+#' @importFrom recipes tidy
+#' @export
+run_module.default <-
+  function(x,
+           modules,
+           data,
+           module_sequence_number,
+           variable_details = NULL,
+           ...) {
+    variables <- x
+    # Standardize module_sequence_number
+    if (module_sequence_number[[1]] == "all") {
+      module_order <- modules[, pkg.globals$Modules.DefaultOrder]
+      # Create module_sequence_number out of all default modules
+      module_sequence_number <-
+        min(module_order, na.rm = TRUE):max(module_order, na.rm = TRUE)
+    } else if (!is.numeric(module_sequence_number)) {
+      stop(
+        "Invalid module_sequence_number passed. Please make sure
+        its either the word \"all\" or numeric.
+        Aborting operation!",
+        call. = FALSE
+      )
+    }
+    
+    verify_data_and_sequence_match(module_sequence_number, data)
+    
+    processed_data <- data
+    previous_data <- NULL
+    # Find type of module and execute the right call
+    for (sequence_number in module_sequence_number) {
+      # Select data subset based on what is included in the step @RUSTY
+      previous_data <- processed_data
+      processed_data <-
+        module_data_subset(data = processed_data,
+                           variables = variables,
+                           sequence_number = sequence_number)
+      var_rows <- processed_data[[2]]
+      processed_data <- processed_data[[1]]
+      
+      processed_data <-
+        parse_module(var_rows, sequence_number, processed_data, modules)
+      
+      attr(processed_data, pkg.globals$bllFlowContent.Sequence) <-
+        sequence_number
+    }
+    
+    return(list(processed_data, previous_data))
+  }
+
+module_data_subset <- function(data, variables, sequence_number) {
+  # vector containing vars that match sequence_number
+  unique_module_ID <-
+    unique(variables[, pkg.globals$columnNames.Operations]) #@RUSTY merge with select_vars_by_role
+  valid_ID_patern <- c()
+  for (ID_patern in unique_module_ID) {
+    # Split by commas to avoid partial matches being false positives
+    ID_list <- strsplit(ID_patern, ",")[[1]]
+    for (ID in ID_list) {
+      if (ID %in% sequence_number) {
+        valid_ID_patern <- append(valid_ID_patern, ID_patern)
+      }
+    }
+  }
+  valid_rows <-
+    variables[variables[[pkg.globals$columnNames.Operations]] == valid_ID_patern,]
+  valid_vars <-
+    as.character(valid_rows[[pkg.globals$MSW.Variables.Columns.Variable]])
+  ret_data <- data[, valid_vars]
+  if(!is.data.frame(ret_data)){
+    ret_data <- as.data.frame(ret_data)
+    colnames(ret_data) <- valid_vars
+  }
+  
+  return(list(ret_data, valid_rows))
+}
+
+parse_module <- function(variables, module_ID, data, modules) {
+  # Isolate individual operations
+  operations_to_run <-
+    as.character(modules[modules[[pkg.globals$Modules.ModuleID]] == module_ID, pkg.globals$WorkingData.ModuleOperations])
+  operations_list <- strsplit(operations_to_run, "],")[[1]]
+  running_data <- data
+  func_list <- c()
+  # Step 1: Parse out function requirements
+  for (single_function in operations_list) {
+    # Seperate package name from function name
+    # @RUSTY add a potential check for the package being loaded/ having that function
+    single_parsed_func <- list()
+    pkg_name <- strsplit(single_function, "::")[[1]]
+    single_function <- pkg_name[[2]]
+    single_parsed_func$pkg_name <- pkg_name[[1]]
+    
+    func_name <- strsplit(single_function, "\\[")[[1]]
+    single_function <- func_name[[2]]
+    single_parsed_func$func_name <- func_name[[1]]
+    
+    vars_used <- list()
+    
+    func_args <- strsplit(single_function, ",")[[1]]
+    single_parsed_func$args <- c()
+    for (single_arg in func_args) {
+      # Catch "formula" argument
+      if (grepl("role\\(", single_arg)) {
+        roles_list <-
+          stringr::str_extract_all(single_arg, "role\\(\\s*(.*?)\\s*\\)")
+        for (role in roles_list) {
+          real_role_name <-
+            stringr::str_extract_all(role, "\\([^()]+\\)")[[1]]
+          real_role_name <-
+            substring(real_role_name, 2, nchar(real_role_name) - 1)
+          vars <- select_vars_by_role(real_role_name, variables)
+          vars_used[[real_role_name]] <- vars
+          single_arg <-
+            gsub(paste0("role", "\\(", real_role_name, "\\)"),
+                 vars,
+                 single_arg)
+        }
+      }
+      if (grepl("formula\\(", single_arg)) {
+        single_arg <- gsub("formula\\(|\\)", "", single_arg)
+      } else{
+        tmp_arg <- as.list(strsplit(single_arg, "=")[[1]])
+        tmp_arg[[1]] <- trimws(tmp_arg[[1]])
+        tmp_arg[[2]] <- gsub("\\[|\\]", "", tmp_arg[[2]])
+        single_parsed_func$params[[tmp_arg[[1]]]] <- tmp_arg[[2]]
+        single_arg <- ""
+      }
+      single_arg <- gsub("\\[|\\]", "", single_arg)
+      single_arg <- trimws(single_arg)
+      single_parsed_func$args <-
+        append(single_parsed_func$args, single_arg)
+    }
+    single_parsed_func$all_vars <- vars_used
+    
+    func_list <- append(func_list, list(single_parsed_func))
+  }
+  
+  # Step 2: Create function calls
+  recipy_flag <- FALSE
+  # Setting scope outside loop
+  recipe_object <- NULL
+  for (single_func in func_list) {
+    # Detect step in the function name
+    if (grepl("step_", single_func$func_name)) {
+      if (!recipy_flag) {
+        recipy_flag <- TRUE
+        # Create recipy
+        outcome_variable <- single_func$all_vars$outcome
+        if (is.null(outcome_variable)) {
+          outcome_variable <- "."
+        }
+        recipe_formula <- paste(outcome_variable, "~ .")
+        recipe_object <-
+          recipes::recipe(formula = recipe_formula,
+                          x = data)
+        
+        # Remove default predictor role if output was suplied
+        if (outcome_variable != "."){
+        recipe_object <-
+          remove_role(recipe_object, all_predictors(), old_role = "predictor")
+        }
+        
+        # Assign roles
+        for (new_role in names(single_func$all_vars)) {
+          params <- list(recipe = recipe_object, rlang::parse_expr(unlist(single_func$all_vars[[new_role]])), new_role = new_role)
+          do.call(add_role, params)
+        }
+      }
+      # Add to recipe
+      params <- list(recipe = recipe_object, rlang::parse_expr(unlist(single_func$args)))
+      for (param_name in names(single_func$params)) {
+        param_to_add <- trimws(single_func$params[[param_name]])
+        if(!is.na(as.numeric(param_to_add))){param_to_add <- as.numeric(param_to_add)}
+        else if(!is.na(as.logical(param_to_add))){param_to_add <- as.logical(param_to_add)}
+        params[[param_name]] <- param_to_add
+      }
+      recipe_object <- do.call(get(single_func$func_name), params)
+      
+    }else{
+      # Check for running recipy
+      if(recipy_flag){
+        # Bake existing recipy and update working data to run non recipy function on
+        recipe_object <-
+          recipes::prep(recipe_object, training = data)
+        data <- recipes::bake(recipe_object, new_data = data)
+        recipy_flag <- FALSE
+        params <- list(data = data, rlang::parse_expr(unlist(single_func$args)))
+        for (param_name in names(single_func$params)) {
+          param_to_add <- trimws(single_func$params[[param_name]])
+          if(!is.na(as.numeric(param_to_add))){param_to_add <- as.numeric(param_to_add)}
+          else if(!is.na(as.logical(param_to_add))){param_to_add <- as.logical(param_to_add)}
+          params[[param_name]] <- param_to_add
+        }
+        data <- do.call(get(single_func$func_name), params)
+      }
+      
+    }
+  }
+  
+  # Bake if not yet baked
+  if(recipy_flag){
+    # Bake existing recipy and update working data to run non recipy function on
+    recipe_object <-
+      recipes::prep(recipe_object, training = data)
+    data <- recipes::bake(recipe_object, new_data = data)
+  }
+  
+  return(data)
+}
+
+#' Verify data sequence match
+#'
+#' This functions verifies that the data is from the previous step in
+#' the module sequence
+#'
+#' @param module_sequence_number the sequence number being checked
+#' @param data the data to check
+verify_data_and_sequence_match <-
+  function(module_sequence_number, data) {
+    if (module_sequence_number[[1]] == 1 &&
+        attr(data, pkg.globals$bllFlowContent.Sequence) != 0) {
+      stop(
+        "Working data was passed where sequence is at step 1.
+        Make sure to pass the starting data.
+        Aborting operation!"
+      )
+    } else if (attr(data, pkg.globals$bllFlowContent.Sequence) == 0) {
+      if (module_sequence_number[[1]] != 1) {
+        stop(
+          paste(
+            "Non-working data was passed to sequence greater then step 1.
+            Please make sure that you are passing working data that is the result of the
+            module sequence before",
+            module_sequence_number,
+            "
+            Aborting operation!"
+          )
+        )
+      }
+    } else if (attr(data, pkg.globals$bllFlowContent.Sequence) + 1 !=
+               module_sequence_number[[1]]) {
+      stop(
+        paste(
+          "The working data passed is not from the previous module please
+          verify that the data passed is from module",
+          module_sequence_number - 1,
+          "
+            Aborting operation!"
+        )
+      )
+    }
+  }
\ No newline at end of file
diff --git a/R/recode-with-table.R b/R/recode-with-table.R
index 0521b3ad..e93d0544 100644
--- a/R/recode-with-table.R
+++ b/R/recode-with-table.R
@@ -1,5 +1,6 @@
 # Removing Note
 . <- NULL
+
 #' Recode with Table
 #'
 #' Recode with Table is responsible for recoding values of a dataset based on
@@ -123,7 +124,10 @@ rec_with_table <-
            notes = TRUE,
            var_labels = NULL,
            custom_function_path = NULL,
-           attach_data_name = FALSE) {
+           attach_data_name = FALSE,
+           id_role_name = NULL) {
+    
+    id_role_name <- list(id_role_name)
     # If custom Functions are passed create new environment and source
     if (!is.null(custom_function_path)) {
       source(custom_function_path)
@@ -190,6 +194,14 @@ rec_with_table <-
       if (attach_data_name) {
         data[["data_name"]] <- database_name
       }
+      if(!is.null(id_role_name)){
+        for (single_id in id_role_name) {
+          if(!is.null(single_id)){
+          data <- create_id_row(data, single_id, database_name)
+          }
+        }
+        
+      }
     } else {
       stop(
         paste(
@@ -396,26 +408,26 @@ recode_columns <-
     map_variables_to_process <-
       variables_to_process[grepl("map::", variables_to_process[[
         pkg.globals$argument.CatValue]]), ]
-
+    
+    id_variables_to_process<-
+      variables_to_process[grepl("id_from::", variables_to_process[[
+        pkg.globals$argument.CatValue]]), ]
+    
     func_variables_to_process <-
       variables_to_process[grepl("Func::", variables_to_process[[
         pkg.globals$argument.CatValue]]), ]
 
     rec_variables_to_process <-
-      variables_to_process[(!grepl("Func::|map::", variables_to_process[[
+      variables_to_process[(!grepl("Func::|map::|id_from::", variables_to_process[[
         pkg.globals$argument.CatValue]])) & (!grepl("DerivedVar::",
                                                     variables_to_process[[
           pkg.globals$argument.VariableStart]])), ]
 
     label_list <- list()
     # Set interval if none is present
-    interval_present <- TRUE
     valid_intervals <- c("[,]", "[,)", "(,]")
     interval_default <- "[,]"
     recoded_data <- data[, 0]
-    if (is.null(rec_variables_to_process[[pkg.globals$argument.Interval]])) {
-      interval_present <- FALSE
-    }
 
     # Loop through the rows of recode vars
     while (nrow(rec_variables_to_process) > 0) {
@@ -529,11 +541,18 @@ recode_columns <-
 
             # Recode the variable
             from_values <- list()
-            if (grepl(":", as.character(row_being_checked[[
+            if (grepl("\\[*\\]", as.character(row_being_checked[[
               pkg.globals$argument.From]]))) {
               from_values <-
                 strsplit(as.character(row_being_checked[[
-                  pkg.globals$argument.From]]), ":")[[1]]
+                  pkg.globals$argument.From]]), ",")[[1]]
+              from_values[[1]] <- trimws(from_values[[1]])
+              from_values[[2]] <- trimws(from_values[[2]])
+              interval_left <- substr(from_values[[1]], 1, 1)
+              interval_right <- substr(from_values[[2]], nchar(from_values[[2]]), nchar(from_values[[2]]))
+              interval <- paste0(interval_left,",",interval_right)
+              from_values[[1]] <- gsub("\\[|\\]", "", from_values[[1]])
+              from_values[[2]] <- gsub("\\[|\\]", "", from_values[[2]])
             } else {
               temp_from <-
                 as.character(row_being_checked[[pkg.globals$argument.From]])
@@ -542,36 +561,19 @@ recode_columns <-
             }
             value_recorded <-
               as.character(row_being_checked[[pkg.globals$argument.CatValue]])
-            if (interval_present) {
-              interval <- as.character(row_being_checked[[
-                pkg.globals$argument.Interval]])
-              if (!interval %in% valid_intervals) {
-                interval <- interval_default
-              }
-              if (from_values[[1]] == from_values[[2]]) {
-                interval <- "[,]"
-              }
-              valid_row_index <- compare_value_based_on_interval(
-                compare_columns = data_variable_being_checked,
-                data = data,
-                left_boundary = from_values[[1]],
-                right_boundary = from_values[[2]],
-                interval = interval
-              )
-            } else {
-              if (from_values[[1]] == from_values[[2]]) {
-                interval <- "[,]"
-              } else {
-                interval <- interval_default
-              }
-              valid_row_index <- compare_value_based_on_interval(
-                compare_columns = data_variable_being_checked,
-                data = data,
-                left_boundary = from_values[[1]],
-                right_boundary = from_values[[2]],
-                interval = interval
-              )
+            if (from_values[[1]] == from_values[[2]]) {
+              interval <- "[,]"
+            }else if (!interval %in% valid_intervals) {
+              message(paste("For variable", variable_being_checked, "invalid interval was passed.\nDefault interval will be used:", interval_default))
+              interval <- interval_default
             }
+            valid_row_index <- compare_value_based_on_interval(
+              compare_columns = data_variable_being_checked,
+              data = data,
+              left_boundary = from_values[[1]],
+              right_boundary = from_values[[2]],
+              interval = interval
+            )
             # Start construction of dataframe for log
             log_table[row, "value_to"] <- value_recorded
             log_table[row, "From"] <-
@@ -640,6 +642,38 @@ recode_columns <-
       func_variables_to_process <-
         derived_return$variables_to_process
     }
+    
+    #Process Id Vars
+    top_function_frame <- parent.frame(2)
+    while (nrow(id_variables_to_process) > 0) {
+      # Extract type of id creation
+      current_id <- id_variables_to_process[1, ]
+      id_variables_to_process <- id_variables_to_process[-1, ]
+      id_creation_function <- as.character(current_id[[pkg.globals$argument.CatValue]])
+      id_creation_function <- strsplit(id_creation_function, "::")[[1]][[2]]
+      id_creation_function <- trimws(id_creation_function)
+      
+      # Extract the variables
+      id_feeder_vars <- as.character(current_id[[pkg.globals$argument.VariableStart]])
+      id_feeder_vars <- strsplit(id_feeder_vars,"::" )[[1]][[2]]
+      id_feeder_vars <-  gsub("\\[|\\]", "", id_feeder_vars)
+      id_feeder_vars <- strsplit(id_feeder_vars,"," )[[1]]
+      tmp_feeder_vars <- c()
+      for (single_var in id_feeder_vars) {
+        single_var <- trimws(single_var)
+        tmp_feeder_vars <- append(tmp_feeder_vars, single_var)
+      }
+      
+      # Extract Id Name
+      id_name <- as.character(current_id[[pkg.globals$argument.Variables]])
+      
+      # Create id_object to append at the end
+      tmp_list <- list(var_name = id_name, feeder_vars = tmp_feeder_vars)
+      top_function_frame$id_role_name <- append(top_function_frame$id_role_name, list(tmp_list))
+      
+      
+    }
+    
     # Populate data Labels
     recoded_data <-
       label_data(label_list = label_list, data_to_label = recoded_data)
diff --git a/R/scramble-data.R b/R/scramble-data.R
index 1ff84b72..a9326f8c 100644
--- a/R/scramble-data.R
+++ b/R/scramble-data.R
@@ -11,56 +11,58 @@
 #' @return bllflow_object where the variables matching the role are
 #' scrambled in working_data
 #' @export
-scramble_data <- function(bllflow_object, role_name = NULL, strata = NULL) {
-  # Extract variables based on role_name values
-  role_name <- paste0(role_name, ",")
-  vars_to_scramble <-
-    as.character(bllflow_object[[
-      pkg.globals$bllFlowContent.Variables]][
-        grepl(role_name,
-              bllflow_object[[pkg.globals$bllFlowContent.Variables]][[
-                pkg.globals$argument.Role]]),
-        pkg.globals$MSW.Variables.Columns.Variable])
-  vars_to_scramble <- trimws(vars_to_scramble)
-  data_to_scramble <-
-    bllflow_object[[pkg.globals$bllFlowContent.WorkingData]]
-
-  if (is.null(strata)) {
-    scrambled_cols <-
-      data_to_scramble[sample(nrow(data_to_scramble)), vars_to_scramble]
-    data_to_scramble[, vars_to_scramble] <- scrambled_cols
-  } else{
-    data_to_scramble <- recurse_scramble(data_to_scramble,
-                                         strata, vars_to_scramble)
-  }
-
-  bllflow_object[[pkg.globals$bllFlowContent.WorkingData]] <-
-    data_to_scramble
-  return(bllflow_object)
-}
-
-recurse_scramble <- function(data_subset, strata_vars, vars_to_scramble) {
-  current_var <- strata_vars[1]
-  # Remove first var
-  strata_vars <- strata_vars[-1]
-
-  # Collect all unique values for current_var
-  current_var_values <- unique(data_subset[[current_var]])
-
-  # Return Data
-  ret_data <- data_subset[0, ]
-  for (value in current_var_values) {
-    data_value_subset <- data_subset[data_subset[[current_var]] == value, ]
-    if (length(strata_vars) == 0) {
+scramble_data <-
+  function(bllflow_object,
+           role_name = NULL,
+           strata = NULL) {
+    # Extract variables based on role_name values
+    role_name <- paste0(role_name, ",")
+    vars_to_scramble <-
+      select_vars_by_role(role_name, bllflow_object[[pkg.globals$bllFlowContent.Variables]])
+    vars_to_scramble <- trimws(vars_to_scramble)
+    data_to_scramble <-
+      bllflow_object[[pkg.globals$bllFlowContent.WorkingData]]
+    
+    if (is.null(strata)) {
       scrambled_cols <-
-        data_value_subset[sample(nrow(data_value_subset)), vars_to_scramble]
-      data_value_subset[, vars_to_scramble] <- scrambled_cols
-    }else{
-      data_value_subset <- recurse_scramble(data_value_subset,
-                                            strata_vars, vars_to_scramble)
+        data_to_scramble[sample(nrow(data_to_scramble)), vars_to_scramble]
+      data_to_scramble[, vars_to_scramble] <- scrambled_cols
+    } else{
+      data_to_scramble <- recurse_scramble(data_to_scramble,
+                                           strata, vars_to_scramble)
     }
-    ret_data <- rbind(ret_data, data_value_subset)
+    
+    bllflow_object[[pkg.globals$bllFlowContent.WorkingData]] <-
+      data_to_scramble
+    return(bllflow_object)
   }
 
-  return(ret_data)
-}
\ No newline at end of file
+recurse_scramble <-
+  function(data_subset,
+           strata_vars,
+           vars_to_scramble) {
+    current_var <- strata_vars[1]
+    # Remove first var
+    strata_vars <- strata_vars[-1]
+    
+    # Collect all unique values for current_var
+    current_var_values <- unique(data_subset[[current_var]])
+    
+    # Return Data
+    ret_data <- data_subset[0,]
+    for (value in current_var_values) {
+      data_value_subset <-
+        data_subset[data_subset[[current_var]] == value,]
+      if (length(strata_vars) == 0) {
+        scrambled_cols <-
+          data_value_subset[sample(nrow(data_value_subset)), vars_to_scramble]
+        data_value_subset[, vars_to_scramble] <- scrambled_cols
+      } else{
+        data_value_subset <- recurse_scramble(data_value_subset,
+                                              strata_vars, vars_to_scramble)
+      }
+      ret_data <- rbind(ret_data, data_value_subset)
+    }
+    
+    return(ret_data)
+  }
\ No newline at end of file
diff --git a/R/step_with_formula_parsing.R b/R/step_with_formula_parsing.R
index e171f97f..eb6b8753 100644
--- a/R/step_with_formula_parsing.R
+++ b/R/step_with_formula_parsing.R
@@ -162,10 +162,7 @@ create_formula_recipe <-
            variables) {
     # Check variable roles for the recipe creation
     # TODO bllflow support make this add to overall recipe
-    outcome_variable <-
-      as.character(variables[grepl("outcome,",
-                                   variables[[pkg.globals$argument.Role]]),
-                             pkg.globals$argument.Variables])
+    outcome_variable <-select_vars_by_role("outcome", variables)
     # TODO add a function for creating a proper formula
     recipe_formula <- paste(outcome_variable, "~ .")
     
diff --git a/R/table-one-long.R b/R/table-one-long.R
index 607ecbb7..f7ec2b17 100644
--- a/R/table-one-long.R
+++ b/R/table-one-long.R
@@ -65,8 +65,7 @@ CreateTableOne.BLLFlow <- function(x,
       vars <-
         as.character(variables_sheet[[pkg.globals$MSW.Variables.Columns.Variable]])
     } else {
-      vars <-
-        as.character(variables_sheet[grepl(select_role, variables_sheet[[pkg.globals$argument.Role]]), pkg.globals$MSW.Variables.Columns.Variable])
+      vars <- select_vars_by_role(select_role, variables_sheet)
     }
     vars <- trimws(vars)
   }
@@ -74,12 +73,12 @@ CreateTableOne.BLLFlow <- function(x,
     if (is.null(select_role)) {
       factor_vars <-
         as.character(variables_sheet[is_equal(variables_sheet[[pkg.globals$MSW.Variables.Columns.VariableType]],
-                                              pkg.globals$ddiValueName.Categorical), pkg.globals$MSW.Variables.Columns.Variable])
+                                              pkg.globals$ddiValueName.Categorical), pkg.globals$MSW.Variables.Columns.Variable][[1]])
     } else {
       factor_vars <-
         as.character(variables_sheet[is_equal(variables_sheet[[pkg.globals$MSW.Variables.Columns.VariableType]],
-                                              pkg.globals$ddiValueName.Categorical) &
-                                       grepl(select_role, variables_sheet[[pkg.globals$argument.Role]]), pkg.globals$MSW.Variables.Columns.Variable])
+                                              pkg.globals$ddiValueName.Categorical) , pkg.globals$MSW.Variables.Columns.Variable][[1]])
+      factor_vars <- factor_vars[[factor_vars %in% vars]]
     }
     factor_vars <- trimws(factor_vars)
   }
diff --git a/R/tableone-print-modification.R b/R/tableone-print-modification.R
index 8188e39c..ea9f83fc 100644
--- a/R/tableone-print-modification.R
+++ b/R/tableone-print-modification.R
@@ -36,34 +36,59 @@
 ##'
 ##' @export
 print.TableOne <-
-  function(x, # TableOne object
-             catDigits = 1, contDigits = 2, pDigits = 3, # Number of digits to show
-             quote = FALSE, # Whether to show quotes
-
-             ## Common options
-             missing = FALSE, # Not implemented yet
-             explain = TRUE, # Whether to show explanation in variable names
-             printToggle = TRUE, # Whether to print the result visibly
-             test = TRUE, # Whether to add p-values
-             smd = FALSE, # Whether to add standardized mean differences
-             noSpaces = FALSE, # Whether to remove spaces for alignments
-             padColnames = FALSE, # Whether to pad column names for alignments
-             varLabels = FALSE, # Whether to show variable labels instead of names.
-             valLabels = FALSE, # Whether to show category labels instead of values.
-             missingLabels = "Missing Label", # What label is assigned to missing value labels
-
-             ## Categorical options
-             format = c("fp", "f", "p", "pf")[1], # Format f_requency and/or p_ercent
-             showAllLevels = FALSE, # Show all levels of a categorical variable
-             cramVars = NULL, # Which 2-level variables to show both levels in one row
-             dropEqual = FALSE, # Do not show " = second level" for two-level variables
-             exact = NULL, # Which variables should be tested with exact tests
-
-             ## Continuous options
-             nonnormal = NULL, # Which variables should be treated as nonnormal
-             minMax = FALSE, # Whether to show median
-
-             ...) {
+  function(x,
+           # TableOne object
+           catDigits = 1,
+           contDigits = 2,
+           pDigits = 3,
+           # Number of digits to show
+           quote = FALSE,
+           # Whether to show quotes
+           
+           ## Common options
+           missing = FALSE,
+           # Not implemented yet
+           explain = TRUE,
+           # Whether to show explanation in variable names
+           printToggle = TRUE,
+           # Whether to print the result visibly
+           test = TRUE,
+           # Whether to add p-values
+           smd = FALSE,
+           # Whether to add standardized mean differences
+           noSpaces = FALSE,
+           # Whether to remove spaces for alignments
+           padColnames = FALSE,
+           # Whether to pad column names for alignments
+           varLabels = FALSE,
+           # Whether to show variable labels instead of names.
+           valLabels = FALSE,
+           # Whether to show category labels instead of values.
+           missingLabels = "Missing Label",
+           # What label is assigned to missing value labels
+           
+           ## Categorical options
+           format = c("fp", "f", "p", "pf")[1],
+           # Format f_requency and/or p_ercent
+           showAllLevels = FALSE,
+           # Show all levels of a categorical variable
+           cramVars = NULL,
+           # Which 2-level variables to show both levels in one row
+           dropEqual = FALSE,
+           # Do not show " = second level" for two-level variables
+           exact = NULL,
+           # Which variables should be tested with exact tests
+           
+           ## Continuous options
+           nonnormal = NULL,
+           # Which variables should be treated as nonnormal
+           minMax = FALSE,
+           # Whether to show median
+           
+           formatOptions = list(scientific = FALSE),
+           # Options for formatting
+           ...) {
+    
 
     ## Extract Cont/CatTable elements of x and dispatch print() appropriately
     FmtTables <- tableone:::ModuleFormatTables(x,
@@ -80,7 +105,10 @@ print.TableOne <-
 
       ## print.ContTable arguments passed
       nonnormal = nonnormal, minMax = minMax,
-      insertLevel = showAllLevels
+      insertLevel = showAllLevels,
+      
+      ## FormatOptions passed
+      formatOptions = formatOptions
     )
 
     ## List of stratum sample size row only tables
diff --git a/R/util-funcs.R b/R/util-funcs.R
index e84433de..b48af46c 100644
--- a/R/util-funcs.R
+++ b/R/util-funcs.R
@@ -48,3 +48,43 @@ clean_strata_values <-
     return(strata_values)
   }
 
+#' Vars selected by role
+select_vars_by_role <- function(roles, variables){
+  # Reduce row looping by only looping over only unique combinations
+  unique_roles <- unique(variables[[pkg.globals$argument.Role]])
+  valid_patern <- c()
+  for (role_patern in unique_roles) {
+    # Split by commas to avoid partial matches being false positives
+    role_list <- strsplit(role_patern, ",")[[1]]
+    for (role in role_list){
+      if(role %in% roles){
+        valid_patern <- append(valid_patern, role_patern)
+      }
+    }
+  }
+  ret <- as.character(variables[variables[[pkg.globals$argument.Role]] == valid_patern, pkg.globals$MSW.Variables.Columns.Variable])
+  
+  return(ret)
+}
+
+# ID role creation
+create_id_row <- function(data, id_role_name, database_name){
+  # Check for role or variables
+  id_cols <- c()
+  if(!is.null(id_role_name$feeder_vars)){
+    id_cols <- append(id_cols,id_role_name$feeder_vars)
+  }else if (!is.null(id_role_name$feeder_roles)){
+    id_cols <- append(id_cols, select_vars_by_role(roles = id_role_name$feeder_roles, variables = variables))
+  }else {
+    message("id_role_name does not contain feeder_roles or feeder_vars.
+                  No id column was created")
+  }
+  if("data_name" %in% id_role_name$feeder_vars && is.null(data[["data_name"]])){
+    data[["data_name"]] <- database_name
+  }
+  tmp_data <- tidyr::unite(data = data, tmp, sep = "_", id_cols)
+  data[[id_role_name$var_name]] <- tmp_data$tmp
+  
+  return(data)
+}
+