Pull cur_column() from across()

qhtcp-workflow/apps/r/calculate_interaction_zscores.R

@@ -789,43 +789,39 @@ filter_and_print_non_finite <- function(df, vars_to_check, print_vars) {
 
 filter_data_for_plots <- function(df, variables, missing = TRUE, limits_map = NULL) {
 
-  # Initialize lists to store lm lines
-  lm_lines <- list()
-  
-  # Check for missing and out-of-range data
+  # Print missing data and out-of-range data separately
   for (variable in variables) {
     y_var_sym <- sym(variable)
 
-    # Print missing data if requested
     if (missing) {
       missing_data <- df %>% filter(is.na(!!y_var_sym))
       if (nrow(missing_data) > 0) {
-        message("Filtering missing data for variable ", variable, " for plotting:")
-        print(head(missing_data, 10))  # Print only the first 10 rows to avoid too much output
+        message("Missing data for variable ", variable, ":")
+        print(missing_data)
       }
     }
 
-    # Print out-of-range data if limits_map is provided
     if (!is.null(limits_map)) {
+      # Get y-limits for the variable
       ylim_vals <- limits_map[[variable]]
+
+      # Identify out-of-range data and print it
       out_of_range_data <- df %>% filter(
         !is.na(!!y_var_sym) &
           (!!y_var_sym < min(ylim_vals, na.rm = TRUE) | !!y_var_sym > max(ylim_vals, na.rm = TRUE))
       )
       if (nrow(out_of_range_data) > 0) {
-        message("Filtering out-of-range data for variable ", variable, " for plotting:")
-        print(head(out_of_range_data, 10))  # Print only the first 10 rows
+        message("Out-of-range data for variable ", variable, ":")
+        print(out_of_range_data)
       }
     }
   }
 
-  # Apply filtering across all variables in one step using if_any and if_all
+  # Filter data by checking if all variables are within the specified limits
   if (!is.null(limits_map)) {
     df_filtered <- df %>%
-      filter(if_all(all_of(variables), ~ !is.na(.))) %>%
-      filter(if_all(all_of(variables),
-        ~ between(., limits_map[[cur_column()]][1], limits_map[[cur_column()]][2])
-      ))
+      filter(if_all(all_of(variables), ~ !is.na(.))) %>%  # Check for non-NA values
+      filter(if_all(all_of(variables), ~ . >= limits_map[[cur_column()]][1] & . <= limits_map[[cur_column()]][2]))
   } else {
     df_filtered <- df %>% filter(if_all(all_of(variables), ~ !is.na(.)))
   }