Skip nullgrob() since plot generation provides full chunks

qhtcp-workflow/apps/r/calculate_interaction_zscores.R

@@ -597,13 +597,13 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
         grid_layout$nrow <- ceiling(num_plots / grid_layout$ncol)
       }
       
-      total_spots <- grid_layout$nrow * grid_layout$ncol
+      # total_spots <- grid_layout$nrow * grid_layout$ncol
-      num_plots <- length(static_plots)
+      # num_plots <- length(static_plots)
 
-      if (num_plots < total_spots) {
+      # if (num_plots < total_spots) {
-        message("Filling ", total_spots - num_plots, " empty spots with nullGrob()")
+      #   message("Filling ", total_spots - num_plots, " empty spots with nullGrob()")
-        static_plots <- c(static_plots, replicate(total_spots - num_plots, nullGrob(), simplify = FALSE))
+      #   static_plots <- c(static_plots, replicate(total_spots - num_plots, nullGrob(), simplify = FALSE))
-      }
+      # }
 
       grid.arrange(
         grobs = static_plots,
@@ -739,36 +739,16 @@ generate_scatter_plot <- function(plot, config) {
     slope <- config$lm_line$slope
     color <- ifelse(!is.null(config$lm_line$color), config$lm_line$color, "blue")
     linewidth <- ifelse(!is.null(config$lm_line$linewidth), config$lm_line$linewidth, 1)
+    y_min <- intercept + slope * x_min
+    y_max <- intercept + slope * x_max
+    
+    # Ensure y-values are within y-limits (if any)
+    if (!is.null(config$ylim_vals)) {
+      y_min_within_limits <- y_min >= config$ylim_vals[1] && y_min <= config$ylim_vals[2]
+      y_max_within_limits <- y_max >= config$ylim_vals[1] && y_max <= config$ylim_vals[2]
 
-    # Ensure none of the values are NA and calculate y-values
+      # Adjust or skip based on whether the values fall within limits
-    if (!is.na(x_min) && !is.na(x_max) && !is.na(intercept) && !is.na(slope)) {
+      if (y_min_within_limits && y_max_within_limits) {
-      y_min <- intercept + slope * x_min
-      y_max <- intercept + slope * x_max
-      
-      # Ensure y-values are within y-limits (if any)
-      if (!is.null(config$ylim_vals)) {
-        y_min_within_limits <- y_min >= config$ylim_vals[1] && y_min <= config$ylim_vals[2]
-        y_max_within_limits <- y_max >= config$ylim_vals[1] && y_max <= config$ylim_vals[2]
-
-        # Adjust or skip based on whether the values fall within limits
-        if (y_min_within_limits && y_max_within_limits) {
-          # Ensure x-values are also valid
-          if (!is.na(x_min) && !is.na(x_max)) {
-            plot <- plot + annotate(
-              "segment",
-              x = x_min,
-              xend = x_max,
-              y = y_min,
-              yend = y_max,
-              color = color,
-              linewidth = linewidth
-            )
-          }
-        } else {
-          message("Skipping linear regression line due to y-values outside of limits")
-        }
-      } else {
-        # If no y-limits are provided, proceed with the annotation
         plot <- plot + annotate(
           "segment",
           x = x_min,
@@ -778,9 +758,20 @@ generate_scatter_plot <- function(plot, config) {
           color = color,
           linewidth = linewidth
         )
+      } else {
+        message("Skipping linear regression line due to y-values outside of limits")
       }
     } else {
-      message("Skipping linear regression line due to missing or invalid values")
+      # If no y-limits are provided, proceed with the annotation
+      plot <- plot + annotate(
+        "segment",
+        x = x_min,
+        xend = x_max,
+        y = y_min,
+        yend = y_max,
+        color = color,
+        linewidth = linewidth
+      )
     }
   }
 
@@ -1052,7 +1043,7 @@ generate_interaction_plot_configs <- function(df_summary, df_interactions, type)
       NG_value <- first(group_data_filtered$NG, default = 0)
       DB_value <- first(group_data_filtered$DB, default = 0)
       SM_value <- first(group_data_filtered$SM, default = 0)
-      
+
       lm_intercept_col <- paste0("lm_intercept_", var)
       lm_slope_col <- paste0("lm_slope_", var)
       lm_intercept_value <- first(group_data_filtered[[lm_intercept_col]], default = 0)
@@ -1485,7 +1476,7 @@ main <- function() {
 
     message("Generating quality control plots in parallel")
     # future::plan(future::multicore, workers = parallel::detectCores())
-    future::plan(future::multisession, workers = 3) # generate 3 plots in parallel
+    future::plan(future::multisession, workers = 3) # generate 3 plot files in parallel
 
     plot_configs <- list(
       list(out_dir = out_dir_qc, filename = "L_vs_K_before_quality_control",