diff --git a/R/calculate_go_enrichment.R b/R/calculate_go_enrichment.R
index febb25df..12dd462e 100644
--- a/R/calculate_go_enrichment.R
+++ b/R/calculate_go_enrichment.R
@@ -82,12 +82,12 @@ go_enrichment <- function(...) {
 #' determines if the enrichment analysis should be performed in order to check for both enrichemnt and
 #' deenrichemnt or only one of the two. This affects the statistics performed and therefore also the displayed
 #' plot.
-#' @param replace_long_name a logical argument that specifies if GO term names above 50 characters should 
-#' be replaced by the GO ID instead for the plot. This ensures that the plotting area doesn't become 
+#' @param replace_long_name a logical argument that specifies if GO term names above 50 characters should
+#' be replaced by the GO ID instead for the plot. This ensures that the plotting area doesn't become
 #' too small due to the long name. The default is `TRUE`.
-#' @param label_move_frac a numeric argument between 0 and 1 that specifies which labels should be 
+#' @param label_move_frac a numeric argument between 0 and 1 that specifies which labels should be
 #' moved outside of the bar. The default is 0.2, which means that the labels of all bars that have a size
-#' of 20% or less of the largest bar are moved to the right of the bar. This prevents labels from 
+#' of 20% or less of the largest bar are moved to the right of the bar. This prevents labels from
 #' overlapping with the bar boundaries.
 #' @param min_n_detected_proteins_in_process is a numeric argument that specifies the minimum number of
 #' detected proteins required for a GO term to be displayed in the plot. The default is 1, meaning
@@ -364,7 +364,7 @@ if you used the right organism ID.", prefix = "\n", initial = ""))
       }
     } %>%
     tidyr::complete(.data$go_id, tidyr::nesting(!!rlang::ensym(is_significant), n_sig), fill = list(n_has_process = 0)) %>%
-    dplyr::ungroup() %>% 
+    dplyr::ungroup() %>%
     tidyr::drop_na(.data$go_id)
 
 
@@ -447,9 +447,9 @@ if you used the right organism ID.", prefix = "\n", initial = ""))
   filtered_result_table <- result_table %>%
     dplyr::ungroup() %>%
     dplyr::filter(.data$n_detected_proteins_in_process >= min_n_detected_proteins_in_process)
-  
-  if(replace_long_name){
-    filtered_result_table <- filtered_result_table %>% 
+
+  if (replace_long_name) {
+    filtered_result_table <- filtered_result_table %>%
       mutate(term = ifelse(nchar(.data$term) > 50, .data$go_id, .data$term))
   }
 
@@ -468,10 +468,10 @@ if you used the right organism ID.", prefix = "\n", initial = ""))
     plot_input <- filtered_result_table %>%
       dplyr::ungroup() %>%
       dplyr::mutate(neg_log_sig = -log10(!!rlang::ensym(type))) %>%
-      dplyr::group_by({{ group }}) %>% 
-      dplyr::mutate(n = 1:dplyr::n()) %>% 
+      dplyr::group_by({{ group }}) %>%
+      dplyr::mutate(n = 1:dplyr::n()) %>%
       dplyr::filter(n <= top)
-      # dplyr::slice(1:top)
+    # dplyr::slice(1:top)
   } else {
     split_cutoff <- stringr::str_split(plot_cutoff, pattern = " ", simplify = TRUE)
     type <- split_cutoff[1]
@@ -481,9 +481,9 @@ if you used the right organism ID.", prefix = "\n", initial = ""))
       dplyr::mutate(neg_log_sig = -log10(!!rlang::ensym(type))) %>%
       dplyr::filter(!!rlang::ensym(type) <= threshold)
   }
-  
+
   # move label if bar is less than 20% (default) of largest bar
-  plot_input <- plot_input %>% 
+  plot_input <- plot_input %>%
     mutate(hjust = ifelse((.data$neg_log_sig / max(.data$neg_log_sig)) < label_move_frac, -0.15, 1.05))
 
   if (plot_style == "barplot") {