diff --git a/R/mod_click_tables.R b/R/mod_click_tables.R
index 1eee7b6..164fe40 100644
--- a/R/mod_click_tables.R
+++ b/R/mod_click_tables.R
@@ -10,7 +10,8 @@
 mod_click_tables_ui <- function(id) {
   ns <- NS(id)
   tagList(
-    uiOutput(ns("dynamic_click_tableUI"))
+    uiOutput(ns("dynamic_click_tableUI")) |>
+      shinycssloaders::withSpinner()
   )
 }
 
@@ -39,7 +40,8 @@ mod_click_tables_server <- function(id, filter_data, data_exists, comment_type =
           shinycssloaders::withSpinner(),
         hr(),
         h5("Please select a Sub-category from the table above in other to drill down the table below"),
-        uiOutput(ns("comment_table"))
+        uiOutput(ns("comment_table")) |>
+          shinycssloaders::withSpinner()
       )
     })
 
diff --git a/R/mod_demographics.R b/R/mod_demographics.R
index 7127ea9..01bd5b0 100644
--- a/R/mod_demographics.R
+++ b/R/mod_demographics.R
@@ -10,7 +10,8 @@
 mod_demographics_ui <- function(id) {
   ns <- NS(id)
 
-  uiOutput(ns("dynamic_demo_UI"))
+  uiOutput(ns("dynamic_demo_UI")) |>
+    shinycssloaders::withSpinner()
 }
 
 #' demographics Server Functions
@@ -48,28 +49,53 @@ mod_demographics_server <- function(id, filter_data, data_exists) {
         br(),
         fluidRow(
           if (has_demography_1) {
-            column(width, plotly::plotlyOutput(ns("demography_1_graph")))
+            column(
+              width,
+              plotly::plotlyOutput(ns("demography_1_graph")) |>
+                shinycssloaders::withSpinner()
+            )
           },
           if (has_demography_2) {
-            column(width, plotly::plotlyOutput(ns("demography_2_graph")))
+            column(
+              width,
+              plotly::plotlyOutput(ns("demography_2_graph")) |>
+                shinycssloaders::withSpinner()
+            )
           },
           if (has_demography_3) {
-            column(width, plotly::plotlyOutput(ns("demography_3_graph")))
+            column(
+              width,
+              plotly::plotlyOutput(ns("demography_3_graph")) |>
+                shinycssloaders::withSpinner()
+            )
           }
         ),
         hr(),
         pre("The below chart shows the average percentage of FFT score for each group in the demographic feature.",
-            "Note: Categories with fewer than 10 individuals are excluded",
-            style = "background-color:#005EB8; color:#fff"),
+          "Note: Categories with fewer than 10 individuals are excluded",
+          style = "background-color:#005EB8; color:#fff"
+        ),
         fluidRow(
           if (has_demography_1) {
-            column(width, plotly::plotlyOutput(ns("compare_demography_1")))
+            column(
+              width,
+              plotly::plotlyOutput(ns("compare_demography_1")) |>
+                shinycssloaders::withSpinner()
+            )
           },
           if (has_demography_2) {
-            column(width, plotly::plotlyOutput(ns("compare_demography_2")))
+            column(
+              width,
+              plotly::plotlyOutput(ns("compare_demography_2")) |>
+                shinycssloaders::withSpinner()
+            )
           },
           if (has_demography_3) {
-            column(width, plotly::plotlyOutput(ns("compare_demography_3")))
+            column(
+              width,
+              plotly::plotlyOutput(ns("compare_demography_3")) |>
+                shinycssloaders::withSpinner()
+            )
           }
         )
       )
@@ -78,23 +104,23 @@ mod_demographics_server <- function(id, filter_data, data_exists) {
     # distribution----
 
     output$demography_1_graph <- plotly::renderPlotly({
-      demo_data <- filter_data()$unique_data %>%
+      demo_data <- filter_data()$unique_data |>
         dplyr::arrange(get_golem_config("demography_1"))
-      demo_data[, get_golem_config("demography_1")] <- demo_data[, get_golem_config("demography_1")] %>%
-        unlist(use.names = F) %>%
+      demo_data[, get_golem_config("demography_1")] <- demo_data[, get_golem_config("demography_1")] |>
+        unlist(use.names = F) |>
         factor()
-      
-      demo_data %>%
+
+      demo_data |>
         demographic_distribution(variable = get_golem_config("demography_1"))
     })
 
     output$demography_2_graph <- plotly::renderPlotly({
-      filter_data()$unique_data %>%
+      filter_data()$unique_data |>
         demographic_distribution(variable = get_golem_config("demography_2"))
     })
 
     output$demography_3_graph <- plotly::renderPlotly({
-      filter_data()$unique_data %>%
+      filter_data()$unique_data |>
         demographic_distribution(variable = get_golem_config("demography_3"))
     })
 
diff --git a/R/mod_trend.R b/R/mod_trend.R
index 80ce1d1..c8a3c12 100644
--- a/R/mod_trend.R
+++ b/R/mod_trend.R
@@ -12,7 +12,8 @@ mod_trend_ui <- function(id) {
 
   tagList(
     br(),
-    uiOutput(ns("dynamic_trendUI"))
+    uiOutput(ns("dynamic_trendUI")) |>
+      shinycssloaders::withSpinner()
   )
 }
 
@@ -55,9 +56,6 @@ mod_trend_server <- function(id, filter_data, data_exists) {
             plotly::plotlyOutput(ns("super_category_trend_plot")) %>%
               shinycssloaders::withSpinner(),
             hr(),
-            # downloadButton(ns("super_category_download_data"), "Download data",
-            #   icon = icon("download")
-            # ),
             uiOutput(ns("dynamic_super_category_table"))
           ),
 
@@ -70,16 +68,11 @@ mod_trend_server <- function(id, filter_data, data_exists) {
                 plotly::plotlyOutput(ns("sub_category_trend_plot")) %>%
                   shinycssloaders::withSpinner(),
                 hr(),
-                # downloadButton(ns("sub_category_download_data"), "Download data",
-                #   icon = icon("download")
-                # ),
                 uiOutput(ns("dynamic_sub_category_table"))
               )
             )
           )
         )
-        # hr(),
-        # DT::DTOutput(ns("dynamic_super_category_table"))
       )
     })