diff --git a/dashboard.Rmd b/dashboard.Rmd
index 047e291..4f2d3a4 100644
--- a/dashboard.Rmd
+++ b/dashboard.Rmd
@@ -949,65 +949,78 @@ output$test_dy <- renderDygraph({
 ```
 
 ### County Data {data-height=450 .no-padding}
-```{r}
+```{r include=FALSE}
 # Growth - County Table
-great_miami_data <- data.frame(
-  county_name = c(
-    "Broward County",
-    "Collier County",
-    "Miami-Dade County",
-    "Monroe County"
-  ),
-  housing_1926 = c(3.7, 0, 25, 3.5),
-  housing_2024 = c(869, 250, 1100, 55),
-  population_1926 = c(14, 0, 103, 16),
-  population_2024 = c(2100, 428, 2990, 81)
-)
 
-output$great_miami_dt <- renderDT({
+output$county_growth_dt <- renderDT({
+  req(growth_counties, input$growth_trend_map_slider, storm_selection$is_selected)
+
+  base_year <- storm_selection$storm_year
+  current_year <- input$growth_trend_map_slider
+
+  # Get county names
+  county_names <- public.counties %>%
+    select(statefp, countyfp, namelsad) %>%
+    collect()
+
+  county_data <- growth_counties() %>%
+    st_drop_geometry() %>%
+    filter(year %in% c(base_year, current_year)) %>%
+    left_join(
+      county_names,
+      by = c("state_fips" = "statefp", "county_fips" = "countyfp")
+    ) %>%
+    select(namelsad, year, population, housing_units) %>%
+    pivot_wider(
+      names_from = year,
+      values_from = c(housing_units, population),
+      names_glue = "{.value}_{year}"
+    )
+
+  housing_base <- paste0("housing_units_", base_year)
+  housing_current <- paste0("housing_units_", current_year)
+  pop_base <- paste0("population_", base_year)
+  pop_current <- paste0("population_", current_year)
+
+  county_data <- county_data %>%
+    select(
+      namelsad,
+      all_of(c(housing_base, housing_current, pop_base, pop_current))
+    )
+
   datatable(
-    great_miami_data,
-    rownames = F,
+    county_data,
+    rownames = FALSE,
     options = list(
       order = list(0, 'asc'),
-      paging = F,
-      searching = F,
-      info = F,
-      lengthChange = F,
-      server = T
+      paging = FALSE,
+      searching = FALSE,
+      info = FALSE,
+      lengthChange = FALSE,
+      server = TRUE
     ),
     colnames = c(
       "County",
-      "1926 HU",
-      "2024 HU",
-      "1926 POP",
-      "2024 POP"
-    ),
+      paste0(base_year, " HU"),
+      paste0(current_year, " HU"),
+      paste0(base_year, " POP"),
+      paste0(current_year, " POP")
+    )
   ) %>%
-    # Format housing columns with blue background
     formatStyle(
-      columns = c("housing_1926", "housing_2024"),
-      backgroundColor = "rgba(0, 0, 255, 0.2)"
+      columns = c(housing_base, housing_current),
     ) %>%
-    # Format population columns with red background
     formatStyle(
-      columns = c("population_1926", "population_2024"),
-      backgroundColor = "rgba(255, 0, 0, 0.2)"
+      columns = c(pop_base, pop_current),
     ) %>%
     formatCurrency(
-      columns = c(
-        "housing_1926",
-        "housing_2024",
-        "population_1926",
-        "population_2024"
-      ),
-      currency = "k",
+      columns = c(housing_base, housing_current, pop_base, pop_current),
       digits = 0,
-      before = F
+      before = FALSE
     )
 })
 
-DTOutput("great_miami_dt")
+DTOutput("county_growth_dt")
 ```
 
 Normalization Calculator {data-navmenu="Compute"}