diff --git a/server.R b/server.R
index 4b1561e..5c22d97 100644
--- a/server.R
+++ b/server.R
@@ -1295,13 +1295,13 @@ server <- function(input, output, session) {
     if (is.null(input$national_comparison_checkbox_o1) && is.null(input$region_comparison_checkbox_o1)) {
       filtered_data <- cla_rates %>%
         filter(geo_breakdown %in% input$geographic_breakdown_o1) %>%
-        select(time_period, geo_breakdown, rate_per_10000, population_count)
+        select(time_period, geo_breakdown, number, rate_per_10000, population_count)
 
       # national only
     } else if (!is.null(input$national_comparison_checkbox_o1) && is.null(input$region_comparison_checkbox_o1)) {
       filtered_data <- cla_rates %>%
         filter((geographic_level %in% input$select_geography_o1 & geo_breakdown %in% input$geographic_breakdown_o1) | geographic_level == "National") %>%
-        select(time_period, geo_breakdown, rate_per_10000, population_count)
+        select(time_period, geo_breakdown, number, rate_per_10000, population_count)
 
       # regional only
     } else if (is.null(input$national_comparison_checkbox_o1) && !is.null(input$region_comparison_checkbox_o1)) {
@@ -1310,7 +1310,7 @@ server <- function(input, output, session) {
 
       filtered_data <- cla_rates %>%
         filter((geo_breakdown %in% c(input$geographic_breakdown_o1, location$region_name))) %>%
-        select(time_period, geo_breakdown, rate_per_10000, population_count)
+        select(time_period, geo_breakdown, number, rate_per_10000, population_count)
 
       # both selected
     } else if (!is.null(input$national_comparison_checkbox_o1) && !is.null(input$region_comparison_checkbox_o1)) {
@@ -1319,14 +1319,14 @@ server <- function(input, output, session) {
 
       filtered_data <- cla_rates %>%
         filter((geo_breakdown %in% c(input$geographic_breakdown_o1, location$region_name) | geographic_level == "National")) %>%
-        select(time_period, geo_breakdown, rate_per_10000, population_count)
+        select(time_period, geo_breakdown, number, rate_per_10000, population_count)
     }
 
     datatable(
       filtered_data %>%
         filter(population_count == "Children starting to be looked after each year") %>%
-        select(time_period, geo_breakdown, rate_per_10000),
-      colnames = c("Time period", "Geographical breakdown", "Rate of children starting in care, per 10,000"),
+        select(time_period, geo_breakdown, number, rate_per_10000),
+      colnames = c("Time period", "Geographical breakdown", "Number of children starting in care", "Rate of children starting in care, per 10,000"),
       options = list(
         scrollx = FALSE,
         paging = TRUE
@@ -1356,10 +1356,10 @@ server <- function(input, output, session) {
     datatable(
       cla_rates %>% filter(geographic_level == "Regional", time_period == max(cla_rates$time_period), population_count == "Children starting to be looked after each year") %>% select(
         time_period, geo_breakdown,
-        rate_per_10000
+        number, rate_per_10000
       ) %>%
         arrange(desc(rate_per_10000)),
-      colnames = c("Time period", "Geographical breakdown", "Rate of children starting in care, per 10,000"),
+      colnames = c("Time period", "Geographical breakdown", "Number of children starting in care", "Rate of children starting in care, per 10,000"),
       options = list(
         scrollx = FALSE,
         paging = TRUE
@@ -1401,21 +1401,21 @@ server <- function(input, output, session) {
 
       data <- cla_rates %>%
         filter(geo_breakdown %in% location, time_period == max(time_period), population_count == "Children starting to be looked after each year") %>%
-        select(time_period, geo_breakdown, rate_per_10000) %>%
+        select(time_period, geo_breakdown, number, rate_per_10000) %>%
         arrange(desc(rate_per_10000))
     } else if (input$select_geography_o1 %in% c("Local authority", "National")) {
       data <- cla_rates %>%
         filter(geographic_level == "Local authority", time_period == max(cla_rates$time_period), population_count == "Children starting to be looked after each year") %>%
         select(
           time_period, geo_breakdown,
-          rate_per_10000
+          number, rate_per_10000
         ) %>%
         arrange(desc(rate_per_10000))
     }
 
     datatable(
       data,
-      colnames = c("Time period", "Geographical breakdown", "Rate of children starting in care, per 10,000"),
+      colnames = c("Time period", "Geographical breakdown", "Number of children starting in care", "Rate of children starting in care, per 10,000"),
       options = list(
         scrollx = FALSE,
         paging = TRUE
@@ -1824,6 +1824,7 @@ server <- function(input, output, session) {
     )
   })
 
+  # UASC chart
   output$plot_uasc <- plotly::renderPlotly({
     shiny::validate(
       need(input$select_geography_o1 != "", "Select a geography level."),
@@ -1836,6 +1837,7 @@ server <- function(input, output, session) {
     )
   })
 
+  # UASC table
   output$table_uasc <- renderDataTable({
     shiny::validate(
       need(input$select_geography_o1 != "", "Select a geography level."),
@@ -1848,9 +1850,9 @@ server <- function(input, output, session) {
           characteristic %in% c("Unaccompanied asylum-seeking children", "Non-unaccompanied asylum-seeking children"),
           population_count == "Children starting to be looked after each year"
         ) %>%
-        select(time_period, geo_breakdown, placement_per_10000, characteristic) %>%
+        select(time_period, geo_breakdown, characteristic, placements_number, placement_per_10000) %>%
         arrange(desc(time_period)),
-      colnames = c("Time period", "Geographical breakdown", "Rate per 10,000 children", "UASC status"),
+      colnames = c("Time period", "Geographical breakdown", "UASC status", "Number of children starting in care", "Rate per 10,000 children"),
       options = list(
         scrollx = FALSE,
         paging = TRUE,
@@ -1859,6 +1861,7 @@ server <- function(input, output, session) {
     )
   })
 
+  # UASC chart by region
   output$plot_uasc_reg <- plotly::renderPlotly({
     shiny::validate(
       need(input$select_geography_o1 != "", "Select a geography level."),
@@ -1871,6 +1874,7 @@ server <- function(input, output, session) {
     )
   })
 
+  # UASC table by region
   output$table_uasc_reg <- renderDataTable({
     shiny::validate(
       need(input$select_geography_o1 != "", "Select a geography level."),
@@ -1882,8 +1886,8 @@ server <- function(input, output, session) {
         population_count == "Children starting to be looked after each year",
         time_period == max(time_period)
       ) %>%
-        select(time_period, geo_breakdown, placement_per_10000, characteristic),
-      colnames = c("Time period", "Geographical breakdown", "Rate per 10,000 children", "UASC status"),
+        select(time_period, geo_breakdown, characteristic, placements_number, placement_per_10000),
+      colnames = c("Time period", "Geographical breakdown", "UASC status", "Number of children starting in care", "Rate per 10,000 children"),
       options = list(
         scrollx = FALSE,
         paging = TRUE
@@ -1891,6 +1895,7 @@ server <- function(input, output, session) {
     )
   })
 
+  # UASC plot by LA
   output$plot_uasc_la <- plotly::renderPlotly({
     shiny::validate(
       need(input$select_geography_o1 != "", "Select a geography level."),
@@ -1903,6 +1908,7 @@ server <- function(input, output, session) {
     )
   })
 
+  # UASC table by LA
   output$table_uasc_la <- renderDataTable({
     shiny::validate(
       need(input$select_geography_o1 != "", "Select a geography level."),
@@ -1926,7 +1932,7 @@ server <- function(input, output, session) {
           geo_breakdown %in% location, time_period == max(combined_cla_data$time_period), characteristic %in% c("Unaccompanied asylum-seeking children", "Non-unaccompanied asylum-seeking children"),
           population_count == "Children starting to be looked after each year",
         ) %>%
-        select(time_period, geo_breakdown, placement_per_10000, characteristic) %>%
+        select(time_period, geo_breakdown, characteristic, placements_number, placement_per_10000) %>%
         arrange(desc(placement_per_10000))
     } else if (input$select_geography_o1 %in% c("Local authority", "National")) {
       data <- combined_cla_data %>%
@@ -1934,13 +1940,13 @@ server <- function(input, output, session) {
           geographic_level == "Local authority", time_period == max(combined_cla_data$time_period), characteristic %in% c("Unaccompanied asylum-seeking children", "Non-unaccompanied asylum-seeking children"),
           population_count == "Children starting to be looked after each year",
         ) %>%
-        select(time_period, geo_breakdown, placement_per_10000, characteristic) %>%
+        select(time_period, geo_breakdown, characteristic, placements_number, placement_per_10000) %>%
         arrange(desc(placement_per_10000))
     }
 
     datatable(
       data,
-      colnames = c("Time period", "Geographical breakdown", "Rate per 10,000 children", "UASC status"),
+      colnames = c("Time period", "Geographical breakdown", "UASC status", "Number of children starting in care", "Rate per 10,000 children"),
       options = list(
         scrollx = FALSE,
         paging = TRUE
@@ -2008,13 +2014,13 @@ server <- function(input, output, session) {
     if (is.null(input$national_comparison_checkbox_o1) && is.null(input$region_comparison_checkbox_o1)) {
       filtered_data <- cla_rates %>%
         filter(geo_breakdown %in% input$geographic_breakdown_o1) %>%
-        select(time_period, geo_breakdown, rate_per_10000, population_count)
+        select(time_period, geo_breakdown, number, rate_per_10000, population_count)
 
       # national only
     } else if (!is.null(input$national_comparison_checkbox_o1) && is.null(input$region_comparison_checkbox_o1)) {
       filtered_data <- cla_rates %>%
         filter((geographic_level %in% input$select_geography_o1 & geo_breakdown %in% input$geographic_breakdown_o1) | geographic_level == "National") %>%
-        select(time_period, geo_breakdown, rate_per_10000, population_count)
+        select(time_period, geo_breakdown, number, rate_per_10000, population_count)
 
       # regional only
     } else if (is.null(input$national_comparison_checkbox_o1) && !is.null(input$region_comparison_checkbox_o1)) {
@@ -2023,7 +2029,7 @@ server <- function(input, output, session) {
 
       filtered_data <- cla_rates %>%
         filter((geo_breakdown %in% c(input$geographic_breakdown_o1, location$region_name))) %>%
-        select(time_period, geo_breakdown, rate_per_10000, population_count)
+        select(time_period, geo_breakdown, number, rate_per_10000, population_count)
 
       # both selected
     } else if (!is.null(input$national_comparison_checkbox_o1) && !is.null(input$region_comparison_checkbox_o1)) {
@@ -2032,14 +2038,14 @@ server <- function(input, output, session) {
 
       filtered_data <- cla_rates %>%
         filter((geo_breakdown %in% c(input$geographic_breakdown_o1, location$region_name) | geographic_level == "National")) %>%
-        select(time_period, geo_breakdown, rate_per_10000, population_count)
+        select(time_period, geo_breakdown, number, rate_per_10000, population_count)
     }
 
     datatable(
       filtered_data %>%
         filter(population_count == "Children looked after at 31 March each year") %>%
-        select(time_period, geo_breakdown, rate_per_10000),
-      colnames = c("Time period", "Geographical breakdown", "Rate per 10,000 children"),
+        select(time_period, geo_breakdown, number, rate_per_10000),
+      colnames = c("Time period", "Geographical breakdown", "Number of children looked after on 31 March", "Rate per 10,000 children"),
       options = list(
         scrollx = FALSE,
         paging = TRUE
@@ -2069,10 +2075,10 @@ server <- function(input, output, session) {
     datatable(
       cla_rates %>% filter(geographic_level == "Regional", time_period == max(cla_rates$time_period), population_count == "Children looked after at 31 March each year") %>% select(
         time_period, geo_breakdown,
-        rate_per_10000
+        number, rate_per_10000
       ) %>%
         arrange(desc(rate_per_10000)),
-      colnames = c("Time period", "Geographical breakdown", "Rate per 10,000 children"),
+      colnames = c("Time period", "Geographical breakdown", "Number of children looked after on 31 March", "Rate per 10,000 children"),
       options = list(
         scrollx = FALSE,
         paging = TRUE
@@ -2114,21 +2120,21 @@ server <- function(input, output, session) {
 
       data <- cla_rates %>%
         filter(geo_breakdown %in% location, time_period == max(time_period), population_count == "Children looked after at 31 March each year") %>%
-        select(time_period, geo_breakdown, rate_per_10000) %>%
+        select(time_period, geo_breakdown, number, rate_per_10000) %>%
         arrange(desc(rate_per_10000))
     } else if (input$select_geography_o1 %in% c("Local authority", "National")) {
       data <- cla_rates %>%
         filter(geographic_level == "Local authority", time_period == max(cla_rates$time_period), population_count == "Children looked after at 31 March each year") %>%
         select(
           time_period, geo_breakdown,
-          rate_per_10000
+          number, rate_per_10000
         ) %>%
         arrange(desc(rate_per_10000))
     }
 
     datatable(
       data,
-      colnames = c("Time period", "Geographical breakdown", "Rate per 10,000 children"),
+      colnames = c("Time period", "Geographical breakdown", "Number of children looked after on 31 March", "Rate per 10,000 children"),
       options = list(
         scrollx = FALSE,
         paging = TRUE