remove static county data table

This commit is contained in:
2025-10-31 12:01:42 -04:00
parent 69b6f32002
commit 53fec2582f
+54 -41
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@@ -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"}