modify all R code with air and quarto formatting

app-profile.R

@@ -1,6 +1,8 @@
 library(profvis)
 
-setwd("/home/dylan/Personal/Projects/Hurricane Normalization/R/dataScripts/restructured/app")
+setwd(
+  "/home/dylan/Personal/Projects/Hurricane Normalization/R/dataScripts/restructured/app"
+)
 
 profvis({
   rmarkdown::render("dashboard.Rmd")

queries.R

@@ -9,7 +9,9 @@ macdir <- "~/Desktop/Personal/Projects/Hurricane Normalization/"
 #baseDir <- macdir
 baseDir <- linuxdir
 
-config <- config::get(file = paste0(baseDir, "R/dataScripts/restructured/app/config.yml"))
+config <- config::get(
+  file = paste0(baseDir, "R/dataScripts/restructured/app/config.yml")
+)
 
 # SUPABASE CON
 con <- dbConnect(
@@ -132,7 +134,12 @@ get_unique_lf_ids <- function(storm) {
       full_lf_id
     ) %>%
     select(
-      storm_basin, storm_year, storm_name, lf_type, lf_id, full_lf_id
+      storm_basin,
+      storm_year,
+      storm_name,
+      lf_type,
+      lf_id,
+      full_lf_id
     )
 
   result <- query %>% collect()
@@ -153,7 +160,9 @@ get_normalized_cost_index <- function(storm, full_lf_id) {
       lf_id == lf_id_parts$lf_id
     ) %>%
     select(
-      normalization_year, mmh, mmp
+      normalization_year,
+      mmh,
+      mmp
     )
 
   result <- query %>% collect()
@@ -162,7 +171,9 @@ get_normalized_cost_index <- function(storm, full_lf_id) {
 }
 
 test_query <- function() {
-  result <- dbGetQuery(con, "WITH yearly_totals AS (
+  result <- dbGetQuery(
+    con,
+    "WITH yearly_totals AS (
     SELECT 
         year,
         SUM(population) as total_population,
@@ -188,14 +199,17 @@ SELECT
     ROUND(total_housing_units / base_housing, 4) as housing_index
 FROM yearly_totals
 CROSS JOIN base_year
-ORDER BY year;")
+ORDER BY year;"
+  )
 
   result <- result %>%
     mutate(
       year = as.Date(paste0(year, "-01-01"))
     ) %>%
     select(
-      year, population_index, housing_index
+      year,
+      population_index,
+      housing_index
     )
 
   result_ts <- result %>%
@@ -217,7 +231,12 @@ get_all_normalized_cost_index <- function(storm) {
       full_lf_id = paste0(lf_type, lf_id)
     ) %>%
     select(
-      normalization_year, mmh_index, mmp_index, mmh_loss = mmh, mmp_loss = mmp, full_lf_id
+      normalization_year,
+      mmh_index,
+      mmp_index,
+      mmh_loss = mmh,
+      mmp_loss = mmp,
+      full_lf_id
     )
 
   result <- query %>% collect()
@@ -345,7 +364,7 @@ get_normalized_metric_growth <- function(storm, full_lf_id) {
       county_fips,
       baseline_population = population,
       baseline_housing = housing_units
-  )
+    )
 
   normalized_metrics <- metrics.pop_and_housing %>%
     filter(
@@ -354,8 +373,10 @@ get_normalized_metric_growth <- function(storm, full_lf_id) {
     inner_join(affected_counties, by = c("state_fips", "county_fips")) %>%
     inner_join(baseline_metrics, by = c("state_fips", "county_fips")) %>%
     mutate(
-      normalized_population = as.numeric(population) / as.numeric(baseline_population),
-      normalized_housing = as.numeric(housing_units) / as.numeric(baseline_housing)
+      normalized_population = as.numeric(population) /
+        as.numeric(baseline_population),
+      normalized_housing = as.numeric(housing_units) /
+        as.numeric(baseline_housing)
     ) %>%
     select(
       state_fips,
@@ -368,9 +389,14 @@ get_normalized_metric_growth <- function(storm, full_lf_id) {
     )
 
   query <- normalized_metrics %>%
-    inner_join(public.counties, by = c("state_fips" = "statefp", "county_fips" = "countyfp")) %>% 
+    inner_join(
+      public.counties,
+      by = c("state_fips" = "statefp", "county_fips" = "countyfp")
+    ) %>%
     mutate(
-      geom_wkt = sql("ST_AsText(ST_SimplifyPreserveTopology(ST_Transform(geom, 4326), .001))")
+      geom_wkt = sql(
+        "ST_AsText(ST_SimplifyPreserveTopology(ST_Transform(geom, 4326), .001))"
+      )
     ) %>%
     select(
       state_fips,