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(
@@ -53,7 +55,7 @@ view.simplified_county_geom <- tbl(con, "simplified_county_geom")
 view.all_loss_storms_tracks <- tbl(con, "all_loss_storms_tracks")
 view.all_conus_landfalls <- tbl(con, "all_conus_landfalls")
 
-#qry <- econ.storm_base_loss %>% 
+#qry <- econ.storm_base_loss %>%
 #  left_join(view.hurdat_track, by = c("storm_basin", "storm_year", "storm_name"))
 
 #show_query(qry)
@@ -88,11 +90,11 @@ disconnect_db <- function() {
   dbDisconnect(con)
 }
 
-# splits full_lf_id into lf_type and lf_id 
+# splits full_lf_id into lf_type and lf_id
 split_full_lf_id <- function(full_lf_id) {
   lf_type = gsub('[0-9]+', '', full_lf_id)
   lf_id = gsub('[^0-9]', '', full_lf_id)
-  
+
   return(list(lf_type = lf_type, lf_id = lf_id))
 }
 
@@ -101,68 +103,77 @@ split_full_lf_id <- function(full_lf_id) {
 # returns a list of loss storms we have data on
 get_all_loss_storms <- function() {
   query <- view.all_loss_storms
-  
+
   result <- query %>% collect()
-  
+
   return(result)
 }
 
 # returns a list of latest normalized losses
 get_latest_aggregate_losses <- function() {
   query <- view.all_normalized_losses
-  
+
   result <- query %>% collect()
-  
+
   return(result)
 }
 
 # returns unique lf ids for a storm
 get_unique_lf_ids <- function(storm) {
-  query <- view.all_loss_landfalls %>% 
+  query <- view.all_loss_landfalls %>%
     filter(
       storm_basin == storm$storm_basin,
       storm_year == storm$storm_year,
       storm_name == storm$storm_name
-    ) %>% 
+    ) %>%
     distinct(
       full_lf_id,
       .keep_all = T
-    ) %>% 
+    ) %>%
     arrange(
       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()
-  
+
   return(result)
 }
 
 # returns normalized mmh/mmp indexes and costs over time by landfall
 get_normalized_cost_index <- function(storm, full_lf_id) {
   lf_id_parts <- split_full_lf_id(full_lf_id)
-  
+
-  query <- view.yearly_normalized_losses %>% 
+  query <- view.yearly_normalized_losses %>%
     filter(
       storm_basin == storm$storm_basin,
       storm_year == storm$storm_year,
       storm_name == storm$storm_name,
       lf_type == lf_id_parts$lf_type,
       lf_id == lf_id_parts$lf_id
-    ) %>% 
+    ) %>%
     select(
-      normalization_year, mmh, mmp
+      normalization_year,
+      mmh,
+      mmp
     )
-  
+
   result <- query %>% collect()
-  
+
   return(result)
 }
 
 test_query <- function() {
-  result <- dbGetQuery(con, "WITH yearly_totals AS (
+  result <- dbGetQuery(
+    con,
+    "WITH yearly_totals AS (
     SELECT 
         year,
         SUM(population) as total_population,
@@ -188,63 +199,71 @@ 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 %>% 
+  result <- result %>%
     mutate(
       year = as.Date(paste0(year, "-01-01"))
-    ) %>% 
+    ) %>%
     select(
-      year, population_index, housing_index
+      year,
+      population_index,
+      housing_index
     )
-  
+
-  result_ts <- result %>% 
+  result_ts <- result %>%
-    select(-year) %>% 
+    select(-year) %>%
     xts(order.by = result$year)
 
   return(result_ts)
 }
-  
+
 # returns normalized mmh/mmp indexes and costs over time by storm
 get_all_normalized_cost_index <- function(storm) {
-  query <- view.yearly_normalized_losses %>% 
+  query <- view.yearly_normalized_losses %>%
     filter(
       storm_basin == storm$storm_basin,
       storm_year == storm$storm_year,
       storm_name == storm$storm_name
-    ) %>% 
+    ) %>%
     mutate(
       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()
-  
+
   return(result)
 }
 
 # returns landfalls and data at landfall from HURDAT
 get_hurdat_landfalls <- function(storm) {
-  query <- view.hurdat_track %>% 
+  query <- view.hurdat_track %>%
     filter(
       storm_basin == storm$storm_basin,
       storm_year == storm$storm_year,
       storm_name == storm$storm_name,
       record_identifier == "L"
-    ) %>% 
+    ) %>%
     select(
-      datetime, 
+      datetime,
       lon,
       lat,
       rmw,
       pressure,
       windspeed
     )
-  
+
   result <- query %>% collect()
-  
+
   #return(
   #  list(
   #    data = result,
@@ -253,31 +272,31 @@ get_hurdat_landfalls <- function(storm) {
   #    timestamp = Sys.time()
   #  )
   #)
-  
+
   return(result)
 }
 
 # returns all tracked storm conus landfalls
 get_all_conus_landfalls <- function() {
   query <- view.all_conus_landfalls
-  
+
   result <- query %>% collect()
-  
+
   return(result)
 }
 
 # returns all storm tracks from HURDAT
 get_all_hurdat_tracks <- function() {
   query <- view.all_loss_storms_tracks
-  
+
   result <- query %>% collect()
-  
+
   return(result)
 }
 
 # returns storm track from HURDAT
 get_hurdat_track <- function(storm) {
-  query <- view.hurdat_track %>% 
+  query <- view.hurdat_track %>%
     filter(
       storm_basin == storm$storm_basin,
       storm_year == storm$storm_year,
@@ -289,13 +308,13 @@ get_hurdat_track <- function(storm) {
       lon,
       lat,
       rmw,
-      pressure, 
+      pressure,
       windspeed,
       record_identifier,
       rmw_meters
     )
-  
+
-  result <- query %>% 
+  result <- query %>%
     collect() %>%
     mutate(
       formatted_datetime = paste0(
@@ -312,51 +331,53 @@ get_hurdat_track <- function(storm) {
       lon,
       lat,
       rmw,
-      pressure, 
+      pressure,
       windspeed,
       record_identifier,
       rmw_meters
     )
-  
+
   return(result)
 }
 
 # returns normalized population and housing growth by county with geometry
 get_normalized_metric_growth <- function(storm, full_lf_id) {
   lf_id_parts <- split_full_lf_id(full_lf_id)
-  
+
-  affected_counties <- gis.affected_area_landfalls %>% 
+  affected_counties <- gis.affected_area_landfalls %>%
     filter(
       storm_basin == storm$storm_basin,
       storm_year == storm$storm_year,
       storm_name == storm$storm_name,
       lf_type == lf_id_parts$lf_type,
       lf_id == lf_id_parts$lf_id
-    ) %>% 
+    ) %>%
     select(state_fips, county_fips)
-  
+
-  baseline_metrics <- metrics.pop_and_housing %>% 
+  baseline_metrics <- metrics.pop_and_housing %>%
     filter(
       year == storm$storm_year
-    ) %>% 
+    ) %>%
-    inner_join(affected_counties, by = c("state_fips", "county_fips")) %>% 
+    inner_join(affected_counties, by = c("state_fips", "county_fips")) %>%
     select(
       state_fips,
       county_fips,
       baseline_population = population,
       baseline_housing = housing_units
-  )
+    )
-  
+
-  normalized_metrics <- metrics.pop_and_housing %>% 
+  normalized_metrics <- metrics.pop_and_housing %>%
     filter(
       year >= storm$storm_year
-    ) %>% 
+    ) %>%
-    inner_join(affected_counties, by = c("state_fips", "county_fips")) %>% 
+    inner_join(affected_counties, by = c("state_fips", "county_fips")) %>%
-    inner_join(baseline_metrics, 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_population = as.numeric(population) /
-      normalized_housing = as.numeric(housing_units) / as.numeric(baseline_housing)
+        as.numeric(baseline_population),
-    ) %>% 
+      normalized_housing = as.numeric(housing_units) /
+        as.numeric(baseline_housing)
+    ) %>%
     select(
       state_fips,
       county_fips,
@@ -366,12 +387,17 @@ get_normalized_metric_growth <- function(storm, full_lf_id) {
       normalized_population,
       normalized_housing
     )
-  
+
-  query <- normalized_metrics %>% 
+  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,
       county_fips,
@@ -381,17 +407,17 @@ get_normalized_metric_growth <- function(storm, full_lf_id) {
       normalized_population,
       normalized_housing,
       geom_wkt
-    ) %>% 
+    ) %>%
     arrange(state_fips, county_fips, year)
-  
+
-  #query <- normalized_metrics %>% 
+  #query <- normalized_metrics %>%
   #  inner_join(
   #    public.counties,
-  #    by = c("state_fips" = "statefp", "county_fips" = "countyfp") 
+  #    by = c("state_fips" = "statefp", "county_fips" = "countyfp")
-  #  ) %>% 
+  #  ) %>%
   #  mutate(
   #    geom_wkt = sql("ST_AsText(ST_Transform(geom, 4326))")
-  #  ) %>% 
+  #  ) %>%
   #  select(
   #    state_fips,
   #    county_fips,
@@ -401,10 +427,10 @@ get_normalized_metric_growth <- function(storm, full_lf_id) {
   #    normalized_population,
   #    normalized_housing,
   #    geom_wkt
-  #  ) %>% 
+  #  ) %>%
   #  arrange(state_fips, county_fips, year)
-  
+
   result <- query %>% collect()
-  
+
   return(result)
 }