Split rendering code for counties and places and update .gitignore

.gitignore

@@ -10,5 +10,5 @@ rfi-applicants-brief.xlsx
 rfi-applicants.xlsx
 
 /.quarto/
-factsheets/999*
-factsheets/998*
+factsheets/999_county-pages/
+factsheets/998_place-pages/

R/create_standard_county_df.R

@@ -22,7 +22,7 @@ if(!file.exists("data/999_all-counties.Rda")) {
 
   geoid_lst <- as.list(prepped_counties$geoid)
 
-  prepped_counties <- tibble(filename = "index.html", 
+  prepped_counties <- tibble(filename = "index-county.html", 
                              params = map(geoid_lst, 
                                           ~list(state_county = ., 
                                                 state_title = FALSE)

R/create_standard_place_df.R

@@ -0,0 +1,39 @@
+#Code to create data for standard factsheets for places
+
+#Gabe Morrison
+
+#2023-03-15
+
+library(tidyverse)
+library(tidycensus)
+
+if(!file.exists("data/998_all-places.Rda")) {
+
+  place_geoids <- read_csv(here("mobility-metrics", "place-populations.csv")) %>%
+    filter(year == 2020)
+
+  prepped_places <- place_geoids %>%  
+    mutate(GEOID = paste0(state, place)) %>%
+    select(geoid = GEOID) %>%
+    mutate(state_title = list(state_title = FALSE),
+           geoid = str_c(geoid, ";"),
+           geoid = strsplit(geoid, ";")
+    )
+
+  geoid_lst <- as.list(prepped_places$geoid)
+
+  prepped_places <- tibble(filename = "index.html", 
+                             params = map(geoid_lst, 
+                                          ~list(state_place = ., 
+                                                state_title = FALSE)
+                                          ),
+                             dir_name = str_c("factsheets/998_place-pages/", geoid_lst, "/")
+  )
+
+  save(prepped_places, file = "data/998_all-places.Rda")
+
+} else {
+
+  load("data/998_all-places.Rda")
+
+}

R/load_data.R

@@ -24,14 +24,6 @@ load_data <- function() {
       )
   ) %>%
     left_join(county_names, by = c("state", "county")) %>%
-    mutate( 
-      share_debt_col_ub = 0,
-      share_debt_col_lb = 1,
-
-      digital_access = 0.1234,
-      digital_access_quality = 3
-
-    ) %>%
     prep_data()
 
   data_years <- read_csv(
@@ -47,14 +39,6 @@ load_data <- function() {
       .default = col_double()
     )
   ) %>%
-    mutate(
-      election_turnout = 0.1234,
-      election_turnout_quality = 3,
-
-      digital_access = 0.1234,
-      digital_access_quality = 3
-
-    ) %>%
     left_join(county_names, by = c("state", "county")) %>%
     prep_data()
 

R/load_place_data.R

@@ -0,0 +1,76 @@
+load_place_data <- function() {
+
+  my_col_type <- cols(
+    state = col_character(), 
+    county = col_character()
+  )
+
+  county_names <- read_csv(here("mobility-metrics", "old", "01_mobility-metrics.csv")) %>%
+    select(state, county, state_name, county_name) %>%
+    distinct()
+
+  data_recent <- read_csv(
+    here("mobility-metrics", "00_mobility-metrics_recent.csv"), 
+    col_type = 
+      cols(
+        state = col_character(),
+        county = col_character(),
+        ratio_black_nh_house_value_households = col_character(),
+        ratio_hispanic_house_value_households = col_character(),
+        ratio_other_nh_house_value_households = col_character(),
+        ratio_white_nh_house_value_households = col_character(),
+        ratio_population_pc_physician = col_character(),
+        .default = col_double()
+      )
+  ) %>%
+    left_join(county_names, by = c("state", "county")) %>%
+    prep_data()
+
+  data_years <- read_csv(
+    here("mobility-metrics","00_mobility-metrics_longitudinal.csv"),
+    col_type = cols(
+      state = col_character(),
+      county = col_character(),
+      ratio_black_nh_house_value_households = col_character(),
+      ratio_hispanic_house_value_households = col_character(),
+      ratio_other_nh_house_value_households = col_character(),
+      ratio_white_nh_house_value_households = col_character(),
+      ratio_population_pc_physician = col_character(),
+      .default = col_double()
+    )
+  ) %>%
+    left_join(county_names, by = c("state", "county")) %>%
+    prep_data()
+
+  data_race_ethnicity <- read_csv(
+    here("mobility-metrics", "01_mobility-metrics_race-ethnicity_recent.csv"), 
+    col_types = my_col_type
+  ) %>%
+    left_join(county_names, by = c("state", "county")) %>%
+    prep_data()
+
+  data_race <- read_csv(
+    here("mobility-metrics", "02_mobility-metrics_race_recent.csv"), 
+    col_types = my_col_type
+  ) %>%
+    left_join(county_names, by = c("state", "county")) %>%
+    prep_data()
+
+  data_race_share <- read_csv(
+    here("mobility-metrics", "03_mobility-metrics_race-share_recent.csv"), 
+    col_types = my_col_type
+  ) %>%
+    left_join(county_names, by = c("state", "county")) %>%
+    prep_data()
+
+  data_list <- list(
+    recent = data_recent,
+    years = data_years,
+    race_ethnicity = data_race_ethnicity,
+    race = data_race,
+    race_share = data_race_share
+  )
+
+  return(data_list)
+
+}

R/quarto_render_wrapper.R

@@ -28,13 +28,13 @@ quarto_render_wrapper <- function(input, output_file, execute_params, dir_name)
   if (!file.exists(template_name)) {
 
     file.copy(
-      from = "index.qmd", 
+      from = input, 
       to = template_name
     )
 
   }
 
-  # copy index.qmd
+  # copy project yaml
   yaml_name <- paste0(dir_name, "_quarto.yml")
   if (!file.exists(yaml_name)) {
 
@@ -45,7 +45,7 @@ quarto_render_wrapper <- function(input, output_file, execute_params, dir_name)
 
   }
 
-  # copy description qmd
+  # copy description .qmd
   description_qmd_name <- paste0(dir_name, "description.qmd")
   if (!file.exists(description_qmd_name)) {
 
@@ -59,14 +59,28 @@ quarto_render_wrapper <- function(input, output_file, execute_params, dir_name)
   # copy description html
   description_html_name <- paste0(dir_name, "description.html")
   if (!file.exists(description_html_name)) {
-    
+
     file.copy(
-      from = "description.html", 
+      from = "description.html",
       to = description_html_name
     )
+
+  }
+
+  www_name <- paste0(dir_name, "www")
+  if (!file.exists(www_name)) {
+
+    dir.create(www_name)
+
+    file.copy(
+      from = "www", 
+      to = dir_name,
+      recursive = TRUE
+    )
 
   }
 
+
   quarto_render(
     input = template_name,
     output_file = output_file, 

R/render_pages.R

@@ -14,7 +14,7 @@
 #' 
 #' @returns pwalk implicitly returns the list inputted to it. The return objects
 #' are not used for this function, however.
-render_pages <- function(prepped_object, input = "index.qmd", workers = 1) {
+render_pages <- function(prepped_object, input, workers = 1) {
 
   future::plan(future::multisession, workers = workers)
 
@@ -33,7 +33,7 @@ render_pages <- function(prepped_object, input = "index.qmd", workers = 1) {
 
   prepped_object %>% 
     select(to = dir_name) %>% 
-    pwalk(file.copy, from = "www", recursive = TRUE, overwrite = TRUE) %>% 
+    #pwalk(file.copy, from = "www", recursive = TRUE, overwrite = TRUE) %>% 
     pwalk(file.copy, from = "site_libs",  recursive = TRUE, overwrite = TRUE) #%>% 
     #pwalk(file.copy, from = "description.html", overwrite = TRUE) 
 

create_standard_pages.R

@@ -11,18 +11,32 @@ library(quarto)
 library(tidycensus)
 
 source("R/create_standard_county_df.R")
+source("R/create_standard_place_df.R")
 source("R/quarto_render_wrapper.R")
 source("R/render_pages.R")
 
 #For testing:
-# small <- prepped_counties %>%
+small_county <- prepped_counties %>%
+  slice_head(n = 4)
+
+tictoc::tic()
+render_pages(prepped_object = small_county, workers = 4, input = "index-county.qmd")
+tictoc::toc()
+
+# small_place <- prepped_places %>%
 #   slice_head(n = 4)
 # 
 # tictoc::tic()
-# render_pages(prepped_object = small, workers = 4)
+# render_pages(prepped_object = small_place, workers = 4, input = "index-place.qmd")
 # tictoc::toc()
 
+
+
 #For actual run:
 tictoc::tic()
 render_pages(prepped_object = prepped_counties, workers = 96)
 tictoc::toc()
+
+tictoc::tic()
+render_pages(prepped_object = prepped_places, workers = 96)
+tictoc::toc()

description.qmd

@@ -461,7 +461,7 @@ This metric highlights racial and ethnic disparities in an important source of w
 
 <br> 
 
-## <span style="color:#8A9191">Pillar: Healthy Environment and Good Access to Healthcare</span>{#pillar-healthy-environment-and-access-to-good-health-care} 
+## <span style="color:#8A9191">Pillar: Healthy Environment and Access to Good Healthcare</span>{#pillar-healthy-environment-and-access-to-good-health-care} 
 
 ![](www/images/mobility-icons/mobility-infographic-health-icon.png){width=2in fig-align="center"}