Resolve merge conflicts to add new var names

.gitignore

@@ -10,3 +10,5 @@ rfi-applicants-brief.xlsx
 rfi-applicants.xlsx
 
 /.quarto/
+factsheets/999*
+factsheets/998*

R/load_data.R

@@ -0,0 +1,92 @@
+load_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")) %>%
+    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(
+    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()
+    )
+  ) %>%
+    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()
+
+  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/prep_data.R

@@ -1,20 +1,39 @@
 prep_data <- function(data) {
 
+  # Get a list of all variables that should be presented as percentage 
+  perc_vars <- c(
+    "share_debt_col", # % with debt
+    "share_homeless", # share homeless
+    "share_election_turnout", # % voting 
+    "rate_low_birth_weight", # % low birthweight
+    "share_white_nh_exposure",
+    "share_black_nh_exposure",
+    "share_hispanic_exposure",
+    "share_other_nh_exposure",
+    "share_poverty_exposure", # % in high poverty
+    "meps20_total", # student poverty concentration
+    "meps20_white", 
+    "meps20_black",
+    "meps20_hispanic", 
+    "share_in_preschool", # % in pre k 
+    "share_hs_degree", # % with HS degree
+    "share_employed"
+  )
+
+
   data <- data %>% 
-    mutate(state = str_pad(state, width = 2, side ="left", pad = "0"), #set max ub as 1 
-           county = str_pad(county, width = 3, side ="left", pad = "0"))
+    mutate(
+      state = str_pad(state, width = 2, side ="left", pad = "0"), 
+      county = str_pad(county, width = 3, side ="left", pad = "0"),
+      fips = paste0(state, county)
+    )
 
   data <- data %>% 
-    unite("fips", state, county, sep = "", remove = FALSE) %>% 
-    # group_by(full_fips) %>% 
-    # fill(state_name, .direction = "updown") %>% 
-    # fill(county_name, .direction = "updown") %>% 
-    # ungroup() %>% 
-    unite("state_county", county_name, state_name, sep = ", ", remove = FALSE) %>% 
     filter(fips %in% params$state_county) %>% 
     arrange(factor(fips, levels = params$state_county)) %>% 
+    unite("state_county", county_name, state_name, sep = ", ", remove = FALSE) %>% 
     mutate(state_county = gsub("County", "", state_county)) %>% 
-    mutate(state_county = rm_white_comma(state_county))
+    mutate(state_county = qdapRegex::rm_white_comma(state_county))
 
   data <- data %>%
     mutate(state_county = str_to_title(state_county))
@@ -28,18 +47,16 @@ prep_data <- function(data) {
   # filter to get the ones only existing in the data 
   perc_vars_in_data <- all_perc_vars[(all_perc_vars %in% colnames(data))]
 
-
   numeric_vars_one_digit <- data %>% 
     select(
-      -matches("average_to_living_wage_ratio"),
+      -matches("ratio_average_to_living_wage"),
+      -matches("share_desc_rep"), 
       -fips, 
       -matches("_quality"),
       -matches("year"),
-      -starts_with("learning_rate"), 
+      -starts_with("rate_learning"), 
       -all_of(perc_vars_in_data),
-      -starts_with("share_burdened"),
       -starts_with("pctl"),
-      -any_of(c("asian_other_pop", "black_nonhispanic_pop", "hispanic_pop", "white_nonhispanic_pop"))
     ) %>%
     select_if(is.numeric) %>% 
     names()
@@ -48,30 +65,37 @@ prep_data <- function(data) {
   data <- data %>%
     mutate_at(vars(ends_with("_quality")),
               function(x) recode(x, `1` = "Strong", `2` = "Marginal", `3` = "Weak")) %>% 
+    mutate(
+      across(
+        any_of(c("rate_learning", "rate_learning_lb", "rate_learning_ub")),
+        .fns = ~round(.x, digits = 2)
+      )
+    ) %>%
     # multiple affordable housing variables by 100
-    mutate_at(vars(matches("share_affordable_")),  
+    mutate_at(vars(matches("share_affordable_[1-9]")),  
               function(x) x*100) %>%
     # update ub for share_hs_degree to 1
     mutate_at(vars(matches("share_hs_degree_ub")),     
               function(x) case_when(x > 1 ~ 1, TRUE  ~ x)) %>%
-    mutate_at(vars(any_of(c("asian_other_pop", "black_nonhispanic_pop", "hispanic_pop", "white_nonhispanic_pop"))),
+    mutate_at(vars(any_of(c("share_desc_rep_asian_other", "share_desc_rep_black_nonhispanic", "share_desc_rep_white_nonhispanic"))),
               function(x) paste0("__:", scales::percent(x))) %>%    
     # convert to percentage
     mutate_at(perc_vars_in_data,             
               function (x) scales::percent(x, accuracy = 0.1)) %>%
     mutate_at(numeric_vars_one_digit,
               function(x) {format(round(x, 1), big.mark=",", scientific=FALSE)}) %>%
-    mutate_at(vars(matches("average_to_living_wage_ratio")), funs(as.numeric)) %>%
-    mutate_at(vars(matches("average_to_living_wage_ratio")),
+    mutate_at(vars(matches("ratio_average_to_living_wage")), funs(as.numeric)) %>%
+    mutate_at(vars(matches("ratio_average_to_living_wage")),
               function(x) {format(round(x, 2), big.mark=",",scientific=FALSE)}) %>%
-    # mutate(na_quality = "NA")  %>%  #to be used for metrics where there is no quality variable %>%
     mutate_at(vars(matches("pctl"), -ends_with("_quality")), 
               function(x) scales::dollar(x, accuracy = 1)) %>%
     # remove whitespace in values  
     mutate_at(vars(-one_of(c("state_county", "subgroup"))),
               function(x) gsub("\\s+", "", x)) %>%  
-    mutate_at(vars(matches("crime_rate"), -ends_with("_quality")), 
-              function(x) gsub("\\..*","", x))
+    mutate_at(vars(matches("crime_violent_rate"), matches("crime_property_rate"), -ends_with("_quality")), 
+              function(x) gsub("\\..*","", x)) 
+
+
 
   return(data)
 

R/prep_pages.R

@@ -32,7 +32,7 @@ prep_pages <- function(url, output_directory, state_title = FALSE, bespoke = TRU
   app_list <- read_csv(url)
 
   # format app list 
-  app_list <- app_list %>% 
+  app_list <- app_list  %>%
     mutate(fips = stringr::str_pad(fips, width = 5, side = 'left', pad = '0')) %>% 
     mutate(full_list = case_when(!is.na(comparisons) ~ stringr::str_c(fips, comparisons, sep = ';'),
                                  TRUE ~ str_c(fips, sep = ';'))) %>% 

R/quarto_render_wrapper.R

@@ -14,7 +14,7 @@
 #' The function is not intended to return anything, but it writes htmls to the
 #'  output_file path 
 
-quarto_render_wrapper <- function(input, output_file, execute_params, dir_name){
+quarto_render_wrapper <- function(input, output_file, execute_params, dir_name) {
 
   # create directory
   if(!dir.exists(dir_name)){