Merge branch 'add-lang' into 'main'

Add Spanish translation See merge request wma/vizlab/vulnerability-indicators!66
DOI-USGS · Sep 10, 2024 · c834bab · c834bab
2 parents 9524d14 + 7816330
commit c834bab
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Showing 52 changed files with 3,276 additions and 637 deletions.
diff --git a/0_config.R b/0_config.R
@@ -20,13 +20,13 @@ p0_targets <- list(
     # Create a tibble to define color palette for dimensions
     p0_viz_config_pal,
     tibble(
-      demographic_characteristics = "#092836",
-      exposure = "#FFA601",
-      health = "#EE5775",
-      land_tenure = "#1C695E", 
-      living_conditions = "#7A5195",
-      risk_perception = "#FF774A",
-      socioeconomic_status = "#2A468F"
+      demographic_characteristics = "#092734",
+      exposure = "#FFA600",
+      health = "#4B1B1B",
+      land_tenure = "#1B695F", 
+      living_conditions = "#4BAFA3",
+      risk_perception = "#365EB5",
+      socioeconomic_status = "#FF734D"
     )
   )
 )  
diff --git a/2_process.R b/2_process.R
@@ -70,6 +70,11 @@ p2_targets <- list(
                # distinct(determinant, .keep_all = TRUE) |> 
                readr::write_csv('public/determinant_uncertainty.csv')
              ),
+tar_target(p2_unc_determinant_json,
+           read_csv(p2_unc_agg_summary_csv) |>
+             toJSON(pretty = TRUE) |>
+             write("public/determinant_uncertainty.json")
+           ),
 tar_target(`p2_unc_agg_summary_ind_csv`,
            p2_unc_agg_ind_summary |>
              left_join(p2_top_trend_ind_stats) |>
@@ -103,9 +108,13 @@ tar_target(`p2_unc_agg_summary_ind_csv`,
                   "B03001_003", "B01001_002", "B01001_026")
              ),
   tar_target(p2_census_acs5_data,
-             get_census_data(geography = 'county', variable = p2_census_acs5_layers,
-                             states = p1_census_states, year = 2022, proj = p1_proj, 
-                             survey_var = "acs5",  percent_rename = FALSE),
+             get_census_data(geography = 'county', 
+                             variable = p2_census_acs5_layers,
+                             states = p1_census_states, 
+                             year = 2022, 
+                             proj = p1_proj, 
+                             survey_var = "acs5",  
+                             percent_rename = FALSE),
              pattern = map(p2_census_acs5_layers),
              iteration = "list"
              ),
@@ -131,19 +140,27 @@ tar_target(`p2_unc_agg_summary_ind_csv`,
 tar_target(p2_census_acs5sub_age_layers,
              c("S0101_C02_022", "S0101_C02_023", "S0101_C02_024", "S0101_C02_028")),
 tar_target(p2_census_acs5sub_age_data,
-           get_census_data(geography = 'county', variable = p2_census_acs5sub_age_layers,
-                           states = p1_census_states, year = 2022, proj = p1_proj,
-                           survey_var = "acs5",  percent_rename = TRUE),
+           get_census_data(geography = 'county', 
+                           variable = p2_census_acs5sub_age_layers,
+                           states = p1_census_states, 
+                           year = 2022, 
+                           proj = p1_proj,
+                           survey_var = "acs5",  
+                           percent_rename = TRUE),
            pattern = map(p2_census_acs5sub_age_layers),
            iteration = "list"),
 # income related variables 
 # S1901_C01_014 = Estimate!!Households!!PERCENT ALLOCATED!!Household income in the past 12 months
 tar_target(p2_census_acs5sub_income_layers,
            c("S1901_C01_014")),
 tar_target(p2_census_acs5sub_income_data,
-           get_census_data(geography = 'county', variable = p2_census_acs5sub_income_layers,
-                           states = p1_census_states, year = 2022, proj = p1_proj, 
-                           survey_var = "acs5", percent_rename = TRUE),
+           get_census_data(geography = 'county', 
+                           variable = p2_census_acs5sub_income_layers,
+                           states = p1_census_states, 
+                           year = 2022, 
+                           proj = p1_proj, 
+                           survey_var = "acs5", 
+                           percent_rename = TRUE),
            pattern = map(p2_census_acs5sub_income_layers),
            iteration = "list"),
 # education related variables 
@@ -152,9 +169,13 @@ tar_target(p2_census_acs5sub_income_data,
 tar_target(p2_census_acs5sub_education_layers,
            c("S1501_C01_003", "S1501_C01_009")),
 tar_target(p2_census_acs5sub_education_data,
-           get_census_data(geography = 'county', variable = p2_census_acs5sub_education_layers,
-                           states = p1_census_states, year = 2022, proj = p1_proj, 
-                           survey_var = "acs5", percent_rename = FALSE),
+           get_census_data(geography = 'county', 
+                           variable = p2_census_acs5sub_education_layers,
+                           states = p1_census_states, 
+                           year = 2022, 
+                           proj = p1_proj, 
+                           survey_var = "acs5", 
+                           percent_rename = FALSE),
            pattern = map(p2_census_acs5sub_education_layers),
            iteration = "list"),
 
@@ -164,9 +185,13 @@ tar_target(p2_census_acs5sub_education_data,
 tar_target(p2_census_acs5_household_layers,
            c("B25010_001", "B25064_001")),
 tar_target(p2_census_acs5sub_household_data,
-           get_census_data(geography = 'county', variable = p2_census_acs5_household_layers,
-                           states = p1_census_states, year = 2022, proj = p1_proj, 
-                           survey_var = "acs5", percent_rename = FALSE),
+           get_census_data(geography = 'county', 
+                           variable = p2_census_acs5_household_layers,
+                           states = p1_census_states, 
+                           year = 2022, 
+                           proj = p1_proj, 
+                           survey_var = "acs5", 
+                           percent_rename = FALSE),
            pattern = map(p2_census_acs5_household_layers),
            iteration = "list"),
 # percent households variable
@@ -193,9 +218,13 @@ tar_target(p2_census_acs5profile_household_sf,
 tar_target(p2_census_acs5_income_by_race_layers,
            c("B19013A_001", "B19013B_001", "B19013C_001", "B19013D_001", "B19013E_001", "B19013I_001")),
 tar_target(p2_census_acs5sub_income_by_race_data,
-           get_census_data(geography = 'county', variable = p2_census_acs5_income_by_race_layers,
-                           states = p1_census_states, year = 2022, proj = p1_proj, 
-                           survey_var = "acs5", percent_rename = FALSE),
+           get_census_data(geography = 'county', 
+                           variable = p2_census_acs5_income_by_race_layers,
+                           states = p1_census_states, 
+                           year = 2022, 
+                           proj = p1_proj, 
+                           survey_var = "acs5", 
+                           percent_rename = FALSE),
            pattern = map(p2_census_acs5_income_by_race_layers),
            iteration = "list"),
 # Disability status
@@ -204,9 +233,13 @@ tar_target(p2_census_acs5sub_income_by_race_data,
 tar_target(p2_census_acs5_disability_layers,
            c("S1810_C03_001", "S1810_C02_001")),
 tar_target(p2_census_acs5sub_disability_data,
-           get_census_data(geography = 'county', variable = p2_census_acs5_disability_layers,
-                           states = p1_census_states, year = 2022, proj = p1_proj, 
-                           survey_var = "acs5", percent_rename = FALSE),
+           get_census_data(geography = 'county', 
+                           variable = p2_census_acs5_disability_layers,
+                           states = p1_census_states, 
+                           year = 2022, 
+                           proj = p1_proj, 
+                           survey_var = "acs5", 
+                           percent_rename = FALSE),
            pattern = map(p2_census_acs5_disability_layers),
            iteration = "list")
 )
diff --git a/3_visualize.R b/3_visualize.R
@@ -4,28 +4,29 @@ source('3_visualize/src/plot_utils.R')
 # For case studies approach: select one demographic characteristic (hispanic/latino), 
 p3_targets <- list(
   tar_target(
-    p3_med_income_png,
+    p3_med_income_png_en,
     plot_census_map(
       census_data = p2_perc_census_acs5_layers_sf[[2]],
       lim_vals = c(0, 155000),
       percent_leg = FALSE,
       dollar_leg = TRUE,
       var = 'estimate',
       conus_sf = p1_conus_sf,
-      outfile_path = "3_visualize/out/med_income_census_2022.png",
+      outfile_path = "3_visualize/out/med_income_census_2022_en.png",
       leg_title = "Median household income, 2022",
       viz_config_df = p0_viz_config_df,
       viz_config_pal = p0_viz_config_pal$socioeconomic_status,
       width = p0_viz_config_df$width_desktop,
       height = p0_viz_config_df$height_desktop,
       font_size  = p0_viz_config_df$font_size_desktop,
       barwidth = 20,
-      barheight = 1
+      barheight = 1,
+      low_ramp_col = "#fef1f1"
     ),
     format = "file"
   ),
   tar_target(
-    p3_perc_latino_png,
+    p3_perc_latino_png_en,
     plot_census_map(
       census_data = p2_perc_census_acs5_layers_sf[[4]],
       percent_leg = TRUE,
@@ -34,62 +35,65 @@ p3_targets <- list(
       break_vals = c(0, 25, 50, 75, 100),
       var = 'percent',
       conus_sf = p1_conus_sf,
-      outfile_path = "3_visualize/out/perc_hispanic_census_2022.png",
+      outfile_path = "3_visualize/out/perc_hispanic_census_2022_en.png",
       leg_title = "Percent Hispanic, 2022",
       viz_config_df = p0_viz_config_df,
       viz_config_pal = p0_viz_config_pal$demographic_characteristics,
       width = p0_viz_config_df$width_desktop,
       height = p0_viz_config_df$height_desktop,
       font_size  = p0_viz_config_df$font_size_desktop,
       barwidth = 20,
-      barheight = 1
+      barheight = 1,
+      low_ramp_col = "#eef0ff"
     ),
     format = "file"
   ),
   tar_target(
-    p3_avg_household_size_png,
+    p3_avg_household_size_png_en,
     plot_census_map(
       census_data = p2_census_acs5sub_household_data[[1]],
       percent_leg = FALSE,
       dollar_leg = FALSE,
       lim_vals = c(1, 5),
       var = 'estimate',
       conus_sf = p1_conus_sf,
-      outfile_path = "3_visualize/out/avg_household_size_2022.png",
+      outfile_path = "3_visualize/out/avg_household_size_2022_en.png",
       leg_title = "Average household size, 2022",
       viz_config_df = p0_viz_config_df,
       viz_config_pal = p0_viz_config_pal$demographic_characteristics,
       width = p0_viz_config_df$width_desktop,
       height = p0_viz_config_df$height_desktop,
       font_size  = p0_viz_config_df$font_size_desktop,
       barwidth = 20,
-      barheight = 1
+      barheight = 1,
+      low_ramp_col = "#eef0ff"
     ),
     format = "file"
   ),
   tar_target(
-    p3_median_rent_png,
+    p3_median_rent_png_en,
     plot_census_map(
       census_data = p2_census_acs5sub_household_data[[2]],
       percent_leg = FALSE,
       dollar_leg = TRUE, 
       lim_vals = c(0, 3000),
       var = 'estimate',
       conus_sf = p1_conus_sf,
-      outfile_path = "3_visualize/out/median_rent_2022.png",
+      outfile_path = "3_visualize/out/median_rent_2022_en.png",
       leg_title = "Median gross rent, 2022",
       viz_config_df = p0_viz_config_df,
       viz_config_pal = p0_viz_config_pal$land_tenure,
       width = p0_viz_config_df$width_desktop,
       height = p0_viz_config_df$height_desktop,
       font_size  = p0_viz_config_df$font_size_desktop,
       barwidth = 20,
-      barheight = 1
+      barheight = 1,
+      low_ramp_col = "#ebfaf8"
     ),
     format = "file"
   ),
   tar_target(
-  p3_perc_disable_png,
+  p3_perc_disable_png_en,
   plot_census_map(
     census_data = p2_census_acs5sub_disability_data[[1]],
     percent_leg = TRUE,
@@ -98,16 +102,130 @@ p3_targets <- list(
     break_vals = c(0, 10, 20, 30, 40, 50),
     var = 'estimate',
     conus_sf = p1_conus_sf,
-    outfile_path = "3_visualize/out/perc_disabled_census_2022.png",
+    outfile_path = "3_visualize/out/perc_disabled_census_2022_en.png",
     leg_title = "Percent disabled, 2022",
     viz_config_df = p0_viz_config_df,
     viz_config_pal = p0_viz_config_pal$demographic_characteristics,
     width = p0_viz_config_df$width_desktop,
     height = p0_viz_config_df$height_desktop,
     font_size  = p0_viz_config_df$font_size_desktop,
     barwidth = 20,
-    barheight = 1
+    barheight = 1,
+    low_ramp_col = "#eef0ff"
     ),
   format = "file"
+),
+# Spanish version's of maps -----------------------------------------------
+tar_target(
+  p3_med_income_png_es,
+  plot_census_map(
+    census_data = p2_perc_census_acs5_layers_sf[[2]],
+    lim_vals = c(0, 155000),
+    percent_leg = FALSE,
+    dollar_leg = TRUE,
+    var = 'estimate',
+    conus_sf = p1_conus_sf,
+    outfile_path = "3_visualize/out/med_income_census_2022_es.png",
+    leg_title = "Media de ingresos por hogar, 2022",
+    viz_config_df = p0_viz_config_df,
+    viz_config_pal = p0_viz_config_pal$socioeconomic_status,
+    width = p0_viz_config_df$width_desktop,
+    height = p0_viz_config_df$height_desktop,
+    font_size  = p0_viz_config_df$font_size_desktop,
+    barwidth = 20,
+    barheight = 1,
+    low_ramp_col = "#fef1f1"
+  ),
+  format = "file"
+),
+tar_target(
+  p3_perc_latino_png_es,
+  plot_census_map(
+    census_data = p2_perc_census_acs5_layers_sf[[4]],
+    percent_leg = TRUE,
+    dollar_leg = FALSE,
+    lim_vals = c(0, 100),
+    break_vals = c(0, 25, 50, 75, 100),
+    var = 'percent',
+    conus_sf = p1_conus_sf,
+    outfile_path = "3_visualize/out/perc_hispanic_census_2022_es.png",
+    leg_title = "Porcentaje de Hispanos, 2022",
+    viz_config_df = p0_viz_config_df,
+    viz_config_pal = p0_viz_config_pal$demographic_characteristics,
+    width = p0_viz_config_df$width_desktop,
+    height = p0_viz_config_df$height_desktop,
+    font_size  = p0_viz_config_df$font_size_desktop,
+    barwidth = 20,
+    barheight = 1,
+    low_ramp_col = "#eef0ff"
+  ),
+  format = "file"
+),
+tar_target(
+  p3_avg_household_size_png_es,
+  plot_census_map(
+    census_data = p2_census_acs5sub_household_data[[1]],
+    percent_leg = FALSE,
+    dollar_leg = FALSE,
+    lim_vals = c(1, 5),
+    var = 'estimate',
+    conus_sf = p1_conus_sf,
+    outfile_path = "3_visualize/out/avg_household_size_2022_es.png",
+    leg_title = "Tamaño promedio de los hogares, 2022",
+    viz_config_df = p0_viz_config_df,
+    viz_config_pal = p0_viz_config_pal$demographic_characteristics,
+    width = p0_viz_config_df$width_desktop,
+    height = p0_viz_config_df$height_desktop,
+    font_size  = p0_viz_config_df$font_size_desktop,
+    barwidth = 20,
+    barheight = 1,
+    low_ramp_col = "#eef0ff"
+  ),
+  format = "file"
+),
+tar_target(
+  p3_median_rent_png_es,
+  plot_census_map(
+    census_data = p2_census_acs5sub_household_data[[2]],
+    percent_leg = FALSE,
+    dollar_leg = TRUE, 
+    lim_vals = c(0, 3000),
+    var = 'estimate',
+    conus_sf = p1_conus_sf,
+    outfile_path = "3_visualize/out/median_rent_2022_es.png",
+    leg_title = "Alquiler bruto medio, 2022",
+    viz_config_df = p0_viz_config_df,
+    viz_config_pal = p0_viz_config_pal$land_tenure,
+    width = p0_viz_config_df$width_desktop,
+    height = p0_viz_config_df$height_desktop,
+    font_size  = p0_viz_config_df$font_size_desktop,
+    barwidth = 20,
+    barheight = 1,
+    low_ramp_col = "#ebfaf8"
+  ),
+  format = "file"
+),
+tar_target(
+  p3_perc_disable_png_es,
+  plot_census_map(
+    census_data = p2_census_acs5sub_disability_data[[1]],
+    percent_leg = TRUE,
+    dollar_leg = FALSE,
+    lim_vals = c(0, 50),
+    break_vals = c(0, 10, 20, 30, 40, 50),
+    var = 'estimate',
+    conus_sf = p1_conus_sf,
+    outfile_path = "3_visualize/out/perc_disabled_census_2022_es.png",
+    leg_title = "Porcentaje de discapacitados, 2022",
+    viz_config_df = p0_viz_config_df,
+    viz_config_pal = p0_viz_config_pal$demographic_characteristics,
+    width = p0_viz_config_df$width_desktop,
+    height = p0_viz_config_df$height_desktop,
+    font_size  = p0_viz_config_df$font_size_desktop,
+    barwidth = 20,
+    barheight = 1,
+    low_ramp_col = "#eef0ff"
+  ),
+  format = "file"
 )
 )