3_visualize.R

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,
    plot_census_map(
      census_data = p2_perc_census_acs5_layers_sf[[2]],
      percent_leg = FALSE,
      var = 'estimate',
      conus_sf = p1_conus_sf,
      outfile_path = "3_visualize/out/med_income_census_2022.png",
      leg_title = "Median household income, 2022",
      viz_config_df = p0_viz_config_df,
      viz_config_pal = p0_viz_config_pal$pal_positive
    ),
    format = "file"
  ),
  tar_target(
    p3_perc_household_income_map_png,
    plot_census_map(
      census_data = p2_census_acs5sub_income_data[[1]],
      percent_leg = TRUE,
      var = 'percent', 
      conus_sf = p1_conus_sf, 
      outfile_path = "3_visualize/out/perc_household_income_2022.png",
      leg_title =  "Percent allocated household income in the past 12 months, 2022",
      viz_config_df = p0_viz_config_df,
      viz_config_pal = p0_viz_config_pal$pal_positive
    ),
    format = "file"
  ),
  # 
  # tar_target(
  #   p3_tot_black_png,
  #   plot_census_map(
  #     census_data = p2_perc_census_acs5_layers_sf[[3]],
  #     percent_leg = FALSE,
  #     var = 'estimate',
  #     conus_sf = p1_conus_sf, 
  #     outfile_path = "3_visualize/out/tot_black_census_2022.png",
  #     leg_title = "Total Black, 2022",
  #     viz_config_df = viz_config_df,
  #     viz_config_pal = viz_config_pal$pal_positive
  #   ),
  #   format = "file"
  # ),
  # tar_target(
  #   p3_perc_black_png,
  #   plot_census_map(
  #     census_data = p2_perc_census_acs5_layers_sf[[3]],
  #     percent_leg = TRUE,
  #     var = 'percent',
  #     conus_sf = p1_conus_sf, 
  #     outfile_path = "3_visualize/out/perc_black_census_2022.png",
  #     leg_title = "Percent Black, 2022",
  #     viz_config_df = viz_config_df,
  #     viz_config_pal = viz_config_pal$pal_positive
  #   ),
  #   format = "file"
  # ),
  
  tar_target(
    p3_tot_latino_png,
    plot_census_map(
      census_data = p2_perc_census_acs5_layers_sf[[4]],
      percent_leg = FALSE,
      var = 'estimate',
      conus_sf = p1_conus_sf, 
      outfile_path = "3_visualize/out/tot_latino_census_2022.png",
      leg_title =  "Total Latino, 2022",
      viz_config_df = p0_viz_config_df,
      viz_config_pal = p0_viz_config_pal$pal_positive
    ),
    format = "file"
  ),
  
  tar_target(
    p3_perc_latino_png,
    plot_census_map(
      census_data = p2_perc_census_acs5_layers_sf[[4]],
      percent_leg = TRUE,
      var = 'percent',
      conus_sf = p1_conus_sf, 
      outfile_path = "3_visualize/out/perc_latino_census_2022.png",
      leg_title = "Percent Latino, 2022",
      viz_config_df = p0_viz_config_df,
      viz_config_pal = p0_viz_config_pal$pal_positive
    ),
    format = "file"
  ),
  
  # tar_target(
  #   p3_tot_male_png,
  #   plot_census_map(
  #     census_data = p2_perc_census_acs5_layers_sf[[5]],
  #     percent_leg = FALSE,
  #     var = 'estimate',
  #     conus_sf = p1_conus_sf, 
  #     outfile_path = "3_visualize/out/tot_male_census_2022.png",
  #     leg_title =  "Total Male, 2022",
  #     viz_config_df = viz_config_df
  #   ),
  #   format = "file"
  # ),
  # 
  # tar_target(
  #   p3_tot_female_png,
  #   plot_census_map(
  #     census_data = p2_perc_census_acs5_layers_sf[[6]],
  #     percent_leg = FALSE,
  #     var = 'estimate', 
  #     conus_sf = p1_conus_sf, 
  #     outfile_path = "3_visualize/out/tot_female_census_2022.png",
  #     leg_title =  "Total Female, 2022",
  #     viz_config_df = viz_config_df,
  #     viz_config_pal = viz_config_pal$pal_positive
  #   ),
  #   format = "file"
  # ),
  
  # #  Disaggregated maps
  # # percent age maps 
  # tar_target(
  #   p3_perc_under_18_map_png,
  #   plot_census_map(
  #     census_data = p2_census_acs5sub_age_data[[1]],
  #     percent_leg = TRUE,
  #     var = 'percent', 
  #     conus_sf = p1_conus_sf, 
  #     outfile_path = "3_visualize/out/perc_pop_under_18_2022.png",
  #     leg_title =  "Percent of Total Poulation Under 18, 2022",
  #     viz_config_df = viz_config_df
  #   ),
  #   format = "file"
  # ),
  # tar_target(
  #   p3_perc_18_24_map_png,
  #   plot_census_map(
  #     census_data = p2_census_acs5sub_age_data[[2]],
  #     percent_leg = TRUE,
  #     var = 'percent', 
  #     conus_sf = p1_conus_sf, 
  #     outfile_path = "3_visualize/out/perc_pop_under_18-24_2022.png",
  #     leg_title =  "Percent of Total Poulation 18-24 years, 2022",
  #     viz_config_df = viz_config_df
  #   ),
  #   format = "file"
  # ),
  # tar_target(
  #   p3_perc_15_44_map_png,
  #   plot_census_map(
  #     census_data = p2_census_acs5sub_age_data[[3]],
  #     percent_leg = TRUE,
  #     var = 'percent', 
  #     conus_sf = p1_conus_sf, 
  #     outfile_path = "3_visualize/out/perc_pop_under_15-44_2022.png",
  #     leg_title =  "Percent of Total Poulation 15-44 years, 2022",
  #     viz_config_df = viz_config_df
  #   ),
  #   format = "file"
  # ),
  # tar_target(
  #   p3_perc_60_plus_map_png,
  #   plot_census_map(
  #     census_data = p2_census_acs5sub_age_data[[4]],
  #     percent_leg = TRUE,
  #     var = 'percent', 
  #     conus_sf = p1_conus_sf, 
  #     outfile_path = "3_visualize/out/perc_pop_under_60_plus_2022.png",
  #     leg_title =  "Percent of Total Poulation 60 years & plus, 2022",
  #     viz_config_df = viz_config_df
  #   ),
  #   format = "file"
  # ),
  # # education related map
  # tar_target(
  #   p3_tot_18_24_highschool_education_map_png,
  #   plot_census_map(
  #     census_data = p2_census_acs5sub_education_data[[1]],
  #     percent_leg = FALSE,
  #     var = 'estimate', 
  #     conus_sf = p1_conus_sf, 
  #     outfile_path = "3_visualize/out/tot_education_18-24_highschool_2022.png",
  #     leg_title =  "Total population 18-24 years\nhigh school graduate (includes equivalency), 2022",
  #     viz_config_df = viz_config_df
  #   ),
  #   format = "file"
  # ),
  # tar_target(
  #   p3_tot_25_plus_highschool_education_map_png,
  #   plot_census_map(
  #     census_data = p2_census_acs5sub_education_data[[2]],
  #     percent_leg = FALSE,
  #     var = 'estimate', 
  #     conus_sf = p1_conus_sf, 
  #     outfile_path = "3_visualize/out/tot_education_25-plus_highschool_2022.png",
  #     leg_title =  "Total population 25 and older\nhigh school graduate (includes equivalency), 2022",
  #     viz_config_df = viz_config_df
  #   ),
  #   format = "file"
  # ),
  # Household related maps
  tar_target(
    p3_total_households_png,
    plot_census_map(
      census_data = p2_census_acs5sub_household_data[[1]],
      percent_leg = FALSE,
      var = 'estimate',
      conus_sf = p1_conus_sf,
      outfile_path = "3_visualize/out/tot_households_2022.png",
      leg_title = "Total households, 2022",
      viz_config_df = p0_viz_config_df,
      viz_config_pal = p0_viz_config_pal$pal_positive
    ),
    format = "file"
  ),
  tar_target(
    p3_female_households_png,
    plot_census_map(
      census_data = p2_census_acs5sub_household_data[[2]],
      percent_leg = FALSE,
      var = 'estimate',
      conus_sf = p1_conus_sf,
      outfile_path = "3_visualize/out/tot_female_households_2022.png",
      leg_title = "Total female households, 2022",
      viz_config_df = p0_viz_config_df,
      viz_config_pal = p0_viz_config_pal$pal_positive
    ),
    format = "file"
  ),
  tar_target(
    p3_median_rent_png,
    plot_census_map(
      census_data = p2_census_acs5sub_household_data[[3]],
      percent_leg = FALSE,
      var = 'estimate',
      conus_sf = p1_conus_sf,
      outfile_path = "3_visualize/out/median_rent_2022.png",
      leg_title = "Median gross rent, 2022",
      viz_config_df = p0_viz_config_df,
      viz_config_pal = p0_viz_config_pal$pal_neg
    ),
    format = "file"
  )
)