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Commit 001b17b8 authored by Azadpour, Elmera's avatar Azadpour, Elmera
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update maps

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1 merge request!12Add disaggregated census data/maps
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3_visualize.R
+ 179
− 132
View file @ 001b17b8
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,
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 = viz_config_df
),
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_df = viz_config_df,
viz_config_pal = viz_config_pal$pal_positive
),
format = "file"
),
tar_target(
p3_perc_black_png,
p3_perc_household_income_map_png,
plot_census_map(
census_data = p2_perc_census_acs5_layers_sf[[3]],
census_data = p2_census_acs5sub_income_data[[1]],
percent_leg = TRUE,
var = 'percent',
var = 'percent',
conus_sf = p1_conus_sf,
outfile_path = "3_visualize/out/perc_black_census_2022.png",
leg_title = "Percent Black, 2022",
viz_config_df = viz_config_df
outfile_path = "3_visualize/out/perc_household_income_2022.png",
leg_title = "Percent allocated household income in the past 12 months, 2022",
viz_config_df = viz_config_df,
viz_config_pal = viz_config_pal$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,
......@@ -53,7 +68,8 @@ p3_targets <- list(
conus_sf = p1_conus_sf,
outfile_path = "3_visualize/out/tot_latino_census_2022.png",
leg_title = "Total Latino, 2022",
viz_config_df = viz_config_df
viz_config_df = viz_config_df,
viz_config_pal = viz_config_pal$pal_positive
),
format = "file"
),
......@@ -67,131 +83,162 @@ p3_targets <- list(
conus_sf = p1_conus_sf,
outfile_path = "3_visualize/out/perc_latino_census_2022.png",
leg_title = "Percent Latino, 2022",
viz_config_df = viz_config_df
viz_config_df = viz_config_df,
viz_config_pal = viz_config_pal$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_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_tot_female_png,
p3_total_households_png,
plot_census_map(
census_data = p2_perc_census_acs5_layers_sf[[6]],
census_data = p2_census_acs5sub_household_data[[1]],
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
),
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"
),
# percent income maps
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 = viz_config_df
var = 'estimate',
conus_sf = p1_conus_sf,
outfile_path = "3_visualize/out/tot_households_2022.png",
leg_title = "Total households, 2022",
viz_config_df = viz_config_df,
viz_config_pal = viz_config_pal$pal_positive
),
format = "file"
),
# education related map
tar_target(
p3_tot_18_24_highschool_education_map_png,
p3_female_households_png,
plot_census_map(
census_data = p2_census_acs5sub_education_data[[1]],
census_data = p2_census_acs5sub_household_data[[2]],
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
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 = viz_config_df,
viz_config_pal = viz_config_pal$pal_positive
),
format = "file"
),
tar_target(
p3_tot_25_plus_highschool_education_map_png,
p3_median_rent_png,
plot_census_map(
census_data = p2_census_acs5sub_education_data[[2]],
census_data = p2_census_acs5sub_household_data[[3]],
percent_leg = FALSE,
var = 'estimate',
conus_sf = p1_conus_sf,
outfile_path = "3_visualize/out/tot_education_25-plus_highschool_2022.png",
leg_title = "Total population 25 and older\nhigh school graduate (includes equivalency), 2022",
viz_config_df = viz_config_df
var = 'estimate',
conus_sf = p1_conus_sf,
outfile_path = "3_visualize/out/median_rent_2022.png",
leg_title = "Median gross rent, 2022",
viz_config_df = viz_config_df,
viz_config_pal = viz_config_pal$pal_neg
),
format = "file"
)
......
3_visualize/src/plot_utils.R
+ 6
− 4
View file @ 001b17b8
......@@ -4,9 +4,11 @@
#' @param conus_sf, sf of conus states outline
#' @param leg_title, character string for legend title
#' @param outfile_path, outfile path for pngs
#' @param viz_config_df `data.frame` with palette, width, height, counties outline color, conus outline color, background color, font nam, and font size
#' @param viz_config_df `data.frame` width, height, counties outline color, conus outline color, background color, font nam, and font size
#' @param viz_config_pal `data.frame` assign colors for postively and negatively correlated dimensions for census maps
#' @param percent_leg if else statement where if TRUE, apply 0-100 legend, otherwise retain 0 - max of variable name
plot_census_map <- function(census_data, conus_sf, leg_title, outfile_path, font_size, var, percent_leg, viz_config_df){
plot_census_map <- function(census_data, conus_sf, leg_title, outfile_path, font_size, var, percent_leg, viz_config_df, viz_config_pal){
font_legend <- viz_config_df$load_font
font_add_google(font_legend)
......@@ -37,7 +39,7 @@ plot_census_map <- function(census_data, conus_sf, leg_title, outfile_path, font
if (percent_leg == FALSE) {
census_map <- census_map +
scale_fill_distiller(
palette = viz_config_df$pal,
palette = viz_config_pal,
direction = 1,
name = leg_title,
limits = c(0, max(census_data[[var]], na.rm = TRUE)),
......@@ -46,7 +48,7 @@ plot_census_map <- function(census_data, conus_sf, leg_title, outfile_path, font
} else {
census_map <- census_map +
scale_fill_distiller(
palette = viz_config_df$pal,
palette = viz_config_pal,
direction = 1,
name = leg_title,
limits = c(0, 100),
......
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