data-raw/spatial_geoid20_to_hra20_acs.R
In PHSKC-APDE/rads.data: Data for rads package and other public health uses
## Header ----
# Author: Danny Colombara
# Date: March 9, 2023
# R version: 4.2.1
# Purpose: Create new crosswalk files from Census 2020 Geographies to APDE 2020
# HRAs
# New geographies created after decennial census, so we need to map
# vintage 2020 block groups, tracts, & places to Daniel Casey's 2020 HRAs.
#
# What I did:
# I identified the intersections of all places, tracts, and block groups
# with HRAs. I preferentially kept the largest geography that intersected
# with the HRA in order to reduce standard error inflation when
# aggregating ACS tabular data. This means that tracts or block groups
# that were nested in places that were themselves in an HRA were ignored.
# Similarly, block groups nested in tracts which were already nested were
# ignored. NOTE! Since 'places' can be large, they can have a bigger
# impact on the estimates so the have a different threshold.
#
# The QA maps and table are produced at the bottom of this code. They are
# not saved in this repo because they can be easily reproduced.
#
# Remember!
# 1) HRAs are properly defined by blocks, but ACS only has blocks for a
# limited number of tables, which is why we have this miserable
# situation.
# 2) There is a set hierarchy for blocks >> block groups >> tracts >>
# counties. We can take advantage of this because of the nesting of
# geoids.
# 3) Places do NOT fit into the hierarchy; they can spill across county
# lines but are always contained within a single state.
## Set up ----
rm(list = ls())
pacman::p_load(data.table, rads, ggplot2, sf, leaflet)
options(scipen = 999) # turn off scientific notation
cutpoint = 0.85 # keep if > XX% of a geography's area intersects with the HRA
place.cutpoint = 0.95 # keep if > XX% of a geography's area intersects with the HRA
export.dir <- "c:/temp/acs_hras"
shapefile.dir <- "//dphcifs/APDE-CDIP/Shapefiles/"
## Get shapefiles ----
# Load and standardize shapefiles ----
hras <- st_read(paste0(shapefile.dir, "HRA/hra_2020.shp"))
hras$hras.area.start = as.numeric(st_area(hras)) # get initial area of each bg
water = st_read("//Kcitfsrprpgdw01/kclib/Plibrary2/hydro/shapes/polygon/wtrbdy.shp")
water <- st_make_valid(st_transform(water, st_crs(hras)))
water <- subset(water, SUBSET == 'Bigwater waterbody')
water <- st_make_valid(subset(water, FEATURE_ID %in% st_make_valid(st_intersection(water, hras))$FEATURE_ID)) # just bigwaters that intersect KC
bgs <- st_read(paste0(shapefile.dir, "/Census_2020/block_group/kc_block_group.shp"), stringsAsFactors = F)
bgs <- st_transform(bgs, st_crs(hras))
bgs <- st_make_valid(bgs)
tracts <- st_read(paste0(shapefile.dir, "/Census_2020/tract/kc_tract.shp"), stringsAsFactors = F)
tracts <- st_transform(tracts, st_crs(hras))
tracts <- st_make_valid(tracts)
places <- st_read(paste0(shapefile.dir, "/Census_2020/places/kc_places.shp"), stringsAsFactors = F)
places <- st_transform(places, st_crs(hras))
places <- st_make_valid(places)
places$area.start = as.numeric(st_area(places)) # get initial area of each place
# erase big waters ----
hras <- rmapshaper::ms_erase(hras, st_crop(water, hras))
bgs <- rmapshaper::ms_erase(bgs, st_crop(water, bgs))
tracts <- rmapshaper::ms_erase(tracts, st_crop(water, tracts))
places <- rmapshaper::ms_erase(places, st_crop(water, places))
# create copies of shapefiles before manipulation below
orig.hras <- copy(hras)
orig.bgs <- copy(bgs)
orig.tracts <- copy(tracts)
orig.places <- copy(places)
## STEP 1: Identify places and HRAs that define the same geography ----
# Compute area intersection (didn't use spatagg:: because needed fraction in both directions)
tempi <- st_make_valid(st_intersection(places, hras))
tempi$prop = as.numeric(st_area(tempi)) / tempi$area.start
tempi$hra.prop = as.numeric(st_area(tempi)) / tempi$hras.area.start
pairs.1 <- setDT(copy(tempi))[hra.prop > place.cutpoint & prop > place.cutpoint, .(place = GEOID, hra = id)]
# drop bg geographies that are nested within these places and hras
tempi <- st_make_valid(st_intersection(bgs, subset(places, GEOID %in% pairs.1$place)))
tempi$prop = as.numeric(st_area(tempi)) / tempi$area.start
tempi <- setDT(tempi)[prop >= cutpoint, .(bgGEOID = GEOID, prop)]
bgs <- subset(bgs, !GEOID %in% tempi$bgGEOID)
# drop tract geographies that are nested within these places and hras
tempi <- st_make_valid(st_intersection(tracts, subset(places, GEOID %in% pairs.1$place)))
tempi$prop = as.numeric(st_area(tempi)) / tempi$area.start
tempi <- setDT(tempi)[prop >= cutpoint, .(tractGEOID = GEOID, prop)]
tracts <- subset(tracts, !GEOID %in% tempi$tractGEOID)
# drop places & hras that are already matched
places <- subset(places, !GEOID %in% pairs.1$place )
hras <- subset(hras, !id %in% pairs.1$hra)
## STEP 2: Identify remaining places that are nested within remaining HRAs ----
# Compute area of intersection
tempi = spatagg::create_xwalk(source = places, target = hras,
source_id = 'GEOID', target_id = 'id', min_overlap = 0.1)
pairs.2 <- setDT(copy(tempi))[s2t_fraction >= place.cutpoint, .(place = source_id, hra = target_id)]
unioned <- st_cast(st_as_sf(st_union(subset(places, GEOID %in% pairs.2$place))), 'POLYGON')
unioned$ID <- 1:nrow(unioned)
# drop bg geographies that are nested within the places in pairs.2
tempi = spatagg::create_xwalk(source = bgs, target = unioned,
source_id = 'GEOID', target_id = 'ID', min_overlap = 0.1)
tempi <- setDT(copy(tempi))[s2t_fraction >= 0.60,]
bgs <- subset(bgs, !GEOID %in% tempi$source_id)
# drop tract geographies that are nested within the places in pairs.2
tempi = spatagg::create_xwalk(source = tracts, target = unioned,
source_id = 'GEOID', target_id = 'ID', min_overlap = 0.1)
tempi <- setDT(copy(tempi))[s2t_fraction >= 0.60,]
tracts <- subset(tracts, !GEOID %in% tempi$source_id)
## STEP 3: Identify remaining tracts that are nested within remaining HRAs ----
# Compute area of intersection
tempi = spatagg::create_xwalk(source = tracts, target = hras,
source_id = 'GEOID', target_id = 'id', min_overlap = 0.1)
pairs.3 <- setDT(copy(tempi))[s2t_fraction >= cutpoint, .(tract = source_id, hra = target_id)]
unioned <- st_cast(st_as_sf(st_union(subset(tracts, GEOID %in% pairs.3$tract))), 'POLYGON')
unioned$ID <- 1:nrow(unioned)
# drop bg geographies that are nested within the tracts in pairs.3
tempi = spatagg::create_xwalk(source = bgs, target = unioned,
source_id = 'GEOID', target_id = 'ID', min_overlap = 0.1)
tempi <- setDT(copy(tempi))[s2t_fraction >= cutpoint,]
bgs <- subset(bgs, !GEOID %in% tempi$source_id)
## STEP 4: Identify remaining blockgroups that are nested withing remaining HRAs ----
# Compute area of intersection
tempi = spatagg::create_xwalk(source = bgs, target = hras,
source_id = 'GEOID', target_id = 'id', min_overlap = 0.1)
pairs.4 <- setDT(copy(tempi))[s2t_fraction >= 0.51, .(bg = source_id, hra = target_id)]
# remove bg geographies that are nested within the tracts in pairs.4
bgs <- subset(bgs, !GEOID %in% pairs.4$bg)
## STEP 5: Create initial complete GEOID20-HRA20 crosswalk ----
# append pairs ----
xwalk <- rbind(pairs.1, pairs.2, pairs.3, pairs.4, fill = T)
# manual tweaks based on visual inspection ----
xwalk <- rbind(xwalk, data.table(hra = c(1, 8), bg = "530330315021"), fill = T)
xwalk <- xwalk[!(!is.na(bg) & hra %in% c(15, 20, 25, 26, 59))]
# final table structure ----
xwalk <- unique(merge(xwalk, setDT(copy(orig.hras))[, .(hra = id, hra_name = name)], all.x = T, all.y = F))
xwalk <- xwalk[, .(hra20_name = hra_name, hra20 = hra, place20 = place, tract20 = tract, bg20 = bg)]
## STEP 6: Map all crosswalks ----
# Create plot.tidy function (places/tracts/bgs cropped to HRA borders) ----
plot.tidy <- function(mygeo){
message(paste0("Mapping ", mygeo, " (tidy)"))
myxwalks <- xwalk[hra20_name == mygeo]
myhras = subset(orig.hras, name == mygeo) # select just one HRA
myplaces = suppressWarnings(subset(st_intersection(orig.places, myhras), GEOID %in% myxwalks$place20)) # only tracts that intersect the HRA of interest
mytracts = suppressWarnings(subset(st_intersection(orig.tracts, myhras), GEOID %in% myxwalks$tract20)) # only tracts that intersect the HRA of interest
mybgs = suppressWarnings(subset(st_intersection(orig.bgs, myhras), GEOID %in% myxwalks$bg20)) # only bgs that intersect the HRA of interest
ggplot() +
geom_sf(data = myhras, fill = 'white', color = "black") +
geom_sf(data = myplaces, fill = "yellow", color = "yellow", alpha = 0.2) +
geom_sf(data = mytracts, fill = "red", color = "red", alpha = 0.2) +
geom_sf(data = mybgs, fill = "blue", color = "black", alpha = 0.2) +
labs(title = mygeo, subtitle = "Tidy version == places, tracts, & block groups cropped to HRA borders." , caption = "yellow = place; red = tract; blue = block group")
}
# Create plot.messy function ----
plot.messy <- function(mygeo){
message(paste0("Mapping ", mygeo, " (messy)"))
myxwalks <- xwalk[hra20_name == mygeo]
myhras = subset(hras, name == mygeo) # select just one HRA
myplaces = suppressWarnings(subset(orig.places, GEOID %in% myxwalks$place20)) # only tracts that intersect the HRA of interest
mytracts = suppressWarnings(subset(orig.tracts, GEOID %in% myxwalks$tract20)) # only tracts that intersect the HRA of interest
mybgs = suppressWarnings(subset(orig.bgs, GEOID %in% myxwalks$bg20)) # only bgs that intersect the HRA of interest
ggplot() +
geom_sf(data = myplaces, fill = "yellow", color = "white", alpha = 0.5) +
geom_sf(data = mytracts, fill = "red", color = "white", alpha = 0.2) +
geom_sf(data = mybgs, fill = "blue", color = "white", alpha = 0.2) +
geom_sf(data = myhras, fill = 'white', color = "black", alpha = 0.2) +
theme(panel.grid.major = element_line(colour = "transparent"),
panel.background = element_rect(fill = 'transparent', colour = 'transparent')) +
labs(title = mygeo, subtitle = "Messy version == places, tracts, & block groups allowed to show spilling over HRA borders." ,
caption = "black line = HRA border; yellow = place; red = tract; blue = block group")
}
# Apply mapping functions and save as PDFs ----
pdf(paste0(export.dir, "/xwalk_maps_GEOID20_HRA20_tidy", ".pdf"), height=8.5, width=11) # open the PDF into which I will append all the files
for(single.hra in sort(unique(xwalk$hra20_name))){
my.plot <- plot.tidy(single.hra)
print(my.plot) # force R to produce plot so it will save into PDF
}
dev.off() # close the PDF
pdf(paste0(export.dir, "/xwalk_maps_GEOID20_HRA20_messy", ".pdf"), height=8.5, width=11) # open the PDF into which I will append all the files
for(single.hra in sort(unique(xwalk$hra20_name))){
my.plot <- plot.messy(single.hra)
print(my.plot) # force R to produce plot so it will save into PDF
}
dev.off() # close the PDF
# STEP 7: Save crosswalk (after visually confirming it is reasonable) ----
# tidy table for export ----
spatial_geoid20_to_hra20_acs <- copy(xwalk)
spatial_geoid20_to_hra20_acs[, creation_date := Sys.Date()]
spatial_geoid20_to_hra20_acs <- spatial_geoid20_to_hra20_acs[, .(hra20_id = hra20, hra20_name, place20, tract20, bg20, creation_date)]
setorder(spatial_geoid20_to_hra20_acs, hra20_id, bg20, tract20, place20)
# Save as RDA file ----
usethis::use_data(spatial_geoid20_to_hra20_acs, compress = "bzip2", version = 3, overwrite = TRUE)
# Save as CSV file -----
write.csv(spatial_geoid20_to_hra20_acs, file = paste0(here::here(), "/inst/extdata/spatial_data/geoid20_to_hra20_acs.csv"), row.names = FALSE)
# The end ----
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## Header ----
# Author: Danny Colombara
# Date: March 9, 2023
# R version: 4.2.1
# Purpose: Create new crosswalk files from Census 2020 Geographies to APDE 2020
# HRAs
# New geographies created after decennial census, so we need to map
# vintage 2020 block groups, tracts, & places to Daniel Casey's 2020 HRAs.
#
# What I did:
# I identified the intersections of all places, tracts, and block groups
# with HRAs. I preferentially kept the largest geography that intersected
# with the HRA in order to reduce standard error inflation when
# aggregating ACS tabular data. This means that tracts or block groups
# that were nested in places that were themselves in an HRA were ignored.
# Similarly, block groups nested in tracts which were already nested were
# ignored. NOTE! Since 'places' can be large, they can have a bigger
# impact on the estimates so the have a different threshold.
#
# The QA maps and table are produced at the bottom of this code. They are
# not saved in this repo because they can be easily reproduced.
#
# Remember!
# 1) HRAs are properly defined by blocks, but ACS only has blocks for a
# limited number of tables, which is why we have this miserable
# situation.
# 2) There is a set hierarchy for blocks >> block groups >> tracts >>
# counties. We can take advantage of this because of the nesting of
# geoids.
# 3) Places do NOT fit into the hierarchy; they can spill across county
# lines but are always contained within a single state.
## Set up ----
rm(list = ls())
pacman::p_load(data.table, rads, ggplot2, sf, leaflet)
options(scipen = 999) # turn off scientific notation
cutpoint = 0.85 # keep if > XX% of a geography's area intersects with the HRA
place.cutpoint = 0.95 # keep if > XX% of a geography's area intersects with the HRA
export.dir <- "c:/temp/acs_hras"
shapefile.dir <- "//dphcifs/APDE-CDIP/Shapefiles/"
## Get shapefiles ----
# Load and standardize shapefiles ----
hras <- st_read(paste0(shapefile.dir, "HRA/hra_2020.shp"))
hras$hras.area.start = as.numeric(st_area(hras)) # get initial area of each bg
water = st_read("//Kcitfsrprpgdw01/kclib/Plibrary2/hydro/shapes/polygon/wtrbdy.shp")
water <- st_make_valid(st_transform(water, st_crs(hras)))
water <- subset(water, SUBSET == 'Bigwater waterbody')
water <- st_make_valid(subset(water, FEATURE_ID %in% st_make_valid(st_intersection(water, hras))$FEATURE_ID)) # just bigwaters that intersect KC
bgs <- st_read(paste0(shapefile.dir, "/Census_2020/block_group/kc_block_group.shp"), stringsAsFactors = F)
bgs <- st_transform(bgs, st_crs(hras))
bgs <- st_make_valid(bgs)
tracts <- st_read(paste0(shapefile.dir, "/Census_2020/tract/kc_tract.shp"), stringsAsFactors = F)
tracts <- st_transform(tracts, st_crs(hras))
tracts <- st_make_valid(tracts)
places <- st_read(paste0(shapefile.dir, "/Census_2020/places/kc_places.shp"), stringsAsFactors = F)
places <- st_transform(places, st_crs(hras))
places <- st_make_valid(places)
places$area.start = as.numeric(st_area(places)) # get initial area of each place
# erase big waters ----
hras <- rmapshaper::ms_erase(hras, st_crop(water, hras))
bgs <- rmapshaper::ms_erase(bgs, st_crop(water, bgs))
tracts <- rmapshaper::ms_erase(tracts, st_crop(water, tracts))
places <- rmapshaper::ms_erase(places, st_crop(water, places))
# create copies of shapefiles before manipulation below
orig.hras <- copy(hras)
orig.bgs <- copy(bgs)
orig.tracts <- copy(tracts)
orig.places <- copy(places)
## STEP 1: Identify places and HRAs that define the same geography ----
# Compute area intersection (didn't use spatagg:: because needed fraction in both directions)
tempi <- st_make_valid(st_intersection(places, hras))
tempi$prop = as.numeric(st_area(tempi)) / tempi$area.start
tempi$hra.prop = as.numeric(st_area(tempi)) / tempi$hras.area.start
pairs.1 <- setDT(copy(tempi))[hra.prop > place.cutpoint & prop > place.cutpoint, .(place = GEOID, hra = id)]
# drop bg geographies that are nested within these places and hras
tempi <- st_make_valid(st_intersection(bgs, subset(places, GEOID %in% pairs.1$place)))
tempi$prop = as.numeric(st_area(tempi)) / tempi$area.start
tempi <- setDT(tempi)[prop >= cutpoint, .(bgGEOID = GEOID, prop)]
bgs <- subset(bgs, !GEOID %in% tempi$bgGEOID)
# drop tract geographies that are nested within these places and hras
tempi <- st_make_valid(st_intersection(tracts, subset(places, GEOID %in% pairs.1$place)))
tempi$prop = as.numeric(st_area(tempi)) / tempi$area.start
tempi <- setDT(tempi)[prop >= cutpoint, .(tractGEOID = GEOID, prop)]
tracts <- subset(tracts, !GEOID %in% tempi$tractGEOID)
# drop places & hras that are already matched
places <- subset(places, !GEOID %in% pairs.1$place )
hras <- subset(hras, !id %in% pairs.1$hra)
## STEP 2: Identify remaining places that are nested within remaining HRAs ----
# Compute area of intersection
tempi = spatagg::create_xwalk(source = places, target = hras,
source_id = 'GEOID', target_id = 'id', min_overlap = 0.1)
pairs.2 <- setDT(copy(tempi))[s2t_fraction >= place.cutpoint, .(place = source_id, hra = target_id)]
unioned <- st_cast(st_as_sf(st_union(subset(places, GEOID %in% pairs.2$place))), 'POLYGON')
unioned$ID <- 1:nrow(unioned)
# drop bg geographies that are nested within the places in pairs.2
tempi = spatagg::create_xwalk(source = bgs, target = unioned,
source_id = 'GEOID', target_id = 'ID', min_overlap = 0.1)
tempi <- setDT(copy(tempi))[s2t_fraction >= 0.60,]
bgs <- subset(bgs, !GEOID %in% tempi$source_id)
# drop tract geographies that are nested within the places in pairs.2
tempi = spatagg::create_xwalk(source = tracts, target = unioned,
source_id = 'GEOID', target_id = 'ID', min_overlap = 0.1)
tempi <- setDT(copy(tempi))[s2t_fraction >= 0.60,]
tracts <- subset(tracts, !GEOID %in% tempi$source_id)
## STEP 3: Identify remaining tracts that are nested within remaining HRAs ----
# Compute area of intersection
tempi = spatagg::create_xwalk(source = tracts, target = hras,
source_id = 'GEOID', target_id = 'id', min_overlap = 0.1)
pairs.3 <- setDT(copy(tempi))[s2t_fraction >= cutpoint, .(tract = source_id, hra = target_id)]
unioned <- st_cast(st_as_sf(st_union(subset(tracts, GEOID %in% pairs.3$tract))), 'POLYGON')
unioned$ID <- 1:nrow(unioned)
# drop bg geographies that are nested within the tracts in pairs.3
tempi = spatagg::create_xwalk(source = bgs, target = unioned,
source_id = 'GEOID', target_id = 'ID', min_overlap = 0.1)
tempi <- setDT(copy(tempi))[s2t_fraction >= cutpoint,]
bgs <- subset(bgs, !GEOID %in% tempi$source_id)
## STEP 4: Identify remaining blockgroups that are nested withing remaining HRAs ----
# Compute area of intersection
tempi = spatagg::create_xwalk(source = bgs, target = hras,
source_id = 'GEOID', target_id = 'id', min_overlap = 0.1)
pairs.4 <- setDT(copy(tempi))[s2t_fraction >= 0.51, .(bg = source_id, hra = target_id)]
# remove bg geographies that are nested within the tracts in pairs.4
bgs <- subset(bgs, !GEOID %in% pairs.4$bg)
## STEP 5: Create initial complete GEOID20-HRA20 crosswalk ----
# append pairs ----
xwalk <- rbind(pairs.1, pairs.2, pairs.3, pairs.4, fill = T)
# manual tweaks based on visual inspection ----
xwalk <- rbind(xwalk, data.table(hra = c(1, 8), bg = "530330315021"), fill = T)
xwalk <- xwalk[!(!is.na(bg) & hra %in% c(15, 20, 25, 26, 59))]
# final table structure ----
xwalk <- unique(merge(xwalk, setDT(copy(orig.hras))[, .(hra = id, hra_name = name)], all.x = T, all.y = F))
xwalk <- xwalk[, .(hra20_name = hra_name, hra20 = hra, place20 = place, tract20 = tract, bg20 = bg)]
## STEP 6: Map all crosswalks ----
# Create plot.tidy function (places/tracts/bgs cropped to HRA borders) ----
plot.tidy <- function(mygeo){
message(paste0("Mapping ", mygeo, " (tidy)"))
myxwalks <- xwalk[hra20_name == mygeo]
myhras = subset(orig.hras, name == mygeo) # select just one HRA
myplaces = suppressWarnings(subset(st_intersection(orig.places, myhras), GEOID %in% myxwalks$place20)) # only tracts that intersect the HRA of interest
mytracts = suppressWarnings(subset(st_intersection(orig.tracts, myhras), GEOID %in% myxwalks$tract20)) # only tracts that intersect the HRA of interest
mybgs = suppressWarnings(subset(st_intersection(orig.bgs, myhras), GEOID %in% myxwalks$bg20)) # only bgs that intersect the HRA of interest
ggplot() +
geom_sf(data = myhras, fill = 'white', color = "black") +
geom_sf(data = myplaces, fill = "yellow", color = "yellow", alpha = 0.2) +
geom_sf(data = mytracts, fill = "red", color = "red", alpha = 0.2) +
geom_sf(data = mybgs, fill = "blue", color = "black", alpha = 0.2) +
labs(title = mygeo, subtitle = "Tidy version == places, tracts, & block groups cropped to HRA borders." , caption = "yellow = place; red = tract; blue = block group")
}
# Create plot.messy function ----
plot.messy <- function(mygeo){
message(paste0("Mapping ", mygeo, " (messy)"))
myxwalks <- xwalk[hra20_name == mygeo]
myhras = subset(hras, name == mygeo) # select just one HRA
myplaces = suppressWarnings(subset(orig.places, GEOID %in% myxwalks$place20)) # only tracts that intersect the HRA of interest
mytracts = suppressWarnings(subset(orig.tracts, GEOID %in% myxwalks$tract20)) # only tracts that intersect the HRA of interest
mybgs = suppressWarnings(subset(orig.bgs, GEOID %in% myxwalks$bg20)) # only bgs that intersect the HRA of interest
ggplot() +
geom_sf(data = myplaces, fill = "yellow", color = "white", alpha = 0.5) +
geom_sf(data = mytracts, fill = "red", color = "white", alpha = 0.2) +
geom_sf(data = mybgs, fill = "blue", color = "white", alpha = 0.2) +
geom_sf(data = myhras, fill = 'white', color = "black", alpha = 0.2) +
theme(panel.grid.major = element_line(colour = "transparent"),
panel.background = element_rect(fill = 'transparent', colour = 'transparent')) +
labs(title = mygeo, subtitle = "Messy version == places, tracts, & block groups allowed to show spilling over HRA borders." ,
caption = "black line = HRA border; yellow = place; red = tract; blue = block group")
}
# Apply mapping functions and save as PDFs ----
pdf(paste0(export.dir, "/xwalk_maps_GEOID20_HRA20_tidy", ".pdf"), height=8.5, width=11) # open the PDF into which I will append all the files
for(single.hra in sort(unique(xwalk$hra20_name))){
my.plot <- plot.tidy(single.hra)
print(my.plot) # force R to produce plot so it will save into PDF
}
dev.off() # close the PDF
pdf(paste0(export.dir, "/xwalk_maps_GEOID20_HRA20_messy", ".pdf"), height=8.5, width=11) # open the PDF into which I will append all the files
for(single.hra in sort(unique(xwalk$hra20_name))){
my.plot <- plot.messy(single.hra)
print(my.plot) # force R to produce plot so it will save into PDF
}
dev.off() # close the PDF
# STEP 7: Save crosswalk (after visually confirming it is reasonable) ----
# tidy table for export ----
spatial_geoid20_to_hra20_acs <- copy(xwalk)
spatial_geoid20_to_hra20_acs[, creation_date := Sys.Date()]
spatial_geoid20_to_hra20_acs <- spatial_geoid20_to_hra20_acs[, .(hra20_id = hra20, hra20_name, place20, tract20, bg20, creation_date)]
setorder(spatial_geoid20_to_hra20_acs, hra20_id, bg20, tract20, place20)
# Save as RDA file ----
usethis::use_data(spatial_geoid20_to_hra20_acs, compress = "bzip2", version = 3, overwrite = TRUE)
# Save as CSV file -----
write.csv(spatial_geoid20_to_hra20_acs, file = paste0(here::here(), "/inst/extdata/spatial_data/geoid20_to_hra20_acs.csv"), row.names = FALSE)
# The end ----
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