library(readr)
library(dplyr)
library(magrittr)
library(tidyr)
source("R/operators.R")
source("R/general_utils.R")
source("R/peer_utils.R")
source("R/glp_utils.R")
path <- "data-raw/zip_codes/"
zip_crosswalk <- read_csv(path %p% "ZIP_COUNTY_122017.csv", col_types = "ccdddd")
zip_crosswalk %<>%
transmute(
FIPS = county,
zip,
pct_pop_in_county = res_ratio * 100,
business_in_county = bus_ratio * 100,
total_in_county = tot_ratio * 100)
MSA_zip <- zip_crosswalk %>%
pull_peers(geog = "MSA", add_info = F) %>%
left_join(MSA_FIPS, by = "FIPS") %>%
group_by(zip, MSA) %>%
summarise(
pct_in_MSA = sum(pct_pop_in_county),
business_in_county = sum(business_in_county),
total_in_county = sum(total_in_county),
.groups = "drop") %>%
select(MSA, zip, pct_in_MSA, business_in_county, total_in_county)
FIPS_zip <- zip_crosswalk %>%
pull_peers(add_info = F) %>%
select(FIPS, zip, pct_pop_in_county, business_in_county, total_in_county)
FIPS_zip_full_MSA <- zip_crosswalk %>%
pull_peers(geog = "MSA", add_info = F) %>%
select(FIPS, zip, pct_pop_in_county, business_in_county, total_in_county)
# tract_zip <- read_csv(path %p% "ZIP_TRACT_122017.csv", col_types = "ccdddd")
#
# zip_to_tract <- tract_zip %<>%
# transmute(
# FIPS = str_sub(tract, 1, 5),
# tract,
# zip,
# pct_zip_pop_in_tract = res_ratio * 100,
# pct_zip_business_in_tract = bus_ratio * 100,
# pct_zip_total_in_tract = tot_ratio * 100) %>%
# filter(str_sub(FIPS, 1, 2) == "21")
#
# tract_to_zip <- tract_zip %>%
# group_by(tract) %>%
# mutate(
# pct_tract_pop_in_zip = pct_zip_pop_in_tract / sum(pct_zip_pop_in_tract) * 100) %>%
# filter(pct_tract_pop_in_zip != 0)
#
# table(tract_to_zip$pct_tract_pop_in_zip == 100)
# mean(tract_to_zip$pct_tract_pop_in_zip == 100)
#
# hist(tract_to_zip$pct_tract_pop_in_zip, freq = F, breaks = 100)
usethis::use_data(MSA_zip, FIPS_zip, FIPS_zip_full_MSA, overwrite = TRUE)
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