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Working on zip code management

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Alex 2025-10-31 12:22:37 -06:00
parent b62ad1223f
commit 2ca87af106
2 changed files with 72 additions and 27 deletions
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50
Data/API_CENSUS_CODES.csv Normal file
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#Codes collected from https://api.census.gov/data/2019/acs/acs1/variables.html to map to
Code,Sex,Min_Age,Max_Age
B01001_002E,"Male",0,Inf
B01001_003E,"Male",0,4
B01001_004E,"Male",5,9
B01001_005E,"Male",10,14
B01001_006E,"Male",15,17
B01001_007E,"Male",18,18
B01001_008E,"Male",20,20
B01001_009E,"Male",21,21
B01001_010E,"Male",22,24
B01001_011E,"Male",25,29
B01001_012E,"Male",30,34
B01001_013E,"Male",35,39
B01001_014E,"Male",40,44
B01001_015E,"Male",45,49
B01001_016E,"Male",50,54
B01001_017E,"Male",55,59
B01001_018E,"Male",60,60
B01001_019E,"Male",62,64
B01001_020E,"Male",65,65
B01001_021E,"Male",67,69
B01001_022E,"Male",70,74
B01001_023E,"Male",75,79
B01001_024E,"Male",80,84
B01001_025E,"Male",85,Inf
B01001_026E,"Female",0,Inf
B01001_027E,"Female",0,4
B01001_028E,"Female",5,9
B01001_029E,"Female",10,14
B01001_030E,"Female",15,17
B01001_031E,"Female",18,18
B01001_032E,"Female",20,20
B01001_033E,"Female",21,21
B01001_034E,"Female",22,24
B01001_035E,"Female",25,29
B01001_036E,"Female",30,34
B01001_037E,"Female",35,39
B01001_038E,"Female",40,44
B01001_039E,"Female",45,49
B01001_040E,"Female",50,54
B01001_041E,"Female",55,59
B01001_042E,"Female",60,60
B01001_043E,"Female",62,64
B01001_044E,"Female",65,65
B01001_045E,"Female",67,69
B01001_046E,"Female",70,74
B01001_047E,"Female",75,79
B01001_048E,"Female",80,84
B01001_049E,"Female",85,Inf
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Zip_Code.r
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library(tidyverse)
library(tidycensus)
library(zipcodeR)
KEY <- '30e13ab22563318ff59286e433099f4174d4edd4'
TRACTS <- get_tracts(search_city('Diamondville','WY')$zipcode) %>% full_join(get_tracts(search_city('Kemmerer','WY')$zipcode))
TRACTS
install.packages("zipcodeR")
install.packages("units")
#install.packages("terra")
census_api_key(KEY, install = TRUE)
vars <- c("P2_002N")
get_decennial(geography="tract",variables=vars,state='WY',county='lincoln',GEOID='56023978400')
vars <- c("B01001_004","B01001_005")
KEY <- '30e13ab22563318ff59286e433099f4174d4edd4'
#Ex
TRACTS <-get_tracts(search_city('Kemmerer','WY')$zipcode) %>% full_join(get_tracts(search_city('Diamondville','WY')$zipcode))
get_decennial(geography="tract",variables=vars,state='WY',county='lincoln')
c('B01002_001E','B01002_002E','B01002_003E')
c('Median_Age','Median_Age_Male','Median_Age_Female')
get_acs(geography="tract",variables=vars,state='WY',county='lincoln',GEOID='56023978400')
######MALE
#Variable names from: https://api.census.gov/data/2019/acs/acs1/variables.html
VARS <-c(
#Under_Five
'B01001_003E',
#5-9
'B01001_004E',
#10-14
'B01001_005E',
#15-17
'B01001_006E',
#18-19
'B01001_007E',
#20
'B01001_008E',
#21
'B01001_009E',
#22-24
'B01001_010E',
#25-29
'B01001_011E',
#85+
'B01001_025E')
sapply(1:25,function(x){paste0("B01001_00",x,"E")})
c(seq(0,15,by=5),18,20,21,22,seq(25,85,by=5))
c(seq(4,17,by=5),17,19,20,21,24,seq(29,84,by=5))
CODES <- read_csv("Data/API_CENSUS_CODES.csv") %>% mutate(Med_Age=(Min_Age+Max_Age)/2)
VARS <- CODES$Code
#Testing age Comparison between the two
TEMP <- get_acs(geography="tract",variables=VARS,state='WY',county='lincoln')
KEM <- TEMP %>% filter(GEOID=='56023978400')