Working on zip code management

Data/API_CENSUS_CODES.csv

@@ -0,0 +1,50 @@
+#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

Zip_Code.r

@@ -1,39 +1,34 @@
 library(tidyverse)
 library(tidycensus)
 library(zipcodeR)
-KEY <- '30e13ab22563318ff59286e433099f4174d4edd4'
+install.packages("zipcodeR")
-TRACTS <- get_tracts(search_city('Diamondville','WY')$zipcode) %>% full_join(get_tracts(search_city('Kemmerer','WY')$zipcode))
+install.packages("units")
-TRACTS 
+
+
+#install.packages("terra")
 census_api_key(KEY, install = TRUE)
-vars <- c("P2_002N")
+KEY <- '30e13ab22563318ff59286e433099f4174d4edd4'
-get_decennial(geography="tract",variables=vars,state='WY',county='lincoln',GEOID='56023978400')
+#Ex
-vars <- c("B01001_004","B01001_005")
+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(
+sapply(1:25,function(x){paste0("B01001_00",x,"E")})
-#Under_Five
+c(seq(0,15,by=5),18,20,21,22,seq(25,85,by=5))
-'B01001_003E',
+c(seq(4,17,by=5),17,19,20,21,24,seq(29,84,by=5))
-#5-9
+CODES <- read_csv("Data/API_CENSUS_CODES.csv") %>% mutate(Med_Age=(Min_Age+Max_Age)/2)
-'B01001_004E',
+VARS <- CODES$Code
-#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')
 #Testing age Comparison between the two
 TEMP <- get_acs(geography="tract",variables=VARS,state='WY',county='lincoln')
 KEM <- TEMP %>% filter(GEOID=='56023978400')