Is it possible to get the US population by state age 12 or older? I'm trying to use the tidycensus package, but I'm not sure how to limit the count to add the age restriction.
library(tidycensus)
library(tidyverse)
census_api_key("MYKEY")
pop90 <- get_acs(geography = "state", variables = "B01003_001", year = 1990)
The "Universe" for that particular variable"B01003-001" is TOTAL POPULATION, it is not broken down any further than that, so you are not able to get the 12+ ages from "B01003-001", only the population of the whole state or county or tract you are pulling from at the time.
However, you can pull and aggregate a data frame for the tables you would like, using B01001 and the suffixes _001 through _049 to pull out the populations by age and gender and then add them up.
OR
You could pull the entire population as you have above and subtract out the ages (both male and female NOT in your target group, which is much less work given the breakdown of age groups for children compared to the rest of life)
The one thing you will have a hard time with is getting 12+, because the highest groupings you would want to exclude are 10-14...which means you could not select under 12
General Age by Sex for All Races Codes:
B01001_001 Total:
B01001_002 Male:
B01001_003 Male: Under 5 years
B01001_004 Male: 5 to 9 years
B01001_005 Male: 10 to 14 years
B01001_006 Male: 15 to 17 years
B01001_007 Male: 18 and 19 years
B01001_008 Male: 20 years
B01001_009 Male: 21 years
B01001_010 Male: 22 to 24 years
B01001_011 Male: 25 to 29 years
B01001_012 Male: 30 to 34 years
B01001_013 Male: 35 to 39 years
B01001_014 Male: 40 to 44 years
B01001_015 Male: 45 to 49 years
B01001_016 Male: 50 to 54 years
B01001_017 Male: 55 to 59 years
B01001_018 Male: 60 and 61 years
B01001_019 Male: 62 to 64 years
B01001_020 Male: 65 and 66 years
B01001_021 Male: 67 to 69 years
B01001_022 Male: 70 to 74 years
B01001_023 Male: 75 to 79 years
B01001_024 Male: 80 to 84 years
B01001_025 Male: 85 years and over
B01001_026 Female:
B01001_027 Female: Under 5 years
B01001_028 Female: 5 to 9 years
B01001_029 Female: 10 to 14 years
B01001_030 Female: 15 to 17 years
B01001_031 Female: 18 and 19 years
B01001_032 Female: 20 years
B01001_033 Female: 21 years
B01001_034 Female: 22 to 24 years
B01001_035 Female: 25 to 29 years
B01001_036 Female: 30 to 34 years
B01001_037 Female: 35 to 39 years
B01001_038 Female: 40 to 44 years
B01001_039 Female: 45 to 49 years
B01001_040 Female: 50 to 54 years
B01001_041 Female: 55 to 59 years
B01001_042 Female: 60 and 61 years
B01001_044 Female: 65 and 66 years
B01001_045 Female: 67 to 69 years
B01001_046 Female: 70 to 74 years
B01001_047 Female: 75 to 79 years
B01001_048 Female: 80 to 84 years
B01001_049 Female: 85 years and over
So you willl need to adjust your model in some way or get PUMS data and aggregate to your liking.