Extracting desired data using the proper Census variable names can be time-consuming. This package takes the pain out of that process.
The use case is best illustrated by an example. Suppose you want age-by-race information at the tract level. Unfortunately, the Census Bureau doesn’t publish a specific age-by-race table. You could build one yourself from public-use microdata, but that lacks tract-level geographic information, for privacy reasons. So you are left trying to find an existing Census product that you can extract age-by-race information from.
Unless you’re a Census pro, you won’t know what exactly what is off
the top of your head. But suppose you know you’d like to get the data
from the decennial census, since it covers the whole nation and asks
about age and race. easycensus provides the
cens_find_dec() function to help you locate exactly which
decennial census table to use to get the data you want.
library(easycensus)
cens_find_dec(age, race)
#>
#> ── Top 2 matching tables ───────────────────────────────────────────────────────
#>
#> P12 - SEX BY AGE
#> Surveys / Files:
#> ✔ Decennial / Summary File 1
#> Parsed variables:
#> • sex
#> • age
#> • race_ethnicity
#> Example values:
#> • female / 45 to 49 years / black or african american alone
#> • female / 20 years / two or more races
#> • male / 55 to 59 years / american indian and alaska native alone
#>
#> PCT12 - SEX BY AGE
#> Surveys / Files:
#> ✔ Decennial / Summary File 1
#> Parsed variables:
#> • sex
#> • age
#> • race_ethnicity
#> Example values:
#> • female / 70 years / total
#> • female / 85 years / native hawaiian and other pacific islander alone
#> • female / 30 years / two or more racesWe can see right away that our best bet is either table
P12 or table PCT12, depending on whether we
want age in 5-year groups or down to individual years. Let’s say you’re
OK with the five-year bins. Then all you need to do to get your data is
to call cens_get_dec().
d_cens = cens_get_dec("P12", "tract", state="AK", county="Nome")
print(d_cens)
#> # A tibble: 960 × 7
#> GEOID NAME variable value sex age race_ethnicity
#> <chr> <chr> <chr> <dbl> <fct> <fct> <fct>
#> 1 02180000100 Census Tract 1, Nome C… P012002 3053 male total total
#> 2 02180000100 Census Tract 1, Nome C… P012003 359 male unde… total
#> 3 02180000100 Census Tract 1, Nome C… P012004 318 male 5 to… total
#> 4 02180000100 Census Tract 1, Nome C… P012005 294 male 10 t… total
#> 5 02180000100 Census Tract 1, Nome C… P012006 165 male 15 t… total
#> 6 02180000100 Census Tract 1, Nome C… P012007 130 male 18 a… total
#> 7 02180000100 Census Tract 1, Nome C… P012008 53 male 20 y… total
#> 8 02180000100 Census Tract 1, Nome C… P012009 47 male 21 y… total
#> 9 02180000100 Census Tract 1, Nome C… P012010 150 male 22 t… total
#> 10 02180000100 Census Tract 1, Nome C… P012011 204 male 25 t… total
#> # ℹ 950 more rowsOnce you’ve gotten your labeled data, it’s easy to marginalize out
the unneeded sex variable. You can either use
group_by() and summarize() as usual, or you
can use the cens_margin_to() function in
easycensus. This has the added advantage of automatically
handling margins of error for ACS data.
library(dplyr)
d_cens = d_cens %>%
# Drop table margins. Can also use `drop_total=TRUE` in `get_dec_table()`
filter(age != "total", race_ethnicity != "total") %>%
cens_margin_to(age, race=race_ethnicity)
print(d_cens)
#> # A tibble: 414 × 5
#> GEOID NAME age race value
#> <chr> <chr> <fct> <fct> <dbl>
#> 1 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… amer… 524
#> 2 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… asia… 1
#> 3 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… blac… 1
#> 4 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… hisp… 3
#> 5 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… nati… 0
#> 6 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… some… 0
#> 7 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… two … 23
#> 8 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… whit… 11
#> 9 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… whit… 10
#> 10 02180000100 Census Tract 1, Nome Census Area, Alaska 15 to 17 ye… amer… 293
#> # ℹ 404 more rowsFinally, you might want to simplify the age and race labels, since
they are kind of verbose. easycensus provides a set of
tidy_*() functions to assist with this.
d_cens %>%
mutate(race = tidy_race(race),
tidy_age_bins(age))
#> # A tibble: 414 × 7
#> GEOID NAME age race value age_from age_to
#> <chr> <chr> <fct> <fct> <dbl> <dbl> <dbl>
#> 1 02180000100 Census Tract 1, Nome Census Ar… 10 t… aian 524 10 14
#> 2 02180000100 Census Tract 1, Nome Census Ar… 10 t… asian 1 10 14
#> 3 02180000100 Census Tract 1, Nome Census Ar… 10 t… black 1 10 14
#> 4 02180000100 Census Tract 1, Nome Census Ar… 10 t… hisp 3 10 14
#> 5 02180000100 Census Tract 1, Nome Census Ar… 10 t… nhpi 0 10 14
#> 6 02180000100 Census Tract 1, Nome Census Ar… 10 t… other 0 10 14
#> 7 02180000100 Census Tract 1, Nome Census Ar… 10 t… two 23 10 14
#> 8 02180000100 Census Tract 1, Nome Census Ar… 10 t… white 11 10 14
#> 9 02180000100 Census Tract 1, Nome Census Ar… 10 t… whit… 10 10 14
#> 10 02180000100 Census Tract 1, Nome Census Ar… 15 t… aian 293 15 17
#> # ℹ 404 more rowsDive into the reference to learn more!
You can install the released version of easycensus from CRAN with:
install.packages("easycensus")Or install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("CoryMcCartan/easycensus")