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 find_dec_table() function to help you locate exactly which decennial census table to use to get the data you want.
library(easycensus)
find_dec_table(age, race)
#>
#> ── Top 2 matching tables ───────────────────────────────────────────────────────
#>
#> P012 - SEX BY AGE
#> 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
#>
#> PCT012 - SEX BY AGE
#> 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 P012 or table PCT012, 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 get_dec_table().
d_cens = get_dec_table("tract", "P012", state="AK", county="Nome")
print(d_cens)
#> # A tibble: 9,600 × 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 02180000200 Census Tract 2, Nome C… P012002 2005 male total total
#> 3 02180000100 Census Tract 1, Nome C… P012003 359 male unde… total
#> 4 02180000200 Census Tract 2, Nome C… P012003 175 male unde… total
#> 5 02180000100 Census Tract 1, Nome C… P012004 318 male 5 to… total
#> 6 02180000200 Census Tract 2, Nome C… P012004 132 male 5 to… total
#> 7 02180000100 Census Tract 1, Nome C… P012005 294 male 10 t… total
#> 8 02180000200 Census Tract 2, Nome C… P012005 161 male 10 t… total
#> 9 02180000100 Census Tract 1, Nome C… P012006 165 male 15 t… total
#> 10 02180000200 Census Tract 2, Nome C… P012006 90 male 15 t… total
#> # … with 9,590 more rowseasycensus is built on top of the great tidycensus package, so all of the usual arguments to those functions (including the ability to get shapefile information) work here, too.
Once 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 marginalize() 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") %>%
marginalize(age, race=race_ethnicity)
print(d_cens)
#> # A tibble: 414 × 5
#> # Groups: GEOID, NAME, age [46]
#> GEOID NAME age race value
#> <chr> <chr> <fct> <fct> <dbl>
#> 1 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… amer… 5240
#> 2 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… asia… 10
#> 3 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… blac… 10
#> 4 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… hisp… 30
#> 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 … 230
#> 8 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… whit… 110
#> 9 02180000100 Census Tract 1, Nome Census Area, Alaska 10 to 14 ye… whit… 100
#> 10 02180000100 Census Tract 1, Nome Census Area, Alaska 15 to 17 ye… amer… 2930
#> # … with 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
#> # Groups: GEOID, NAME, age [46]
#> 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 5240 10 14
#> 2 02180000100 Census Tract 1, Nome Census Ar… 10 t… asian 10 10 14
#> 3 02180000100 Census Tract 1, Nome Census Ar… 10 t… black 10 10 14
#> 4 02180000100 Census Tract 1, Nome Census Ar… 10 t… hisp 30 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 230 10 14
#> 8 02180000100 Census Tract 1, Nome Census Ar… 10 t… white 110 10 14
#> 9 02180000100 Census Tract 1, Nome Census Ar… 10 t… whit… 100 10 14
#> 10 02180000100 Census Tract 1, Nome Census Ar… 15 t… aian 2930 15 17
#> # … with 404 more rowsDive into the reference to learn more!
You can install the development version from GitHub with: