Quickly Find, Extract, and Marginalize U.S. Census Tables.
easycensus
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 races
We 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 varia…¹ value sex age race_…²
#> <chr> <chr> <chr> <dbl> <fct> <fct> <fct>
#> 1 02180000100 Census Tract 1, Nome Census Ar… P012002 3053 male total total
#> 2 02180000100 Census Tract 1, Nome Census Ar… P012003 359 male unde… total
#> 3 02180000100 Census Tract 1, Nome Census Ar… P012004 318 male 5 to… total
#> 4 02180000100 Census Tract 1, Nome Census Ar… P012005 294 male 10 t… total
#> 5 02180000100 Census Tract 1, Nome Census Ar… P012006 165 male 15 t… total
#> 6 02180000100 Census Tract 1, Nome Census Ar… P012007 130 male 18 a… total
#> 7 02180000100 Census Tract 1, Nome Census Ar… P012008 53 male 20 y… total
#> 8 02180000100 Census Tract 1, Nome Census Ar… P012009 47 male 21 y… total
#> 9 02180000100 Census Tract 1, Nome Census Ar… P012010 150 male 22 t… total
#> 10 02180000100 Census Tract 1, Nome Census Ar… P012011 204 male 25 t… total
#> # … with 950 more rows, and abbreviated variable names ¹variable,
#> # ²race_ethnicity
#> # ℹ Use `print(n = ...)` to see more rows
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 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
#> # … with 404 more rows
Finally, 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_f…¹ age_to
#> <chr> <chr> <fct> <fct> <dbl> <dbl> <dbl>
#> 1 02180000100 Census Tract 1, Nome Census Are… 10 t… aian 524 10 14
#> 2 02180000100 Census Tract 1, Nome Census Are… 10 t… asian 1 10 14
#> 3 02180000100 Census Tract 1, Nome Census Are… 10 t… black 1 10 14
#> 4 02180000100 Census Tract 1, Nome Census Are… 10 t… hisp 3 10 14
#> 5 02180000100 Census Tract 1, Nome Census Are… 10 t… nhpi 0 10 14
#> 6 02180000100 Census Tract 1, Nome Census Are… 10 t… other 0 10 14
#> 7 02180000100 Census Tract 1, Nome Census Are… 10 t… two 23 10 14
#> 8 02180000100 Census Tract 1, Nome Census Are… 10 t… white 11 10 14
#> 9 02180000100 Census Tract 1, Nome Census Are… 10 t… whit… 10 10 14
#> 10 02180000100 Census Tract 1, Nome Census Are… 15 t… aian 293 15 17
#> # … with 404 more rows, and abbreviated variable name ¹age_from
Dive into the reference to learn more!
Installation
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")