r-从ACS数据中获得家庭收入中位数

Question

下面的代码完美地返回了我所需要的：使用2019年ACS的每只美洲狮的家庭收入中位数(1年(。然而，缺少的是国家名称。我尝试了state＝"的选项；所有"；但它没有起作用。我如何获得各州和美洲狮感兴趣的数据？

谢谢，

NM

PUMA_level <- get_acs(geography = "puma",
variable = "B19013_001",
survey = "acs1",
# state="all",
year = 2019)

Answer 1

使用usmap::fips_info函数，您可以获得州代码、名称和缩写的列表，然后可以将其合并到人口普查数据中，如下所示：

library(tidycensus)
library(usmap)
PUMA_level <- get_acs(geography = "puma",
variable = "B19013_001",
survey = "acs1",
year = 2019,
keep_geo_vars = TRUE)
#> Getting data from the 2019 1-year ACS
#> The 1-year ACS provides data for geographies with populations of 65,000 and greater.
PUMA_level$fips <- substr(PUMA_level$GEOID, 1, 2)
states <- usmap::fips_info(unique(PUMA_level$fips))
#> Warning in get_fips_info(fips_, sortAndRemoveDuplicates): FIPS code(s) 72 not
#> found
PUMA_level <- merge(PUMA_level, states, by = "fips")
head(PUMA_level)
#>   fips   GEOID
#> 1   01 0100100
#> 2   01 0100200
#> 3   01 0100302
#> 4   01 0100400
#> 5   01 0100500
#> 6   01 0100301
#>                                                                                         NAME
#> 1                  Lauderdale, Colbert, Franklin & Marion (Northeast) Counties PUMA; Alabama
#> 2 Limestone & Madison (Outer) Counties--Huntsville City (Far West & Southwest) PUMA, Alabama
#> 3                                            Huntsville City (Central & South) PUMA, Alabama
#> 4                                                    DeKalb & Jackson Counties PUMA, Alabama
#> 5     Marshall & Madison (Southeast) Counties--Huntsville City (Far Southeast) PUMA, Alabama
#> 6                                   Huntsville (North) & Madison (East) Cities PUMA, Alabama
#>     variable estimate  moe abbr    full
#> 1 B19013_001    46449 3081   AL Alabama
#> 2 B19013_001    74518 6371   AL Alabama
#> 3 B19013_001    51884 5513   AL Alabama
#> 4 B19013_001    43406 3557   AL Alabama
#> 5 B19013_001    56276 3216   AL Alabama
#> 6 B19013_001    63997 5816   AL Alabama