A couple of points here:
- Must use acs5 or decennial (acs1 not published to this level of detail)
- This is a table of the real world names to the internal/terse SAS names
- The total list is 21k variables, so you probably need to filter to something
library(dplyr)
library(tidycensus)
View(
load_variables(year="2020", dataset="acs5", cache=TRUE)
%>% filter(geography == "block group")
%>% filter(grepl('race', concept, ignore.case=TRUE)))