Library

library(here)
library(tidyverse)
library(tidycensus)
library(kableExtra)

Data

#Load US Census region data
census_regions <- readxl::read_excel(here::here("data/raw/Census_Data_SVI/census_regions.xlsx"))

# View divisions
census_regions %>% select(Division) %>% distinct()

## # A tibble: 9 × 1
##   Division                   
##   <chr>                      
## 1 New England Division       
## 2 Middle Atlantic Division   
## 3 East North Central Division
## 4 West North Central Division
## 5 South Atlantic Division    
## 6 East South Central Division
## 7 West South Central Division
## 8 Mountain Division          
## 9 Pacific Division

import::here( "census_division",
             # notice the use of here::here() that points to the .R file
             # where all these R objects are created
             .from = here::here("analysis/project_data_steps_courtney.R"),
             .character_only = TRUE)

census_division

## [1] "Middle Atlantic Division"

# Load API key, assign to TidyCensus Package, remember do not print output
source(here::here("analysis/password.R"))
tidycensus::census_api_key(census_api_key)

Census Variable Data Dictionary

census_variables <- load_variables(2020, "acs5/subject", cache = TRUE)

census_variables %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

Division Population

# Query Census API via tidyverse
acs_pull <- get_acs(geography = "division", 
              variables = c("S0101_C01_001", "S0101_C03_001", "S0101_C05_001"), 
              year = 2020) %>% filter(NAME == census_division)

# Join data set with census_variable df
left_join(acs_pull, census_variables, join_by("variable" == "name")) %>% mutate("year" = "2020") %>% kbl(format.args = list(big.mark = ",")) %>% kable_styling() %>% scroll_box(width = "100%")