Methods 1, Week 6

tidycensus

Today we talk about the US Census and the tidycensus package.

• tidycensus requires a key: get it here.
• Create new project from new folder: methods1/class5
• Install the tidycensus package: install.packages("tidycensus")

Outline

• Homework questions and overview

• US Census

• tidycensus

• In-class exercise

• Homework

The decennial US Census

• The U.S. census counts every resident of the country every ten years (year ending in zero).
• The Constitution mandates the count.
• Conducted a survey every ten years since 1790.
• The Census Bureau counts each resident where they live on April 1.
• The census is not a count of citizens, it is a count of residents.

Uses of the census: Apportionment

• The process of dividing the 435 seats in the U.S. House of Representatives among the 50 states, based on the state population counts.

Uses of the census: Redistricting

• Provides population data to adjust or redraw electoral districts.

Uses of the census: Allocate federal money

Helps determine how federal funds are distributed across the country, including:

• funding for schools, hospitals, roads, and public infrastructure.
• Medicaid, Head Start, block grant programs for community mental health services, and the Supplemental Nutrition Assistance Program (SNAP).

Uses of the census:

• Research
• Maps

The 1-year and 5-year American Community Survey

The American Community Survey (ACS) is a demographics survey conducted by the US Census Bureau every year since 2005.

• The Census sends surveys to a randomly selected 3.5 million addresses every year

ACS data are estimates. ACS data has a margin of error, and it is larger for areas with smaller populations.

Comparison

Geographic levels

Smallest areas

Which census data should you use?

It always depends on your analysis questions!

Decennial vs ACS

• The Decennial Census is an actual count, not an estimate. Use it when possible.
  
• The ACS is useful for questions not included in the decennial survey
  • becomes more useful later in the decade.

If you use ACS, never forget that it is an estimation.

Question changes

• The questions and methods of collection change constantly

  • historical overiew

Prison gerrymandering

The Census Bureau counts incarcerated people as residents of the district where they are confined.

• A few states now reallocate incarcerated people from where they are incarcerated to their last residence.
• Most do not.

tidycensus

tidycensus is a package that imports census data directly from the U.S. Census as tidyverse-ready dataframes. It is very nice.

• Overview
• Basic usage
• You can also look at the ChangeLog to see what is available for the 2020 census.

Major functions

There are two major functions implemented in tidycensus:

• get_decennial(): grants access to the 2000, 2010, and 2020 decennial US Census APIs
  • only the redistricting file - “pl” - is available from the 2020 decennial census
• get_acs(): grants access to the 1-year and 5-year American Community Survey APIs
  • ACS 1-year for 2021 was released Sept 15, 2022 (more release notes)

get_decennial()

Install census api key

Install tidycensus and the census API key

Get Census API key to use tidycensus here.

In your console:

• install.packages("tidycensus")

• census_api_key("put your census api key here", install = TRUE)

Help section

library(tidyverse)
library(tidycensus)


# look at the help section for the load_variables() function 
# run the line of code below in your console and look at the help section
?load_variables

HELP

List 2020 Census variables

The 2020 Census data release is very delayed from covid

# create table of all variables in the 2020 redistricting file
pl_2020 <- load_variables(2020, "pl", cache = T)

Redistricting Race/Ethnicity data

• P1. Race
• P2. Hispanic or Latino, and Not Hispanic or Latino by Race
• P3. Race for the Population 18 Years and Over
• P4. Hispanic or Latino, and Not Hispanic or Latino by Race for the Population 18 Years and Over
• P5. Group Quarters Population by Major Group Quarters Type
• H1. Occupancy Status

Import Housing Units data

housing_units <- get_decennial(geography = "state",
                             variables = c(housing_units = "H1_001N"), 
                             year = 2020)

A question:

What percentage of housing units receive an American Community Survey each year?

The answer

# ACS questionaires go to 3.5 million addresses each year
acs_percent <- 3500000/sum(housing_units$value)

acs_percent

[1] 0.02463108

Import multiple columns

housing_vars = c("H1_001N", "H1_002N", "H1_003N")

raw_housing_2020 = get_decennial(geography = "state", 
                   variables = housing_vars, 
                   year = 2020) %>% 
  arrange(NAME)

Tidy data

Each state has 3 rows, one for each of the 3 housing variables. This is called long format.

We want the data to be in wide format to make it tidy:

• meaning each row is an observation with all of the variables associated with the observation in a column.

Pivot_wider

We’ll use pivot_wider() to convert from long to wide format:

Then rename the columns to be more descriptive

housing_2020 <-  raw_housing_2020 %>%
  pivot_wider(names_from = variable, values_from = value) %>% 
  rename(tot_housing_units = H1_001N,
         occupied = H1_002N,
         vacant = H1_003N) 

Tidy data

There are three interrelated rules which make a dataset tidy:

• Each observation has its own row.
• Each variable has its own column.
• Each value has its own cell.

Percent Vacant

Now calculate percent occupied and percent vacant

housing_2020 <-  raw_housing_2020 %>%
  pivot_wider(names_from = variable, values_from = value) %>% 
  rename(state = NAME,
         tot_housing_units = H1_001N,
         occupied = H1_002N,
         vacant = H1_003N) %>% 
  mutate(pct_occupied = round(occupied/tot_housing_units, 3),
         pct_vacant = round(vacant/tot_housing_units, 3))

Plot each state

Bar plot

Use geom_col to create a bar plot

vacant_housing_plot <- ggplot(data=housing_2020, 
                              aes(x=reorder(state,pct_vacant), 
                                  y=pct_vacant)) +
  geom_col() +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) + 
  labs(x = "State", y = "Percent Vacant Housing Units") 

In-class Analysis

Use the get_decennial function to create a state dataframe with the following variables:

Assignment 6a: Reading

• Read the census questionnaire for 2020 (and previous years if you want to)

• Read Black Is Over (Or, Special Black) by Tressie McMillan Cottom.

Assignment 6b: Tidycensus

Assignment 6b: specific instructions

raw_mhi_2020 = get_acs(geography = "state", 
                                 variables = c(mhi = "B19013_001"), 
                                 year = 2020)