Methods 1, Week 7

Outline

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

• Homework questions and overview

• US Census

• tidycensus

• In-class exercise

• Homework

Homework questions and overview

tidycensus

Now that you’ve had some practice, we’re going to create a new project to have a fresh start without your beginner scripts

• Download the new part2 folder and save it to your methods1 folder
• Create new project in your methods1/part2 folder
• Install the tidycensus package: install.packages("tidycensus")

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

Major functions

There are two major functions implemented in tidycensus:

• get_decennial(): grants access to the 2000, 2010, and 2020 decennial US Census APIs
• get_acs(): grants access to the 1-year and 5-year American Community Survey APIs
  • ACS 1-year for 2022 was released Sept 15, 2023

get_decennial()

Help section

library(tidyverse)
library(tidycensus)
library(scales)

# 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

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

• cache = TRUE means it’s faster to load the next time

Data in the Redistricting Dataset

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_tidy = get_decennial(geography = "state", 
                   variables = housing_vars, 
                   year = 2020)

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 easier to work with.

Wide format

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

raw_housing_2020 = get_decennial(geography = "state", 
                   variables = housing_vars, 
                   year = 2020,
                   output = "wide")

Percent Vacant

housing_2020 <-  raw_housing_2020 |>
  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))

Bar plot - code

Use geom_col to create a bar plot of percent vacant

ggplot(data=housing_2020, aes(x=state, 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") 

Bar plot

Plot each state and reorder columns

• Use the reorder() function to alphabetize the states
• Format the y-axis as %

ggplot(data=housing_2020, aes(x=reorder(state,pct_vacant), y=pct_vacant)) +
  geom_col() +
  scale_y_continuous(labels = percent_format(accuracy = 1)) +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) + 
  labs(x = "State", y = "Percent Vacant",
       title = "Proportion of Housing Units that are vacant") 

Plot each state and reorder columns - code

Redistricting Race/Ethnicity data

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,
                       survey = "acs5")