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lorae / household-size

Analyze the extent to which demographic changes can account for changes in American household sizes since 2000.
MIT License
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Introduction

The purpose of this project is to analyze the extent to which demographic changes in the U.S. population can account for changes in average American household size since 2000. The methodology, based upon Galster (2024), is to construct a multidimensional matrix, with each cell representing a unique combination of demographic (age, sex, race, etc) and geographic (PUMA) characteristics. The value in each cell represents the average household size among persons with that characteristic in the year 2000. The methodology is then applied to data from the year 2020 to determine which demographic groups have the largest changes.

The project uses American Community Survey (ACS) IPUMS microdata, which are freely available to the public after registering for an API key.

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Project Structure

The following is an overview of the project's file structure:

household-size/
├── README.md                  # The document you're currently reading
├── household-size.Rproj       # RStudio project file
├── renv/                      # R environment directory
│   ├── activate.R             
│   ├── settings.dcf           
│   └── library/               
├── src/                       # Source code directory
│   ├── utils/                 # Utility scripts
│   │   ├── bucketing-tools.R  # Functions for bucketing data
│   │   ├── create-summary-array.R  # May be deprecated soon
│   │   └── create-synthetic-data.R # For testing? Maybe move to tests/
│   └── sample_code_for_sarah.R # Demonstrates SQL requests using lookup tables using DuckDB
│   
└── tests/                     # Testing directory
    ├── testthat/              # Unit tests directory
    │   ├── test-range-match-lookup-db.R # Tests range_match_lookup() function on a database
    │   ├── test-range-match-lookup-df.R # Tests range_match_lookup() function on a tibble
    │   └── test-bucketing.R   # Test script for bucketing functions
    └── test-data/             # Mock data for testing
        ├── mock_data.csv
        ├── mock_age_lookup.csv
        ├── mock_income_lookup.csv
        ├── expected_age_output.csv
        └── expected_income_output.csv

Project Setup

The project is set up as an R project with renv to manage dependencies. When you open the project in RStudio, renv will automatically load the required environment, and you will have access to the correct versions of all the necessary packages.

If you're running the code for the first time...

  1. Sign up for an IPUMS account and obtain an API key.

  2. Clone the repository:

    git clone https://github.com/lorae/household-size.git

  3. Open the R project file (household-size.Rproj) in RStudio.

  4. Verify that RTools is already installed on your computer. RTools is required for compiling some of the packages in this project's library.

    a. Load the pkgbuild package, installing if necessary:

    ```r
if (!requireNamespace("pkgbuild", quietly = TRUE)) {
  install.packages("pkgbuild")
}
library("pkgbuild")
```

    b. Use the find_rtools() function from the pkgbuild package to verify that RTools is installed and configured.

    ```r
pkgbuild::find_rtools()
```

If the output is `[1] TRUE`, RTools is installed and ready to use. Proceed to step 5.

If the output is `[2] FALSE`:

  i. Verify your current version of R by running 

      ```r 
      R.version.string
      ```
  ii. Navigate to https://cran.r-project.org/bin/windows/Rtools/. Download the version of RTools consistent with your version of R: For example, install RTools44 for version 4.4.0 and above, and RTools43 for version 4.3.0 and above.

  iii. Close any instances of R and RStudio and open the executable to follow installation instructions. Once downloaded, reopen RStudio rerun the above steps to verify proper RTools installation.

  5. Install the renv package by running the following command in R:

    install.packages("renv")

  6. Run renv::restore() to install all the required packages as specified in the renv.lock file:

    renv::restore()

    This will ensure that your environment is set up with the same package versions used in the project.

  7. Run the main script to perform the analysis.

    [TODO: instructions here]

  8. View the results as needed.

If you're running the code any subsequent time...

  1. Open the R project file (household-size.Rproj) in RStudio.

  2. Run renv::restore() to install all the required packages as specified in the renv.lock file:

    renv::restore()

    This will ensure that your environment is set up with the same package versions used in the project.

  3. Run the main script to perform the analysis.

    [TODO: instructions here]

  4. View the results as needed.

FAQ

  1. How granular is this analysis, geographically?

    The Census Bureau divides the United States into Public Use Microdata Areas (PUMAs). They're designed to collectively cover the entire U.S. population, not cross state boundaries, and "contain no fewer than 100,000 people each." There are roughly 2,000 PUMAs total.

    Due to migration and demographic shifts, the Census Bureau is continually redrawing the boundaries of its PUMAs. This can pose a challenge for comparing values in the same region over multiple years. Luckily, for this purpose, the Bureau also defines Constant Public Use Microdata Areas (ConsPUMAs, or CPUMAs) for various date ranges. The ConsPUMAs we use in this analysis are CPUMA0010, which "represents areas that are consistent among 2000 and 2010 PUMAs" and "can be consistently identified from the geographic codes available in U.S. Census PUMS from 2000 to 2021 samples."

    Compared to PUMAs, ConsPUMAs are larger: there 1,085 ConsPUMAS defined in CPUMA0010. The land area of these ConsPUMAs varies widely: in unpopulated regions, they can be quite large, while in populated regions they can be quite small. In Minnesota, for example, the smallest ConsPUMA encompass only a small portion of Minneapolis, while the largest ConsPUMA encompasses much of the state.

    Map of Minnestota PUMAs in 2020

  2. How granular is this analysis, demographically?

    The variables we use to bin individuals into groups are the following. See our codebook [link to codebook] for more information.

    Age: 5-year bins:

    • 0-4 years
    • 5-9 years
    • 10-14 years
    • 15-19 years
    • 20-24 years
    • 25-29 years
    • 30-24 years
    • ...
    • 75-79 years
    • 80-84 years
    • 85+

    [perhaps a table of how many individuals fall in each age group?]

    Race: Add info here

    Household income: Add information here on how it's defined, whether it's pre-tax/transfer, whether it's only earned or earned and unearned, how it's deflated to compare over time, etc.

Unit Testing

Tests are located in the tests/testthat folder. To run all tests:

library("testthat")
test_dir("tests/testthat/")

Performance Testing

Ad hoc performance tests can be run suing the profvis() function in the profvis package. For example, to test the performance of main.R, run:

library("profvis")
profvis(source("main.R"))

Data Validation

As we process the data, we run several tests to validate that the pipeline is working as expected. The module containing these validation functions is located at src/utils/data-validation.R.

Current data checks include:

References