Portable Routines for Preparing CCES and ACS data for MRP
Cite as:
- Shiro Kuriwaki (2020). ccesMRPprep: Functions and Data to Prepare CCES data for MRP. R package. https://www.github.com/kuriwaki/ccesMRPprep
Purpose and Contribution
Multilevel Regression and Poststratification (MRP) is an increasingly popular method for analyzing surveys, and can be implemented on public datasets such as the CCES and ACS. Several helpful tutorials give introductions with sample R code (Kastellec, Lax, and Phillips, 2019; Hanretty, 2019),
But despite its increasingly popularity, doing your own MRP entails
considerable upfront costs specific to the data: downloading the
appropriate survey and contextual, recoding values and generating
post-stratification frames so the survey and census datasets can be
joined. While there already exist some attempts to make a “MRP package”
(e.g. gelman/mrp,
stan-dev/rstanarm,
kuriwaki/sparseregMRP),
often these packages define general functions with specific input
requirements and not enough on data cleaning and data loading in order
to use those functions.
This package instead provides data loading, processing, and formatting
functions for a particular task: using CCES data for MRP. Limiting its
usage to a fixed (but fairly widespread) set of survey data has several
benefits. Its key contributions are functions that are calibrated to a
consistent syntax, pre-built lookup tables and value-key pairs of data
that are based upon a careful reading of data sources, and data loading
functions that use APIs
(IQSS/dataverse-client-r
and walkerke/tidycensus) to
reduce the dependency on downloading large files. Model fitting and
visualization of MRP itself is handled elsewhere (See
kuriwaki/ccesMRPrun).
This package is focused on the preparation to get there.
Installation
# remotes::install_github("kuriwaki/ccesMRPprep")
library(ccesMRPprep)Vignettes
See the vignettes for more long-form workflow overviews
(vignette("overview")) and documentation and explanation of the nature
of the data (vignette("acs")). Otherwise, each function and built-in
data provides documentation as well.
Workflow
See the overview vignette (vignette("overview")) from a illustrative
workflow.
Data Sources
Function-specific pages will detail the documentation used in each function. Here is a manual compilaiton:
| Information | Source | Citation and URL (if public) |
|---|---|---|
| CCES Covariates | Cumulative CCES | Shiro Kuriwaki, “Cumulative CCES Common Content”. https://doi.org/10.7910/DVN/II2DB6 |
| CCES Outcomes | Each Year’s CCES | Stephen Ansolabehere, Sam Luks, and Brian Schaffner. “CCES Common Content” (varies by year). https://cces.gov.harvard.edu/ |
| Poststratification | Census Bureau ACS | American Community Survey. Extracted via tidycensus package. See ACS vignette |
| District-level Contestedness and Incumbency | Collected mainly by Jim Snyder | |
| CD-level Presidential Voteshare | Daily Kos | Daily Kos, The ultimate Daily Kos Elections guide to all of our data sets |
| State-level Presidential Voteshare | MEDSL | MIT Election Data and Science Lab, 2017, “U.S. President 1976–2016”. https://doi.org/10.7910/DVN/42MVDX |
Related Packages
- kuriwaki/rcces has another set of CCES related functions, but these are either my own personal functions in development (not for production), or specific to non-MRP projects.
- kuriwaki/CCES_district-opinion is a private package that uses (among others) this package to process large CCES data for MRP at scale.
Support
This package is a part of the CCES MRP project, supported by NSF Grant 1926424: Bayesian analytical tools to improve survey estimates for subpopulations and small areas. The contents are based on collaborations with Ben Bales, Lauren Kennedy, Mitzi Morris, and Soichiro Yamauchi.