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CongressData: A Functional Tool for the CongressData Dataset

CongressData is a package designed to allow a user with only basic knowledge of R interact with CongressData, a dataset with nearly 800 variables that compiles information about all US congressional districts across 1789-2023, and its codebook. The dataset tracks district characteristics, members of congress, and the behavior of those members in policymaking. Users can find variables related to demographics, politics, and policy; subset the data across multiple dimensions; create custom aggregations of the dataset; and access citations in both plain text and BibTeX for every variable.

Installing this Package

CongressData is a functional package that interacts with the CongressData dataset via the internet. Install the package from GitHub like so:

# use the devtools library to download the package from GitHub
library(devtools)

# if there are issues or you only want to download CongressData
install_github("ippsr/CongressData")

Finding Variables

get_var_info: Retrieve information regarding variables in CongressData and identify variables of interest with get_var_info. The function allows you to search to codebook to find the years each variable is observed in the data; a short and long description of each variable; and the source and citation/s for each variable. Citations are available in both bibtex and plain text. Use the function to search for broad terms like ‘tax’ with the related_to argument and/or partial-match variable names with var_names.

suppressMessages(library(dplyr))
library(CongressData)
#> Please cite:
#> Grossmann, M., Lucas, C., McCrain, J, & Ostrander, I. (2022). CongressData.
#> East Lansing, MI: Institute for Public Policy and Social Research (IPPSR).
#> 
#> Run `CongressData::get_congress_version()` to print the version of CongressData the package is using.
# variables related to health insurance
h_ins_cong <- get_var_info(related_to = "health insurance")

cat("There are",nrow(h_ins_cong),"variables related to health insurance in CongressData")
#> There are 41 variables related to health insurance in CongressData
head(h_ins_cong$variable)
#> [1] "percent_under18_healthins" "percent_private_under18"  
#> [3] "percent_public_under18"    "percent_privpub_under18"  
#> [5] "percent_pop18_34"          "percent_private_18_34"
# variables with 'under18' in their name
under18_cong <- get_var_info(var_names = "under18")

head(under18_cong$variable)
#> [1] "percent_under18"           "percent_under18_healthins"
#> [3] "percent_private_under18"   "percent_public_under18"   
#> [5] "percent_privpub_under18"   "under18"

get_var_info returns the following information to simplify using CongressData:

  • variable: Variable name
  • year: The precise years the variable is observed
  • short_desc: A short description of the variable
  • long_desc: A long description of the variable
  • source: The sources of the data
  • category: the variable’s category (not all are coded)
  • plaintext_cite[1-4]: Plain text citation(s) for the data
  • bibtext_cite[1-4]: BibTeX citation(s) for the data

Accessing CongressData

get_cong_data: Access all or a part of CongressData with get_cong_data. Subset by state names with state and years with years (either a single year or a two-year vector that represents the min/max of what you want). You can also use the related_to argument to search across variable names, short/long descriptions from the codebook, and citations for non-exact matches of a supplied term. For example, searching ‘tax’ will return variables with words like ‘taxes’ and ‘taxable’ in any of those columns.

# load the entire dataset
all_the_dat <- get_cong_data()

# subset by state, topic, and years
cong_subset <- get_cong_data(states = c("Indiana","Kentucky","Michigan")
                             ,related_to = "tax"
                             ,years = c(1960,1980))

Run get_congress_version to see what version of the dataset is available in CongressData.

CongressData::get_congress_version()
#> You are using CongressData version: 1.1

Pulling Citations

get_var_info: Each variable in CongressData was collected from external sources, please use get_var_info to obtain their citations (plain text and BibTeX). We’ve made it easy to cite the source of each variable you use with the get_var_info function described above. Supply a vector of variable names to the function with the var_names function and collect the citations provided in the plain text or BibTeX columns. NOTE: Some variables have multiple citations, so do check you have them all.

# bibtex is also available
get_var_info(var_names = "com_benghazi_299") %>%
  pull(plaintext_cite)
#> [1] "Charles Stewart III and Jonathan Woon. Congressional Committee Assignments, 103rd to 114th Congresses, 1993--2017: House of Representatives, 2017.\n"
# bibtex is also available
get_var_info(var_names = "percent_bus") %>%
  pull(plaintext_cite)
#> [1] "U.S. Census Bureau. (2022). 2009-2019 American Community Survey 1-year Estimates. Retrieved from the Census Bureau Data API."

Dataset and Package Citation

In addition to citing each variable’s source, we ask that you cite CongressData if use this package or the dataset. A recommended citation is below.

Grossmann, M., Lucas, C., McCrain, J, & Ostrander, I. (2022). CongressData. East Lansing, MI: Institute for Public Policy and Social Research (IPPSR)

Contact

For questions about the CongressData dataset, contact Ben Yoel ([email protected]).