The filibustr package provides data utilities for research on the U.S.
Congress. This package provides a uniform interface for accessing data
from sources such as Voteview, the Legislative Effectiveness Scores, and
more. Accessing your data using these functions removes many of the
manual steps involved with importing data. This has two primary
benefits:
- Speeding up your workflow and enabling you to quickly experiment with a variety of data choices.
- Ensuring you always have the most up-to-date data.
filibustr is inspired by the
baseballr package, which
provides similar conveniences for baseball analytics data.
You can install the stable version of filibustr from CRAN with:
install.packages("filibustr")You can install the development version of filibustr from GitHub with:
# install.packages("devtools")
devtools::install_github("feinleib/filibustr")There are four functions that retrieve data from Voteview:
get_voteview_members(): data on members (Presidents, Senators, and Representatives).get_voteview_parties(): data on parties (size and ideology)get_voteview_rollcall_votes(): results of recorded votes (overall results, not positions of individual members)get_voteview_member_votes(): individual members’ votes on recorded votes
These functions share a common interface. Here are their arguments:
chamber: Which chamber to get data for. Options are:"all","congress": Both House and Senate data (the default)."house","h","hr": House data only."senate","s","sen": Senate data only. These options are case-insensitive. If you explicitly pass a different value, it will default to “all” with a warning.
Note: For get_voteview_members() and get_voteview_parties(),
presidents are included in all datasets. Therefore, reading both
"house" and "senate" data will duplicate data on the presidents. The
recommended way to get all data is to use the default argument, "all".
-
congress: A whole number (to get data for a single Congress), or a numeric vector (to get data for a set of congresses). Optional; will retrieve data for all Congresses by default. If specified, Congress numbers cannot be greater than thecurrent_congress()(i.e., you cannot try to get future data). -
local: Whether to read the data from a local file, as opposed to the Voteview website. Default isTRUE. If the local file does not exist, will fall back to reading from online. -
local_dir: The directory containing the local file. Defaults to the working directory.
Note: Especially when working with large datasets, reading data from Voteview can take a long time. If you are repeatedly loading the same static dataset (i.e., not including information from the current Congress), it may be useful to download the dataset as a CSV from Voteview so you can read that local file instead of having to use the web interface.
For demonstration, here is the table returned by
get_voteview_parties().
library(filibustr)
get_voteview_parties()
#> # A tibble: 840 × 9
#> congress chamber party_code party_name n_members nominate_dim1_median
#> <int> <fct> <int> <fct> <int> <dbl>
#> 1 1 President 5000 Pro-Administrat… 1 NA
#> 2 1 House 4000 Anti-Administra… 29 0.018
#> 3 1 House 5000 Pro-Administrat… 31 0.576
#> 4 1 Senate 4000 Anti-Administra… 9 -0.238
#> 5 1 Senate 5000 Pro-Administrat… 20 0.427
#> 6 2 President 5000 Pro-Administrat… 1 NA
#> 7 2 House 4000 Anti-Administra… 32 -0.022
#> 8 2 House 5000 Pro-Administrat… 40 0.533
#> 9 2 Senate 4000 Anti-Administra… 14 -0.392
#> 10 2 Senate 5000 Pro-Administrat… 17 0.446
#> # ℹ 830 more rows
#> # ℹ 3 more variables: nominate_dim2_median <dbl>, nominate_dim1_mean <dbl>,
#> # nominate_dim2_mean <dbl>The function get_les() retrieves Legislative Effectiveness Scores Data
from the Center for Effective Lawmaking.
get_les() takes the following arguments:
chamber: Which chamber to get data for. See the Voteview section above for more info on this argument.
Note: Unlike the Voteview functions, there is no “all” option for
chamber. You must specify either House or Senate data, since there
is no “default” option.
There are non-trivial differences between the House and Senate datasets, so take care when joining House and Senate data.
-
les_2: Whether to use LES 2.0 (instead of Classic Legislative Effectiveness Scores). LES 2.0 credits lawmakers when language from their sponsored bills is included in other legislators’ bills that become law. LES 2.0 is only available for the 117th Congress. Classic LES is available for the 93rd through 117th Congresses. -
local,local_dir: Same as the Voteview functions.
Here is an example table returned by get_les().
library(filibustr)
get_les(chamber = "senate", les_2 = FALSE)
#> # A tibble: 2,533 × 60
#> last first state congress cgnum icpsr year dem majority elected female
#> <chr> <chr> <fct> <int> <int> <int> <int> <lgl> <lgl> <int> <lgl>
#> 1 Abourezk James SD 93 1 13000 1972 TRUE TRUE 1972 FALSE
#> 2 Aiken Geor… VT 93 2 52 1972 FALSE FALSE 1940 FALSE
#> 3 Allen James AL 93 3 12100 1972 TRUE TRUE 1968 FALSE
#> 4 Baker Howa… TN 93 4 11200 1972 FALSE FALSE 1966 FALSE
#> 5 Bartlett Dewey OK 93 5 14100 1972 FALSE FALSE 1972 FALSE
#> 6 Bayh Birch IN 93 6 10800 1972 TRUE TRUE 1962 FALSE
#> 7 Beall J. MD 93 7 12002 1972 FALSE FALSE 1970 FALSE
#> 8 Bellmon Henry OK 93 8 12101 1972 FALSE FALSE 1968 FALSE
#> 9 Bennett Wall… UT 93 9 645 1972 FALSE FALSE 1950 FALSE
#> 10 Bentsen Lloyd TX 93 10 660 1972 TRUE TRUE 1970 FALSE
#> # ℹ 2,523 more rows
#> # ℹ 49 more variables: afam <lgl>, latino <lgl>, votepct <int>, chair <lgl>,
#> # subchr <lgl>, seniority <int>, state_leg <lgl>, state_leg_prof <dbl>,
#> # maj_leader <lgl>, min_leader <lgl>, votepct_sq <int>, lagles <dbl>,
#> # power <lgl>, freshman <lgl>, sensq <int>, deleg_size <int>,
#> # party_code <int>, bioname <chr>, bioguide_id <chr>, born <int>, died <int>,
#> # dwnom1 <dbl>, dwnom2 <dbl>, meddist <dbl>, majdist <dbl>, cbill1 <int>, …The function get_hvw_data() retrives replication data for
Harbridge-Yong, Volden, and Wiseman
(2023).
get_hvw_data() takes the following arguments:
chamber: Which chamber to get data for. See the Voteview section above for more info on this argument.
Note: Unlike the Voteview functions, there is no “all” option for
chamber. You must specify either House or Senate data, since there
is no “default” option.
The House and Senate data do not have the same number of variables, or the same variable names, so it is not trivial to join the two tables.
local,local_dir: Same as the Voteview functions.
Here are the tables returned by get_hvw_data():
library(filibustr)
get_hvw_data("house")
#> # A tibble: 9,825 × 109
#> thomas_num thomas_name icpsr congress year st_name cd dem elected
#> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 1 Abdnor, James 14000 93 1973 SD 2 0 1972
#> 2 2 Abzug, Bella 13001 93 1973 NY 20 1 1970
#> 3 3 Adams, Brock 10700 93 1973 WA 7 1 1964
#> 4 4 Addabbo, Joseph 10500 93 1973 NY 7 1 1960
#> 5 5 Albert, Carl NA 93 1973 OK 3 NA 1946
#> 6 6 Alexander, Bill 12000 93 1973 AR 1 1 1968
#> 7 7 Anderson, John 10501 93 1973 IL 16 0 1960
#> 8 8 Anderson, Glenn 12001 93 1973 CA 35 1 1968
#> 9 9 Andrews, Ike 14001 93 1973 NC 4 1 1972
#> 10 10 Andrews, Mark 10569 93 1973 ND 1 0 1963
#> # ℹ 9,815 more rows
#> # ℹ 100 more variables: female <dbl>, votepct <dbl>, dwnom1 <dbl>,
#> # deleg_size <dbl>, speaker <dbl>, subchr <dbl>, ss_bills <dbl>,
#> # ss_aic <dbl>, ss_abc <dbl>, ss_pass <dbl>, ss_law <dbl>, s_bills <dbl>,
#> # s_aic <dbl>, s_abc <dbl>, s_pass <dbl>, s_law <dbl>, c_bills <dbl>,
#> # c_aic <dbl>, c_abc <dbl>, c_pass <dbl>, c_law <dbl>, afam <dbl>,
#> # latino <dbl>, power <dbl>, budget <dbl>, chair <dbl>, state_leg <dbl>, …
get_hvw_data("senate")
#> # A tibble: 2,228 × 104
#> last first state cabc caic cbill claw cpass sabc saic sbill slaw spass
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Grav… Mike AK 0 0 17 0 0 2 0 48 0 1
#> 2 Stev… Ted AK 0 0 9 0 0 6 0 71 3 6
#> 3 Allen James AL 0 0 5 0 0 2 0 14 0 1
#> 4 Spar… John AL 1 0 23 0 1 7 0 62 0 7
#> 5 Fulb… James AR 0 0 0 0 0 9 0 31 3 8
#> 6 McCl… John AR 0 0 3 0 0 3 0 20 1 2
#> 7 Fann… Paul AZ 0 0 4 0 0 1 0 32 1 1
#> 8 Gold… Barry AZ 0 0 6 0 0 0 0 13 0 0
#> 9 Cran… Alan CA 7 0 17 1 7 5 0 64 2 4
#> 10 Tunn… John CA 0 0 1 0 0 4 0 35 0 1
#> # ℹ 2,218 more rows
#> # ℹ 91 more variables: ssabc <dbl>, ssaic <dbl>, ssbill <dbl>, sslaw <dbl>,
#> # sspass <dbl>, congress <dbl>, cgnum <dbl>, icpsr <dbl>, year <dbl>,
#> # dem <dbl>, majority <dbl>, elected <dbl>, female <dbl>, afam <dbl>,
#> # latino <dbl>, votepct <dbl>, dwnom1 <dbl>, chair <dbl>, subchr <dbl>,
#> # seniority <dbl>, state_leg <dbl>, state_leg_prof <dbl>, maj_leader <dbl>,
#> # min_leader <dbl>, allbill <dbl>, allaic <dbl>, allabc <dbl>, …The following functions retrieve data tables from Senate.gov.
get_senate_sessions(): The start and end dates of each legislative session of the Senate. (table link)get_senate_cloture_votes(): Senate action on cloture motions and cloture votes. (table link)
These functions take no arguments, and they always return the full data table from the Senate website.
This package also provides some smaller utility functions for working with congressional data.
year_of_congress()returns the starting year for a given Congress.congress_in_year()returns the Congress number for a given year.current_congress()returns the number of the current Congress, which is currently 118.current_congress()is equivalent tocongress_in_year(Sys.Date()).
This package uses data from the following websites and research:
- Harbridge-Yong, L., Volden, C., & Wiseman, A. E. (2023). The Bipartisan Path to Effective Lawmaking. The Journal of Politics, 85(3), 1048–1063. https://doi.org/10.1086/723805
- Lewis, Jeffrey B., Keith Poole, Howard Rosenthal, Adam Boche, Aaron Rudkin, and Luke Sonnet (2023). Voteview: Congressional Roll-Call Votes Database. https://voteview.com/
- U.S. Senate. https://www.senate.gov/
- Volden, C., & Wiseman, A. E. (2023). Legislative Effectiveness Scores [dataset]. Center for Effective Lawmaking. https://thelawmakers.org/