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:
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 votesThese functions share a common interface and arguments.
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/DTA file so you can read that local file using
local_path instead of having to download data from
online.
For demonstration, here is the table returned by
get_voteview_parties().
library(filibustr)
get_voteview_parties()
#> # A tibble: 845 × 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 835 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.
There are non-trivial differences between the House and Senate datasets, so take care when joining House and Senate data.
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).
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.
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
#> <int> <chr> <int> <int> <int> <fct> <int> <lgl> <int>
#> 1 1 Abdnor, James 14000 93 1973 SD 2 FALSE 1972
#> 2 2 Abzug, Bella 13001 93 1973 NY 20 TRUE 1970
#> 3 3 Adams, Brock 10700 93 1973 WA 7 TRUE 1964
#> 4 4 Addabbo, Joseph 10500 93 1973 NY 7 TRUE 1960
#> 5 5 Albert, Carl NA 93 1973 OK 3 NA 1946
#> 6 6 Alexander, Bill 12000 93 1973 AR 1 TRUE 1968
#> 7 7 Anderson, John 10501 93 1973 IL 16 FALSE 1960
#> 8 8 Anderson, Glenn 12001 93 1973 CA 35 TRUE 1968
#> 9 9 Andrews, Ike 14001 93 1973 NC 4 TRUE 1972
#> 10 10 Andrews, Mark 10569 93 1973 ND 1 FALSE 1963
#> # ℹ 9,815 more rows
#> # ℹ 100 more variables: female <lgl>, votepct <dbl>, dwnom1 <dbl>,
#> # deleg_size <int>, speaker <lgl>, subchr <lgl>, ss_bills <int>,
#> # ss_aic <int>, ss_abc <int>, ss_pass <int>, ss_law <int>, s_bills <int>,
#> # s_aic <int>, s_abc <int>, s_pass <int>, s_law <int>, c_bills <int>,
#> # c_aic <int>, c_abc <int>, c_pass <int>, c_law <int>, afam <lgl>,
#> # latino <lgl>, power <lgl>, budget <lgl>, chair <lgl>, state_leg <lgl>, …
get_hvw_data("senate")
#> # A tibble: 2,228 × 104
#> last first state cabc caic cbill claw cpass sabc saic sbill slaw spass
#> <chr> <chr> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 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 <int>, ssaic <int>, ssbill <int>, sslaw <int>,
#> # sspass <int>, congress <int>, cgnum <int>, icpsr <int>, year <int>,
#> # dem <lgl>, majority <lgl>, elected <int>, female <lgl>, afam <lgl>,
#> # latino <lgl>, votepct <dbl>, dwnom1 <dbl>, chair <lgl>, subchr <lgl>,
#> # seniority <int>, state_leg <lgl>, state_leg_prof <dbl>, maj_leader <lgl>,
#> # min_leader <lgl>, allbill <int>, allaic <int>, allabc <int>, …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 actions 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 119. current_congress() is
equivalent to congress_in_year(Sys.Date()).get_voteview_cast_codes() returns a key to the
cast_code column in
get_voteview_member_votes().read_html_table() is a general-use function for reading
HTML tables from online. It’s a nice shortcut for a common
rvest workflow that otherwise takes 3 functions. (It’s what
powers the Senate.gov functions!)If you notice any bugs, or have suggestions for new features, please submit an issue on the Issues page of this package’s GitHub repository!
This package uses data from the following websites and research: