Exploring the Brazilian Federal Senate

In congressbr: Downloads, Unpacks and Tidies Legislative Data from the Brazilian Federal Senate and Chamber of Deputies

The API of the Brazilian Federal Senate can be explored with congressbr by searching for details on individual senators, commissions and votes, among other options.

congressbr can be installed by running:

if(!require(devtools)){install.packages("devtools", repos = "http://cran.us.r-project.org")}
if(!require(ggplot2)){install.packages("ggplot2", repos = "http://cran.us.r-project.org")}

install.packages("congressbr")

And then loaded with:

library(congressbr)

library(congressbr)
library(ggplot2)

Votes

The voting behaviour of legislators is an area of great interest both inside and outside of academia. congressbr has the sen_votes() function, which returns a data frame of votes in the Senate. These are not necessarily nominal votes, as some may be secret votes. In this case, the API records whether the senator voted or not. The other variables in the data frame returned pertain to the time of the vote, its number, id, year, description and its result. Information on individual senators (their party, name, id, gender and state) is also returned. This function has an argument, binary, which if TRUE, transforms the recorded (nominal) votes from "Yes" to 1 and "No" to 0. This is handy if you want to use the wnominate, pscl or MCMCpack functions to run ideal point analyses, for example. Please note that dates are in "yyyymmdd" format.

sen_votes(date = "20160908")

For convenience, we have included a dataset of all nominal votes in the Federal Senate from 1991 to early 2017, which can be accessed with data("sen_nominal_votes"). The dataset is only 38KB and can be loaded quickly on any computer.

If you want to know more about the results from the plenary in the Federal Senate for a specified date, you may also use the sen_plenary() function. As an example, you can retrieve information from the session of the 3rd of March 2016 with:

sen_plenary_result(date = "20160303")

Senator info

Information on individual senators can be had with sen_senator_details(). This function returns the senator's id, name, party and state, as well as information on his/her date of birth, place of birth, their mandates, and office information such as email and correspondence address. If the senator is a titular senator, information on his/her deputies (suplentes) is available with sen_senator_suplentes(). The senator's votes can be had with sen_senator_votes(), and their mandates with sen_senator_mandates(). For information on absences and party affiliations, use sen_senator(). All of these functions use the senator's id (with the id option). A data frame of all of these is available from the function sen_senator_list():

sen_senator_list()

Commissions and coalitions

There are certain legislative coalitions in the Senate, all of whom have unique id numbers in the API database. These numbers can be accessed with sen_coalitions().

A data frame of commissions (full name and abbreviation) can be obtained with data("commissions"). The sen_commissions() function returns a detailed dataframe of commissions, their ids, type, house and purpose. Commissions can be explored by type variable returned from this function. For example, the "cpi" type (Comissão de Inquérito Parlamentar, Parliamentary Inquiry Commission) can be used in the sen_commissions_type() function, which will return a data frame of commissions of the type specified.

Using the commission abbreviations, we can see which senators serve on the commission. One well-known commission is the Commission for the Constitution, Justice and Citizenship (Constituicao, Justica e Cidadania). Its abbreviation is "CCJ":

data("commissions")
sen_commissions_senators(code = "CCJ")

General Information

Parties

A list of the parties who have held seats in the Senate can be had with sen_parties():

sen_parties()

States

For some functions, there are options to narrow the search by focusing on certain states. If you are not familiar with all of the Brazilian states or with their commonly-used abbreviations, use the UF() function to print out a list of these abbreviations. For the full names of the states and their abbreviations, use data("statesBR").

UF()

Bill types

The Senate API can be queried by the type of bill. If you are not familiar with the types of bills that come to the Senate floor, use sen_bills_types(). Other similar functions, useful for getting to know the Senate, are sen_plenary_sessions(), which returns data on the types of sessions held in the Senate, sen_bills_topics(), which returns a data frame with the topic labels for different subject categories.

The bill types can be used for other queries, such as for seeing what bills of a certain type are currently passing through the house. For example, we can see which MPVs (medida provisória; executive decree) were under consideration in the Senate in 2001:

sen_bills_passing(year = "2001", type = "MPV")

This function can also be used to get information for a single date.

congressbr also has functions for accessing budget information (sen_budget()), and the agenda in the Senate for a particular date, or range of dates:

sen_agenda(initial_date = "20161105", end_date = "20161125")

Examples

congressbr can be used for various types of analyses. As a quick example, let's explore the distribution of men and women currently sitting in the house. (This example makes use of the ggplot2 package.)

all_sens <- sen_senator_list()

library(ggplot2)
all_sens <- sen_senator_list()

ggplot(all_sens, aes(x = gender)) +
  geom_bar(aes(fill = gender), colour = "white") +
  theme_classic() +
  scale_fill_manual(values = c("#45C74A", "#FFFF00"))

Looking at the distribution of Titular senators and deputies (suplentes) is also straightforward:

ggplot(all_sens, aes(x = status)) +
  geom_bar(aes(fill = status), colour = "black") +
  theme_classic() +
  scale_fill_manual(values = c("#45C74A", "navy")) +
  theme(legend.position = "none") +
  coord_flip() 

Which states have the most suplentes?

if(!require(dplyr)){install.packages("dplyr", repos = "http://cran.us.r-project.org")}
library(dplyr)

all_sens %>% filter(status != "Titular") %>% group_by(state) %>% 
  summarise(totals = n()) %>% arrange(desc(totals))

Mato Grosso, it seems, with two-thirds of their senators being stand-in suplentes.

congressbr documentation built on Dec. 16, 2019, 1:21 a.m.