knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" )
To get the current development version from Github:
## install devtools package if it's not already if (!requireNamespace("devtools", quietly = TRUE)) { install.packages("devtools") } ## install dev version of CamaraBR from github devtools::install_github("danielmarcelino/CamaraBR") ## load CamaraBR package library(CamaraBR) if(packageVersion("CamaraBR") > "0.0.7") { print("Package: OK!\n") }
library(tidyverse) library(CamaraBR) library(progress); anos = 1988:2021 barra <- progress_bar$new(total = length(anos), format = "[:bar] :percent eta: :eta") proposals <- purrr::map_df(anos, ~{ barra$tick() loadCamaraProposals(.x) }) proposals %>% count(type_bill) # fwrite(proposals, file = "data/proposals.txt") # save(proposals, file = "data/proposals.rda")
library(CamaraBR) library(progress) anos = 2019:2021 barra <- progress_bar$new(total = length(anos), format = "[:bar] :percent eta: :eta") rollcall <- purrr::map_df(anos, ~{ barra$tick() loadVotacoesCamara(.x) }) rollcall %>% count(legislator_party)
library(CamaraBR) anos = 2019:2021 orientation <- purrr::map_df(anos, ~{ loadVotacoesOrientacoesCamara(.x) }) orientation %>% count(sigla_orgao) orientation %>% count(sigla_bancada)
# legacy: data <- data %>% filter(type_bill == "MPV", number_bill == 897, year_bill == 2019 ) # data <- buildRollcallDataset(year = 1999) library(CamaraBR); library(dplyr) data <- buildRollcallDataset(year = 2021) # Votações válidas no ano para o nosso cálculo data %>% # filter(!is.na(Governo)) %>% distinct(rollcall_id) %>% nrow() data %>% dplyr::filter(legislator_vote %in% c("Nao", "Obstrução","Sim")) %>% dplyr::filter(!is.na(legislator_vote)) %>% count(Governo)
#vsiglaTipo = c("PEC", "PL", "PLP") base <- transformVotes(data, filter = FALSE) # considers every plenary rollcall base2 <- transformVotes(data, filter = TRUE) # only considers rollcall with gov orientation base %>% # filter(!is.na(Governo)) %>% distinct(rollcall_id) %>% nrow()
# base %>% group_by(legislator_party) %>% summarise(n=n()) %>% data.frame() ## Governismo geral base %>% group_by(rollcall_id) %>% summarise(governismo = mean(governismo, na.rm=T)) %>% summarise(governismo = mean(governismo, na.rm=T))
# Por partido governismo_partido <- base %>% group_by(rollcall_id, legislator_party) %>% summarise(soma = sum(governismo, na.rm=TRUE), nao = sum(abs(-1 + governismo)), governismo = mean(governismo, na.rm=TRUE), freq = n()) %>% # filter(legislator_party %in% c("PT", "PSDB", "PMDB","MDB", "PSL", "PP","PR","PSD", "DEM")) %>% group_by(legislator_party) %>% summarise(governismo = mean(governismo, na.rm=TRUE), soma = sum(soma, na.rm=TRUE), nao = sum(nao)) governismo_partido %>% arrange(governismo) %>% data.frame() # Média de governismo entre os partidos governismo_partido %>% summarize(mu = mean(governismo, na.rm=T), sd = sd(governismo, na.rm=T))
library(SciencesPo) library(ggdecor) camara <- data.frame( parties = c("PSL", "PT", "MDB", "PTB", "PP", "PSB", "DEM", "PSDB", "Outros"), seats = c(80, 61, 60, 35, 46, 28, 67, 50, 86), stringsAsFactors = FALSE) ggplot(camara) + geom_chamber(aes(seats = seats, fill = parties), color = "black") + scale_fill_classic() + coord_fixed() + theme_void()
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