bolsonaro.r

# Libraries ---------------------------------------------------------------

library(congressbr)
library(tidyverse)
library(lubridate)


# Bills -------------------------------------------------------------------

bills2<-c()
for(i in (1991:2018)){
  bills <- cham_plenary_bills(year=i)
  bills2 <- rbind(bills2, bills)
}


bills2 %>% 
  mutate(date = dmy(vote_date),
         year = year(date),
         month = month(date),
         day = day(date)) %>% 
  separate(bill_name, c("type", "name", "extra"), sep=" ", extra="merge") %>% 
  separate(name, c("id", "year1"), sep="/") %>% 
  group_by(type, id, year1) %>% 
  summarise(n=n()) -> bills3

## vote
vote <- list()
args <- list(type=bills3$type,
               number=as.numeric(bills3$id),
               year=bills3$year1)



p_cham_votes <- possibly(cham_votes, NULL)

vote <- pmap(args, function(type, number, year) p_cham_votes(type=type, number=number, year=year))


# Vote2 -------------------------------------------------------------------

fhc1 <- interval(ymd("1995-01-01"), ymd("1998-12-31"))
fhc2 <- interval(ymd("1999-01-01"), ymd("2002-12-31"))
lula1 <- interval(ymd("2003-01-01"), ymd("2006-12-31"))
lula2 <- interval(ymd("2007-01-01"), ymd("2010-12-31"))
dilma1 <- interval(ymd("2011-01-01"), ymd("2014-12-31"))
dilma2 <- interval(ymd("2015-01-01"), ymd("2016-05-11"))
temer <- interval(ymd("2016-01-01"), ymd("2018-12-31"))

vote2 <- vote[!map_lgl(vote, is.null)] %>% 
  map(~ .x %>% 
         dplyr::select(rollcall_id, legislator_id, legislator_name, legislator_party, legislator_state, legislator_vote,
         decision_date, ends_with("orientation")) %>% 
        mutate(data = dmy(decision_date),
               governo = case_when(data %within% fhc1 ~ "fhc1",
                                   data %within% fhc2 ~ "fhc2",
                                   data %within% lula1 ~ "lula1",
                                   data %within% lula2 ~ "lula2",
                                   data %within% dilma1 ~ "dilma1",
                                   data %within% dilma2 ~ "dilma2",
                                   data %within% temer ~ "temer",
                                   year(data) >= 2019 ~ "bolsonaro",
                                   TRUE ~ "others"),
               gov_year = paste(year(data), governo, sep="-"),
               bolsonaro = ifelse(legislator_id=="74847", 1, 0),
               legislator_party = as.character(toupper(legislator_party)),
               party = case_when(legislator_party == "PTDOB" ~ "AVANTE",
                                 legislator_party == "PSDC" ~ "DC",
                                 legislator_party == "PMDB" ~ "MDB",
                                 legislator_party == "PEN" ~ "PATRIOTA",
                                 legislator_party == "PFL" ~ "DEM",
                                 legislator_party == "PODEMOS" ~ "PODE",
                                 legislator_party == "PMR" ~ "PRB",
                                 legislator_party == "SOLIDARIED" ~ "SDD",
                                 legislator_party %in% c('PDS', 'PPB', 'PPR') ~ "PP",
                                 TRUE ~ as.character(legislator_party))) %>% 
        filter(!(str_detect(rollcall_id, "PLN")))) 

write_rds(vote2, 'vote2.rds')

# Teste  ------------------------------------------------------------------

vote_t <- vote2 %>% 
  map(~ .x %>%  select(gov_year)) %>% 
  bind_rows() %>% 
  distinct(gov_year)


# Governo -----------------------------------------------------------------

idx <- as.logical(map_dbl(vote2, function(x) sum(grepl("GOV_orientation", colnames(x)))))

vote3 <- vote2[idx] %>% 
  map( ~ .x %>%
  dplyr::select(rollcall_id, bolsonaro, gov_year, legislator_id, party, legislator_vote, GOV_orientation)) %>% 
  bind_rows() %>% 
  group_by(rollcall_id, bolsonaro, gov_year, legislator_id, party, legislator_vote, GOV_orientation) %>% 
  summarise(n=n())


vote31 <- vote2 %>% 
  map( ~ .x %>%
         dplyr::select(rollcall_id, bolsonaro, gov_year, legislator_id, party, legislator_vote)) %>% 
  bind_rows() %>% 
  group_by(rollcall_id, bolsonaro, gov_year, legislator_id, party, legislator_vote) %>% 
  summarise(n=n())



# Governismo --------------------------------------------------------------

vote3 %>% 
  filter(GOV_orientation != "Liberado") %>% 
  filter(legislator_vote %in% c("Sim", "Nao", "Obstrucao")) %>% 
  group_by(rollcall_id, gov_year, bolsonaro, party, legislator_vote, GOV_orientation) %>% 
  summarise(n=sum(n, na.rm=T)) -> vote4


vote4 %>% 
  tbl_df() %>% 
  mutate(party = ifelse(bolsonaro==1, "BOLSONARO", party)) %>% 
  mutate(vote = ifelse(legislator_vote=="Obstrucao", "Nao", legislator_vote)) %>% 
  mutate(gov = ifelse(legislator_vote==GOV_orientation, 1, 0)) %>% 
  group_by(gov_year, bolsonaro, party) %>% 
  summarise(mean=weighted.mean(gov, w=n, na.rm=T), N=sum(n, na.rm=T)) -> vote5


governos <- unique(vote5$gov_year)

for (i in seq_along(governos)) {
vote5 %>%
  filter((party!="S.PART." & N > 20 & gov_year==governos[i]) | (party == "BOLSONARO" & gov_year==governos[i])) %>% 
  ggplot() +
  geom_dotplot(aes(x=mean, fill=party), binpositions="all", stackgroups=TRUE,
               binwidth = 0.025, dotsize = 1, stackdir = "center", alpha=0.5) +
  scale_x_continuous(limits=c(0,1)) -> p
  
ext.functions::extplot(governos[i], 100+i, 568, plot=p, display=F)
print(i)
}

write.csv(vote5, file='gov_year.csv', row.names=F)


# Deputados ---------------------------------------------------------------

vote31 %>% 
  filter(bolsonaro==1) %>%
  tbl_df() %>% 
  select(rollcall_id, legislator_vote) %>% 
  rename(bolso_vote = legislator_vote) -> bolsovote
  
vote31 %>% 
  filter(legislator_vote %in% c("Sim", "Nao", "Obstrucao", "Abstencao")) %>% 
  left_join(bolsovote, by=c('rollcall_id')) %>% 
  mutate(igual = as.integer(legislator_vote == bolso_vote)) -> vote6



vote2 %>%
  bind_rows() %>% 
  group_by(legislator_id, legislator_name) %>% 
  summarise(n=n()) %>% 
  group_by(legislator_id) %>% 
  mutate(rank = rank(-n, ties.method = 'first')) %>% 
  filter(rank==1) %>% 
  tbl_df() %>% 
  select(legislator_id, legislator_name) %>% 
  filter(legislator_id != "") -> vote313

vote6 %>% 
  group_by(legislator_id) %>% 
  summarise(proximidade = mean(igual, na.rm=T), n=n()) %>% 
  left_join(vote313, by=c('legislator_id')) -> vote7

##-- partido do legislador

vote2 %>%
  bind_rows() %>% 
  group_by(legislator_id, legislator_name, data, party) %>% 
  summarise(n=n()) %>% 
  group_by(legislator_id) %>% 
  mutate(rank = rank(-as.numeric(data), ties.method = 'first')) %>% 
  filter(rank==1) %>% 
  tbl_df() %>% 
  select(legislator_id, party) %>% 
  filter(legislator_id != "") -> vote314

##-- grafico
vote7 %>% 
  left_join(vote314, by=c('legislator_id')) %>% 
  filter(n>200) %>%
  ggplot() +
  geom_dotplot(aes(x=proximidade), binpositions="all", stackgroups=TRUE, 
               binwidth = 0.01, dotsize = 0.3, stackdir = "center", alpha=0.5) +
  scale_x_continuous(limits=c(0,1)) -> p


ext.functions::extplot('parlamentares 1', 200, 568, plot=p, display=F)

vote7 %>% 
  left_join(vote314, by=c('legislator_id')) %>% 
  filter(n>200) %>%
  ggplot() +
  geom_dotplot(aes(y=proximidade, x=party), binpositions="all", stackgroups=TRUE, binaxis ="y",
               binwidth = 0.01, dotsize = 0.3, stackdir = "center", alpha=0.5) +
  coord_flip() +
  facet_wrap(~party, scales="free_y") +
  scale_y_continuous(limits=c(0,1)) -> p


ext.functions::extplot('parlamentares 2', 201, 568, plot=p, display=F)

write.csv(vote7, file='parlamentares.csv', row.names=F)


# Adaptar -----------------------------------------------------------------

vote6 %>% 
  group_by(party) %>%
  summarise(mean = mean(igual, na.rm=T)) %>% 
  mutate(rank = 100+(rank(-mean))) -> vote400

vote400 %>% 
  ggplot(aes(x=reorder(party, mean), y=mean)) +
  geom_point() +
  coord_flip() +
  scale_y_continuous(limits=c(0,1))
  
ext.functions::extplot('partidos pela', 301, 568, display=F)

vote7 %>% 
  left_join(vote314, by=c('legislator_id')) %>% 
  filter(n>200) %>%
  left_join(vote400, by=c('party')) %>% 
  ggplot() +
  geom_dotplot(aes(y=proximidade, x=reorder(paste0(rank,"-", party), rank)), binpositions="all", stackgroups=TRUE, binaxis ="y",
               binwidth = 0.01, dotsize = 0.3, stackdir = "center", alpha=0.5) +
  coord_flip() +
  facet_wrap(~paste0(rank,"-", party), scales="free_y") +
  scale_y_continuous(limits=c(0,1)) -> p

ext.functions::extplot('parlamentares pela media', 302, 568, plot=p, display=F)





# Em relação a certos partidos --------------------------------------------


vote31 %>% 
  filter(legislator_vote %in% c("Sim", "Nao", "Obstrucao", "Abstencao")) %>% 
  left_join(bolsovote, by=c('rollcall_id')) %>% 
  mutate(igual = as.integer(legislator_vote == bolso_vote)) -> vote6


vote6 %>% 
  group_by(party) %>% 
  summarise(proximidade = mean(igual, na.rm=T), n=n()) -> vote7


vote7 %>% 
  filter(n>200) %>%
  ggplot() +
  geom_dotplot(aes(x=proximidade, fill=party), binpositions="all", stackgroups=TRUE,
               binwidth = 0.02, dotsize = 1, stackdir = "center", alpha=0.5) +
  scale_x_continuous(limits=c(0,1)) -> p

p

ext.functions::extplot('partidos 1998-2018', 300, 568, plot=p, display=F)

write.csv(vote7, file='partidos.csv', row.names=F)