In this section we’ll look at both the biggest spenders and most frugal parliamentarians.

We’ll examine each group in ranks of 10 parliamentarians, a commonplace in terms of ranking.

Which parliamentarians spent the most?

data %>%
  group_by(sgUF,idCadastro,nomeParlamentar) %>%     # calculate expense for
  summarize(total_expense = sum(valorLíquido)) %>%  # each parliamentarian
  ungroup() %>%                                   
  top_n(n=10, wt=total_expense) %>%  # get the top 10 expenders
  ggplot(aes(total_expense, 
             reorder(nomeParlamentar,
                     total_expense))) +
  geom_segment(aes(x = min(total_expense),
                   y =reorder(nomeParlamentar,
                              total_expense),
                   xend = total_expense,
                   yend = nomeParlamentar),
               color = "grey50", size=0.35) +
  geom_point(aes(color=sgUF), size=3.5) +
  scale_x_continuous(breaks = seq(from = 135E3,
                                  to=1E7,
                                  by = 1E4)) +
  theme(axis.text.x = element_text(angle = 30,
                                   hjust = 1)) +
  guides(color=guide_legend("State")) +
  labs(x="Total Expense (R$)", 
       y="Parliamentarian name")

• The parliamentarian Edio Lopes from the state of Roraima is the biggest spender with a total expense of \(R\$\:1,\!485,\!000.\,00\) (that’s almost 1.5 million reais).
  • Edio Lopes spent what the second place Hiran Gonçalves (also from Roraima) spent plus some \(R\$\:55,\!000.\,00\).
  • Edio Lopes spent what the tenth place Silas Câmara from the state of Amazonas spent plus some \(R\$\: 80,\!000.\,00\).
• All 10 parliamentarians spent well over 1 million reais, furthermore except for Amazonas none of the states involved are usually pointed as one of the richest Brazilian states.
  • Among the states that made to the rank Amazonas would be the most prominent and yet its parliamentarian is right at the bottom of the top 10 rank.
• Half of the big spenders are from the state of Roraima (top three included).

state_info %>%
  ggplot(aes(reorder(UF,limite_mensal),
             limite_mensal)) +          
  geom_point() +
  scale_y_continuous(breaks = seq(5E2,5E5,1.5E3)) +
  labs(y="Montly allowance", x="State")

• The state of Roraima (RR) has the biggest monthly allowance in the whole country which is approximately \(R\$\:45,\!500.\,00\). It makes sense that their parliamentarians are among the biggest spenders in the whole country.

• The federal district (DF), where’s the capital, has the smallest allowance which is a little more than \(R\$\:30,\!500.\,00\). A parliamentarian who lives there is already close to the capital so doesn’t have to travel by plane and probably already has a house there, so the smaller allowance is to be expected.

Which parliamentarians spent less?

data %>%
  group_by(sgUF,idCadastro,nomeParlamentar) %>%     # calculate expense for
  summarize(total_expense = sum(valorLíquido)) %>%  # each parliamentarian
  ungroup() %>%
  filter(total_expense >= 0) %>%  # Remove those without actual net amount 
  arrange(total_expense) %>%      # Order by total expense  
  slice(1:10) %>%                 # Get the top 10 frugal
  ggplot(aes(total_expense,
             reorder(nomeParlamentar,
                     total_expense))) +
  geom_segment(aes(x = 0,
                   y = reorder(nomeParlamentar,
                               -total_expense),
                   xend = total_expense,
                   yend = nomeParlamentar),
               color = "grey50", size=0.35) +
  geom_point(aes(color=sgUF), size=3.5) +
  scale_x_continuous(breaks = seq(from = 0,
                                  to=1E5,
                                  by = 20)) +
  guides(color=guide_legend("State")) +
  labs(x="Total Expense (R$)",
       y="Parliamentarian name")

Going against what one would expect none of the more frugal parliamentarians are from the Federal District (DF) which has the smallest monthly allowance.

• Our frugal friends live in other states so they had to travel and pay for accommodations. So, how did they pay for the aforementioned expenses? Did it come from their own pockets?
  • The parliamentarian Camilo Cola spent a astonishingly small sum, this demands a closer look.

data %>%
  group_by(sgUF,idCadastro,nomeParlamentar) %>%
  summarize(total_expense = sum(valorLíquido)) %>%
  ungroup() %>%
  filter(total_expense >= 0) %>%
  arrange(total_expense) %>%
  slice(1:3)

## # A tibble: 3 x 4
##   sgUF  idCadastro nomeParlamentar total_expense
##   <chr>      <int> <chr>                   <dbl>
## 1 ES        141395 CAMILO COLA              0.62
## 2 MS        178899 MARCIO MONTEIRO         14.2 
## 3 PR        146613 MARCELO ALMEIDA         26.2

data %>%
  select(sgUF,fornecedor,
         nomeParlamentar,
         sgPartido,tipoDespesa) %>%
  filter(nomeParlamentar %in% 
         c("CAMILO COLA",
           "MARCIO MONTEIRO",
           "MARCELO ALMEIDA"))

## # A tibble: 3 x 5
##   sgUF  fornecedor nomeParlamentar sgPartido tipoDespesa
##   <chr> <chr>      <chr>           <chr>     <chr>      
## 1 ES    RAMAL      CAMILO COLA     PMDB      TELEPHONY  
## 2 PR    RAMAL      MARCELO ALMEIDA PMDB      TELEPHONY  
## 3 MS    RAMAL      MARCIO MONTEIRO PSDB      TELEPHONY

In this section we’ll look at the parliamentary expenses paid for with the CEAP budget from the point of view of the states these parliamentarians represent.

When talking about the parliamentarians themselves we’ll keep our ranking of 10 parliamentarians. However, when examining the states we’ll adopt a ranking of 3 states. Talking about 10 states means talking about almost half the states, which would be cumbersome.

Parliamentarians from which states spent the most abroad? And the less?

data %>%
  filter(tipoDocumento == 2) %>%  # Keep only expenses abroad 
  group_by(sgUF) %>%  # group by State 
  summarize(international_expense = sum(valorLíquido)) %>% # calc expense by state
  ggplot(aes(reorder(sgUF,international_expense),
             international_expense)) +
  geom_bar(stat = "identity") +
  labs(x="States", y="Expenses abroad (R$)")

• Parliamentarians from Maranhão, Paraíba and Pará spent the less abroad.
  • The three states are less prominent (smaller gross domestic product for instance) so it makes sense their parliamentarians would spend less abroad.
  • Maranhão spendings are incredibly small, a closer look is due.
• Parliamentarians from São Paulo, Minas Gerais and Pernambuco spent the most abroad
  • São Paulo and Minas Gerais and in lesser degree Pernambuco are some of the richest states in Brazil.

data %>%
  filter(tipoDocumento == 2) %>% # keep only expenses abroad
  filter(sgUF == "MA") %>%  # keep only expenses from MA
  select(fornecedor,
         tipoDespesa,
         valorLíquido,
         nomeParlamentar,
         sgUF,
         sgPartido)

## # A tibble: 1 x 6
##   fornecedor tipoDespesa       valorLíquido nomeParlamentar sgUF  sgPartido
##   <chr>      <chr>                    <dbl> <chr>           <chr> <chr>    
## 1 7 ELEVEN   SUPPLY OF FOOD F…         41.0 JUNIOR MARRECA  MA    PEN

• The whole international expense made by all the parliamentarians from the state of Maranhão was some meal bought at a convenience store. For the looks of the chain of convenience stores it was most likely fast food.

Which parliamentarians violated the CEAP budget set by their state the most?

data %>%
  group_by(sgUF,idCadastro,year_month,          # group by parliamentarian
           nomeParlamentar,limite_mensal) %>%   # and vars of interest
  summarise(monthly_expense = sum(valorLíquido)) %>% # calc expense by month/year
  filter(year_month != "NA-NA") %>%  # remove expense without a month/year  
  filter(monthly_expense > limite_mensal) %>%  # keep only violations
  ungroup() -> expense_month_year
#
expense_month_year %>%
  select(nomeParlamentar,
         year_month,
         limite_mensal,
         monthly_expense) %>%
  sample_n(5)

## # A tibble: 5 x 4
##   nomeParlamentar year_month limite_mensal monthly_expense
##   <chr>           <chr>              <dbl>           <dbl>
## 1 CAJAR NARDES    2016-9            40876.          56009.
## 2 RÔMULO GOUVEIA  2016-1            42033.          50728.
## 3 JUTAHY JUNIOR   2016-6            39011.          60907.
## 4 WALDIR MARANHÃO 2016-12           42152.          82388.
## 5 AELTON FREITAS  2015-10           36093.          48787.

• We now have the monthly expense of the parliamentarians that violated their respective monthly allowance.

expense_month_year %>%
  group_by(idCadastro,nomeParlamentar) %>% # group only by parliamentarian 
  arrange(desc(monthly_expense)) %>%   # order by month/year expense
  slice(1) %>%                         # keep biggest expense of each parliamentarian
  ungroup() %>%
  top_n(n=10, wt=monthly_expense) %>%  # get overall top 10 month/year expenses 
  mutate(overbudget = monthly_expense - limite_mensal) %>% # calc actual overbudget
  ggplot(aes(overbudget,
             reorder(nomeParlamentar,
                     overbudget))) +
  geom_segment(aes(x = 1E5, 
                   y =reorder(nomeParlamentar,
                              overbudget),
                   xend = overbudget,
                   yend = nomeParlamentar),
               color = "grey50",
               size=0.35) +
  geom_point(aes(color=sgUF),size=3) +
  labs(x="Overbudget (R$)",
       y="parliamentarians")

• At the top of the rank we have Pastor Eurico who managed to spend over R$ 180,000 more than he should in a single month.
• All of our ranked parliamentarians for some reason had to spend way over R$ 100,000 than they should have in the period of a single month.

At which states the parliamentarians spent the most with airfare ? And the less?

We will consider both the cost of air tickets themselves and the cost of their issuance.

data %>%
  filter(tipoDespesa %in% 
         c("AIRLINE TICKETS",
           "AIR TICKET ISSUANCE")) %>% # keep only fligths
  group_by(sgUF) %>%
  summarize(international_expense = sum(valorLíquido)) %>%
  filter(sgUF != "NA") %>%
  ggplot(aes(reorder(sgUF,international_expense),
             international_expense)) +
  geom_bar(stat = "identity") +
  labs(x="States", y="Flight expense")

In this section we’ll pay closer attention to the state of Paraíba. We’ll examine which political parties are the most avid and the most restrained spenders in terms of frequency and overall cost.

We’ll put the political parties in rankings of 3, a number we consider reasonable as our examination covers a total of 12 political parties.

In which parties the parliamentarians used CEAP more frequently? In which did they use it less frequently?

data %>%
  filter(sgUF == "PB") %>% # keep only PB
  na.omit(sgUF,
          sgPartido) %>%
  group_by(sgPartido) %>% 
  summarize(frequency =n()) %>% # calc num of times
  ggplot(aes(reorder(sgPartido,frequency),
             frequency)) +
  geom_bar(stat = "identity") +
  labs(x="Parties", y="Absolute frequency")

• The parties PMDB (Brazilian Democratic Movement | centre to centre-right), PP (Progressistas | right-wing) and SD (Solidariedade | centre-left) are by far the ones that used CEAP the most times.
  • In the case of PMDB it was to be expected as it detains the majority of offices.
• The parties PSC (Social Christian Party | centre-right) and PROS (Republican Party of the Social Order | centre-right) are by far the ones that have used CEAP the least times.
  • The third party to have used CEAP the least times PT (Workers’ Party | centre-left to left-wing) still used CEAP many times more than PSC and PROS.

In which parties the parliamentarians spent the most from CEAP? In which they spent the less?

data %>%
  filter(sgUF == "PB") %>% # keep only PB
  na.omit(sgUF,
          sgPartido,
          valorLíquido) %>%
  group_by(sgPartido) %>%
  summarize(total_expense = sum(valorLíquido)) %>% # calc total expense
  ggplot(aes(reorder(sgPartido,total_expense),
             total_expense)) +
  geom_bar(stat = "identity") +
  labs(x="Parties", y="Total expense")

• PMDB (Brazilian Democratic Movement | centre to centre-right) is by far the party that spent the most in Paraíba, which was to be expected as PMDB detains the majority of offices.
  • Second place PSD (Social Democratic Party | centre) and third place and DEM (Democrats | centre-right to right) total expenses are far below the biggest spender.
• The parties PSC (Social Christian Party | centre-right) and PROS (Republican Party of the Social Order | centre-right) both spent very little compared to the rest and are clearly the parties that spent less in Paraíba.
  • The third party to spend less PTB (Brazilian Labour Party | centre-right) still has a total expense considerably bigger than that of PSC and PROS.

At this section we’ll look closely at the expenses of three of the major political parties in Brazil: PT, PSDB and PMDB. Candidates from PT and PSDB are often the competitors at the run-off election for presidency and PMDB often holds the parliamentary majority (as is the case in this dataset). These are only some of the evidences of the importance of the chosen parties in the Brazilian political scene.

What type of expense is more frequently paid for with CEAP by parliamentarians of these parties?

data %>%
  filter(sgPartido %in% c("PSDB")) %>% # keep only PSDB
  group_by(tipoDespesa) %>%
  summarize(frequency = n()) %>% # calc total times
  ggplot(aes(reorder(tipoDespesa,frequency),
             frequency)) +
  geom_bar(stat="identity",
           fill="#1E347F") +
  labs(y= "Absolute Frequency", 
       x="Type of expense") +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1))

data %>%
  filter(sgPartido %in% c("PMDB")) %>% # keep only PMDB
  group_by(tipoDespesa) %>%
  summarize(frequency = n()) %>% # calc total times
  ggplot(aes(reorder(tipoDespesa,frequency),
             frequency)) +
  geom_bar(stat="identity",
           fill="#7F6607") + 
  labs(y= "Absolute Frequency", 
       x="Type of expense") +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1))

data %>%
  filter(sgPartido %in% c("PT")) %>% # keep only PT
  group_by(tipoDespesa) %>%
  summarize(frequency = n()) %>% # calc total times
  ggplot(aes(reorder(tipoDespesa,frequency),
             frequency)) +
  geom_bar(stat="identity",
           fill="#7F1707") + 
  labs(y= "Absolute Frequency", 
       x="Type of expense") +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1))

What type of expense paid for with CEAP by parliamentarians of these parties costed more?

data %>%
  filter(sgPartido %in% c("PSDB")) %>% # keep only PSDB
  group_by(tipoDespesa) %>%
  summarize(expense = sum(valorLíquido)) %>% # calc total expense
  ggplot(aes(reorder(tipoDespesa,expense),
             expense)) +
  geom_bar(stat="identity",
           fill="#1E347F") +
  labs(y= "Total expense", 
       x="Type of expense") +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1))

data %>%
  filter(sgPartido %in% c("PMDB")) %>% # keep only PMDB
  group_by(tipoDespesa) %>%
  summarize(expense = sum(valorLíquido)) %>% # calc total expense
  ggplot(aes(reorder(tipoDespesa,expense),
             expense)) +
  geom_bar(stat="identity",
           fill="#7F6607") +
  labs(y= "Total expense", 
       x="Type of expense") +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90, 
                                   hjust = 1))

data %>%
  filter(sgPartido %in% c("PT")) %>% # keep only PT
  group_by(tipoDespesa) %>%
  summarize(expense = sum(valorLíquido)) %>% # calc total expense
  ggplot(aes(reorder(tipoDespesa,expense),
             expense)) +
  geom_bar(stat="identity",
           fill="#7F1707") +
  labs(y= "Total expense", 
       x="Type of expense") +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1))