Περιγραφική Ανάλυση Ελληνικού Κοινοβουλίου

Μία βασική ανάλυση των διατελέσαντων βουλευτών της περιόδου (1981 - 2019)

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Συγγραφέας

stesiam

Δημοσιευμένο

10 Οκτωβρίου 2022

Εισαγωγή

And here we go…

This is the first notebook on my website and I’d like to be a little special. I didn’t want to just take a ready-made dataset and apply a machine learning technique (I will do this in the next articles). So, I decided to make my own with the help of hellenic’s parliament website.

The Greek political scene has particularly preoccupied global public opinion in recent years, due to the Greek economic crisis. A part of it was spent on the reasons that caused it. The causes of the Greek crisis are many and a point of contention to this day. In this article we will deal essentially with one of the points of criticism. The election of the same persons.

With this notebook I will try to analyze whether this claim is valid by counting how many times someone has been elected to the Greek parliament. In addition, I will study the obsession with the same persons at the party level, but also at the local level (constituencies).

Prerequisites

Import Libraries

First and foremost, we have to load our libraries.

Show the code
# General purpose R libraries
library(tidyverse)
library(kableExtra)
library(reactable)
library(highcharter)

# Graphs
library(ggplot2)
library(ggpol) 
library(ggtext)

# Other settings
options(digits=2) # print only 2 decimals

Import dataset

After loading R libraries, then I will load my data.

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parliament <- read_csv("data/greek_parliament.csv")

Preview Dataset

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preview_dataset = head(parliament, 10)
kbl(preview_dataset, 
    align = 'c',
    booktabs = T,
    centering = T,
    valign = T) %>%
  kable_paper() %>%
  scroll_box(width = "600px", height = "250px")
Πίνακας 1: Preview Dataset (first 6 rows)
FullName Party Constituency Term
Agorastis Vasileios PA.SO.K. (Panhellenic Socialist Movement) Larissa 3
Akrita Sylva - Kaiti PA.SO.K. (Panhellenic Socialist Movement) Athens B 3
Akritidis Nikolaos PA.SO.K. (Panhellenic Socialist Movement) Thessaloniki A 3
Akrivakis Alexandros PA.SO.K. (Panhellenic Socialist Movement) Viotia 3
Alevras Ioannis PA.SO.K. (Panhellenic Socialist Movement) Athens A 3
Alexandris Efstathios (Stathis) PA.SO.K. (Panhellenic Socialist Movement) Athens B 3
Alexiadis Konstantinos PA.SO.K. (Panhellenic Socialist Movement) Trikala 3
Alexiou Thomas NEA DIMOKRATIA Xanthi 3
Allamanis Stylianos NEA DIMOKRATIA Karditsa 3
Amanatidis Konstantinos PA.SO.K. (Panhellenic Socialist Movement) Thessaloniki B 3

Structure of Dataset

Variable Property Description
Full Name qualitative
(nominal)		 Surname and name of the member of parliament
Party qualitative
(nominal)		 The party on which the MP got elected
Constituency qualitative
(nominal)		 MP got elected on this area
Term qualitative
(ordinal)		 Plenum term

Thus, my sample has 4 variables, of which 0 are quantitative and 4 are quantitative properties, of which 3 are nominal and the rest one (Term) is ordinal.

Recoding variables

Party names can vary from short to lengthy ones. The last ones are a problem for our analysis because their names can not fit to our visualisations. The table below is showing all the parties that have ever participated in parliament. Is is apparent that some parties have really long names.

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data.frame(
  Party = unique(parliament$Party),
  Length = str_length(unique(parliament$Party))
) %>%
  arrange(-Length) %>%
  reactable(
    defaultPageSize = 5
  )

In our case the parties with the longest name is AN.EL. and Democratic Coalition with 81 and 70 characters, respectively. On the contrary, the shortest party name is POL.A. with 6 characters.

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## Recoding parliament$Party
parliament$Party[parliament$Party == "ANEXARTITOI DIMOKRATIKOI VOULEFTES"] <- "ADP"
parliament$Party[parliament$Party == "ANEXARTITOI ELLINES (Independent Hellenes)"] <- "ANEL"
parliament$Party[parliament$Party == "ANEXARTITOI ELLINES (Independent Hellenes) National Patriotic Democratic Alliance"]<- "ANEL"
parliament$Party[parliament$Party == "Coalition of the Left and Progress"] <- "SYRIZA"
parliament$Party[parliament$Party == "Communist Party of Greece (Interior)"] <- "KKE (interior)"
parliament$Party[parliament$Party == "DEMOCRATIC COALITION (Panhellenic Socialist Movement Democratic Left )"] <- "PASOK"
parliament$Party[parliament$Party == "DHM.AR (Democratic Left)"] <- "DHMAR"
parliament$Party[parliament$Party == "DI.ANA."] <- "DIANA"
parliament$Party[parliament$Party == "DI.K.KI."] <- "DIKKI"
parliament$Party[parliament$Party == "INDEPENDENT"] <- "INDEPENDENT"
parliament$Party[parliament$Party == "KOMMOUNISTIKO KOMMA ELLADAS"] <- "KKE"
parliament$Party[parliament$Party == "LA.O.S."] <- "LAOS"
parliament$Party[parliament$Party == "LAIKI ENOTITA"] <- "LAE"
parliament$Party[parliament$Party == "LAIKOS SYNDESMOS - CHRYSI AVGI (People’s Association – Golden Dawn)"] <- "XA"
parliament$Party[parliament$Party == "NEA DIMOKRATIA"] <- "ND"
parliament$Party[parliament$Party == "PA.SO.K. (Panhellenic Socialist Movement)"] <- "PASOK"
parliament$Party[parliament$Party == "POL.A."] <- "POLA"
parliament$Party[parliament$Party == "SYNASPISMOS RIZOSPASTIKIS ARISTERAS"] <- "SYRIZA"
parliament$Party[parliament$Party == "TO POTAMI (The River)"] <- "POTAMI"
parliament$Party[parliament$Party == "ΟΟ.ΕΟ."] <- "EO"

Setting colors

A few days after, I decided that there should be a consistency in the choice of colors. That’s the reason of this section. Thus, I will assign a dedicated hex color code to each party.

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parties = data.frame(
  Party = unique(parliament$Party)
) |>
  mutate(Color = case_when(
    Party == "PASOK" ~ "#95bb72",
    Party == "ND" ~ "#0492c2",
    Party == "KKE" ~ "#FF6666",
    Party == "SYRIZA" ~ "#e27bb1",
    Party == "KKE (interior)" ~ "#FF3366",
    Party == "INDEPENDENT" ~ "#ffffff",
    Party == "DIANA" ~ "orange",
    TRUE ~ "#808080"
  ))

kke_color = "#FF6666"
nd_color = "#0492c2"
pasok_color = "#95bb72"
syriza_color = "#e27bb1"
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names = c("Panhellenic Socialistic Mpvement", "New Democracy", "Communist Party of Greece", "Communist Party οf Greece (interior)",
          "Independent", "Coalition of the Radical Left", "Democratic Renewal", "Alternative Ecologists", "Political Spring",
          "Democratic Social Movement", "Popular Orthodox Rally", "Democratic Left", "Independent Greeks", "Golden Dawn", "Independent Democratic MPs",
          "Popular Unity", "The River")

parties$names = names

parties$Party_el= c("ΠΑΣΟΚ", "ΝΔ", "ΚΚΕ", "ΚΚΕ (εσωτερικού)", "Ανεξάρτητοι", "ΣΥΡΙΖΑ", "ΔΗΑΝΑ", "ΕΟ", "ΠΟΛΑΝ", "ΔΗΚΚΙ",
             "ΛΑΟΣ", "ΔΗΜΑΡ", "ΑΝΕΛ", "ΧΑ", "ΑΔΒ", "ΛΑΕ", "ΠΟΤΑΜΙ")

parties$names_el = c("Πανελλήνιο Σοσιαλιστικό Κίνημα", "Νέα Δημοκρατία", "Κομμουνιστικό Κόμμα Ελλάδας", 
                    "ΚΚΕ (εσωτερικού)","Ανεξάρτητοι", "Συνασπισμός Ριζοσπαστικής Αριστεράς", "Δημοκρατική Ανανέωση", 
                    "Εναλλακτικοί Οικολόγοι", "Πολιτική Άνοιξη",
            "Δημοκρατικό Κοινωννικό Κίνημα", "Λαϊκός Ορθόδοξος Συναγερμός", "Δημοκρατική Αριστερά", "Ανεξάρτητοι Έλληνες",
            "Χρυσή Αυγή", "Ανεξ Δημ. Βουλευτές", "Λαϊκή Ενότητα", "Το Ποτάμι")

Parliament over the years

Now, I will make a short sum-up of the electoral resuts over the years. It is important to share that I made it thanks to this post and ggpol package. As I am planning to do this procedure for many electoral terms, I will convert it as a function.

Arguments like term and custom_title are vital to create reproducible plots for all the terms. Although a main problem is the colors. On the aforementioned post it was about only one term. We knew how many parties we had, so we knew what colors to set and how many. If we try that on many terms (on which the number of parties can vary from 3 to 8) an error will come up which will say “Hey, you have set 3 colors but I see that you have 8 values (parties)”. This was a real obstacle for me. After some tries and misses I came up with the concept of dynamic arguments.

Show the code
make_parliament_plot <- function(term, custom_title, ...){

custom = parliament %>% filter(Term == term) %>% select(Party) %>% table() %>% t() %>% as.data.frame() %>%  `colnames<-`(c("","Party", "Seats"))

colors<-c(...)

custom$legend <- paste0(custom$Party," (", custom$Seats,")")

#draw a parliament diagram 
p<-ggplot(custom) + 
  geom_parliament(aes(seats =Seats, fill =  Party), color = "white") + 
  scale_fill_manual(values = colors , labels = custom$legend) +
  coord_fixed() + 
  theme_void()+
  labs(title  = custom_title,
       caption = "Source: stesiam | stesiam.github.io, 2022")+
  theme(title = element_text(size = 18),
        plot.title = element_text(hjust = 0.5,size = 14,face = 'bold'),
        plot.subtitle = element_text(hjust = 0.5),
        plot.caption = element_text(vjust = -3,hjust = 0.9, size = 8),    
        legend.position = 'bottom',
        legend.direction = "horizontal",
        legend.spacing.y = unit(0.1,"cm"),
        legend.spacing.x = unit(0.1,"cm"),
        legend.key.size = unit(0.8, 'lines'),
        legend.text = element_text(margin = margin(r = 1, unit = 'cm')),
        legend.text.align = 0)+
        guides(fill=guide_legend(nrow=3,byrow=TRUE,reverse = TRUE,title=NULL))

return(p)
}
Σημείωση

Note that till this moment I have not figure out how to change the position of the parties in an efficient way (in a function) that will somehow represent their potitical view. The next visualizations of the parliament are exclusively about how many MPs got elected by each party.

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plot_parliament_term <- function(term_num, parliament_data, parties_data, lang = "el") {
  # Filter the parliament data for the selected term and summarize by party
  term_data <- parliament_data |>
    dplyr::filter(Term == term_num) |>
    group_by(Party) |>
    summarise(n = n()) |>
    ungroup() 

  # Perform fuzzy join with parties data based on party name similarity
  term_data_joined <- fuzzyjoin::stringdist_inner_join(term_data, parties_data, by = "Party",
                                                      max_dist = 2, distance_col = "distance") |>
    group_by(Party.x) |>
    slice_min(distance) |>   # Ensure only the closest match is selected
    rename(Party = Party.x)

  # Conditionally adjust the data based on language
  if (lang == "en") {
    term_data_joined <- select(term_data_joined, names, Party, n, Color)
    title_text = "Distribution of seats"
    subtitle_text = "Final formation of Hellenic Parliament"
    caption_text = '<b>Source:</b> Hellenic Parliament | <b>Graphic:</b> <a href="https://stesiam.com/" target="_blank">stesiam.com</a>'
    hover_text = "Representatives"
  } else {
    term_data_joined <- select(term_data_joined, names_el, Party_el, n, Color)
    title_text = "Κατανομή θέσεων"
    subtitle_text = "Τελική μορφή Ελληνικού Κοινοβουλίου"
    caption_text = '<b>Πηγή:</b> Ελληνικό Κοινοβούλιο | <b>Γράφημα:</b> <a href="https://stesiam.com/" target="_blank">stesiam.com</a> '
    hover_text = "Βουλευτές"
  }

  # Create the highcharter plot for the specific term
  highchart() %>%
    hc_chart(type = 'item') %>%
    hc_title(text = title_text) %>%
    hc_subtitle(text = subtitle_text) %>%
    hc_caption(
      text = caption_text,
      align = 'center',
      verticalAlign = 'bottom',
      y = 10
    ) %>%
    hc_legend(labelFormat = '{name} <span style="opacity: 0.4">{y}</span>') %>%
    hc_add_series(
      name = hover_text,
      data = purrr::pmap(
        if (lang == "en") {
          list(term_data_joined$names, term_data_joined$n, term_data_joined$Color, term_data_joined$Party)
        } else {
          list(term_data_joined$names_el, term_data_joined$n, term_data_joined$Color, term_data_joined$Party_el)
        },
        list
      ),
      keys = c("name", "y", "color", "label"),
      dataLabels = list(
        enabled = TRUE,
        format = "{point.label}",
        style = list(textOutline = "5px contrast")
      ),
      center = list("50%", "88%"),
      size = "170%",
      startAngle = -100,
      endAngle = 100
    ) %>%
    hc_responsive(
      rules = list(
        list(
          condition = list(maxWidth = 600),
          chartOptions = list(
            series = list(
              list(dataLabels = list(distance = -30))
            )
          )
        )
      )
    )
}
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plot_parliament_term(3,parliament, parties, lang = "el")
Adding missing grouping variables: `Party`
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plot_parliament_term(4,parliament, parties, lang = "el")
Adding missing grouping variables: `Party`
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plot_parliament_term(5,parliament, parties)
Adding missing grouping variables: `Party`
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plot_parliament_term(6,parliament, parties)
Adding missing grouping variables: `Party`
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plot_parliament_term(7,parliament, parties)
Adding missing grouping variables: `Party`
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plot_parliament_term(8,parliament, parties)
Adding missing grouping variables: `Party`
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plot_parliament_term(9,parliament, parties, lang = "el")
Adding missing grouping variables: `Party`
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plot_parliament_term(10,parliament, parties)
Adding missing grouping variables: `Party`
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plot_parliament_term(11,parliament, parties)
Adding missing grouping variables: `Party`
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plot_parliament_term(12,parliament, parties)
Adding missing grouping variables: `Party`
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plot_parliament_term(13,parliament, parties)
Adding missing grouping variables: `Party`
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plot_parliament_term(14,parliament, parties)
Adding missing grouping variables: `Party`
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plot_parliament_term(15,parliament, parties)
Adding missing grouping variables: `Party`
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plot_parliament_term(16,parliament, parties)
Adding missing grouping variables: `Party`
Προειδοποίηση

As you can see on the next plot there are many MPs that are classified as independent (according to Hellenic’s Parliament website). Many of them are members of parties “ANEL” and “Enosi Kentroon”. However, a large wave of departures from the parties led to not fulfilling the conditions to be considered as parliamentary parties. For that reason members of these parties (that did not left) are considered independent.

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plot_parliament_term(17,parliament, parties)
Adding missing grouping variables: `Party`

Most elected members of parliament

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#total_times_elected_freqs <- function(input_constituency, min_times_elected){
total_times_elected_freqs_df = parliament %>%  count(FullName, Party) %>% filter(n >= 9) %>% as.data.frame()
  
#df = table(parliament$FullName)%>% sort(decreasing = T) %>% as.data.frame() %>% filter(Freq>=11)

ggplot(data = total_times_elected_freqs_df, aes(x = reorder(FullName, n), y = n, fill = Party ))+
  geom_bar(stat = "identity",width = 0.88) +
  geom_text(aes(label=n), hjust = 1.5, vjust=0.5, color="white", size=4)+
  theme_minimal() +
  scale_fill_manual(values = c("KKE" = kke_color,"ND" = nd_color, "PASOK" = pasok_color, "SYRIZA" = syriza_color)) +
  labs(title = "Most elected MPs on Greek Parliament (1981 - 2019) <br>
       <span style = 'font-size:10pt'> A list that shows the most elected members of parliament (elected 11 times or more). <br> The following ones are members of <span style = 'color:blue;'>ND</span>,  or <span style = 'color:darkgreen;'>PASOK</span></span>.",
       caption = "Source: **stesiam** | stesiam.github.io, 2022",
       x = "Times elected",
       y = "Members of Parliament") +
    theme(
    plot.title.position = "plot",
    plot.title = element_textbox_simple(
      size = 14,
      lineheight = 1,
      padding = margin(5.5, 5.5, 5.5, 5.5),
      margin = margin(0, 0, 5.5, 0),
      fill = "cornsilk"
    ))+
  coord_flip()

Most elected MPs

Most elected members by party

So far we have seen the most elected members of Greek parliament for the period 1981-2019. Ιt would be particularly interesting to study the same feature by party. However, finding the most elected member per party can be challenging on parties with relatively low representation on Greek parliament. That is the reason for choosing only some of those.

Concerning the implementation part, I see that I will make some visualizations with minimum changes (e.g., filter dataset based on party, change color, etc.) so, the creation of a function is justified. That way, I will not repeat same code, following one of the principles of programming (DRY - Don’t Repeat Yourself). On the following function we can see that I set 3 variables :

  • Party : I have to specify which data to filter
  • color : In order to customize color of my barplots to be similar to each party’s color
  • times_elected_min : A argument that I added up later. The problem with its absence is that I have to deal with parties that elect for example 150 MPs and some others just 10. If I set a universal number of elections to visualize I will have diagrams with many problems. Let’s suppose that I set a big value (e.g., 10). Then I would visualize my data for ND and PASOK, although parties with relatively low number of MPs would have only one or noone to show (KKE & SYRIZA, respectively). On the other hand if I set a low value I create a new problem as there will be many MPs of and it creates the need to edit many more things (like width of bars). An argument like times_elected_min can adapt the specific characteristics of each party.
Show the code
party_plot <- function(party, party_color, times_elected_min){
  party_df = parliament %>% filter(Party == party) %>%
  select(FullName) %>% table() %>% sort(decreasing = TRUE) %>% as.data.frame() %>% filter(Freq >= times_elected_min) %>% `colnames<-`(c("Full_Name", "Freq"))
  
ggplot(data = party_df, aes(x = reorder(Full_Name , Freq), y = Freq))+
  geom_bar(stat = "identity",width = 0.88, fill = party_color) +
  geom_text(aes(label=Freq), hjust = 1.5, vjust=0.5, color="white", size=4)+
  theme_minimal() +
   labs(title = "Most elected MPs",
       subtitle = "",
       caption = "Source: stesiam | stesiam.github.io, 2022",
       x = "Times elected",
       y = "Members of Parliament") +
  coord_flip()
}

The visualizations are presented in alphabetical order.

KKE

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party_plot("KKE", "#FF6666", times_elected_min = 5)

ND

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party_plot("ND", "#0492c2", times_elected_min = 10)

PASOK

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party_plot("PASOK", "#95bb72", times_elected_min = 10)

SYRIZA

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party_plot("SYRIZA", "#e27bb1", times_elected_min = 5)

Most elected members based per constituency

So far we have seen :

  • the composition of Greek Parliament
  • the most elected MPs on national level and
  • the most elected MPs on party level

The last part of my analysis will investigate the most popular MPs on local level (per profecture).

Obviously, the same logic applies to party charts (same processes with minor changes), so the use of a function is almost mandatory (over fifty -50- cases !).

On my previous workflow I used table() function in order to take frequencies. That’s one easy way to go. Although I didn’t figured out to add characteristics from other columns (like party of the MP). Fir that reason I took the decision to use count() instead of table.

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constituency_freqs <- function(input_constituency, min_times_elected){
cont_df = parliament %>% filter(Constituency == input_constituency) %>%
  count(FullName, Party) %>% filter(n >= min_times_elected) %>% as.data.frame()
  
  ggplot(data = cont_df, aes(x = reorder(FullName, n), y = n, fill = Party ))+
  geom_bar(stat = "identity",width = 0.88) +
  scale_fill_manual(values = c("ANEL" = "#bcd2e8", "INDEPENDENT" = "#cccccc","KKE"=kke_color,"ND" = nd_color, "PASOK" = pasok_color, "SYRIZA" = syriza_color,"XA" = "#000000")) +
  geom_text(aes(label=n), hjust = 1.5, vjust=0.5, color="white", size=4)+
  theme_minimal() +
   labs(title = "Most elected MPs",
       subtitle = "",
       caption = "Source: stesiam | stesiam.github.io, 2022") +
  coord_flip()
}

Now I have frequencies and the party for every MP. That will be useful on my diagrams.

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#unique(parliament$Constituency) %>% sort()

State

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constituency_freqs("State", 3)

Attica

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constituency_freqs("Athens A",5)

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constituency_freqs("Athens B",7)

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constituency_freqs("Piraeus A",5)

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constituency_freqs("Piraeus B",5)

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constituency_freqs("Of Attica (rest)",5)

Central Greece

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constituency_freqs("Viotia",5)

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constituency_freqs("Evrytania",2)

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constituency_freqs("Fokida",2)

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constituency_freqs("Fthiotida",3)

Central Macedonia

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constituency_freqs("Thessaloniki A",6)

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constituency_freqs("Thessaloniki B",6)

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constituency_freqs("Kilkis",3)

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constituency_freqs("Pella",3)

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constituency_freqs("Pieria",3)

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constituency_freqs("Serres",3)

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constituency_freqs("Halkidiki",3)

Crete

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constituency_freqs("Chania",3)

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#constituency_freqs("Irakleio",3)
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constituency_freqs("Lasithi",3)

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constituency_freqs("Rethymno",3)

Eastern Macedonia and Thrace

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constituency_freqs("Drama",3)

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constituency_freqs("Evros",4)

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constituency_freqs("Kavala",3)

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constituency_freqs("Xanthi",3)

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constituency_freqs("Rodopi",3)

Epirus

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constituency_freqs("Arta",3)

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constituency_freqs("Ioannina",4)

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constituency_freqs("Preveza",3)

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constituency_freqs("Thesprotia",3)

Ionian Islands

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constituency_freqs("Corfu",3)

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constituency_freqs("Kefallonia",3)

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constituency_freqs("Lefkada",3)

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constituency_freqs("Zakynthos",3)

North Aegean

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constituency_freqs("Chios",3)

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constituency_freqs("Lesvos",3)

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constituency_freqs("Samos",3)

Peloponnese

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constituency_freqs("Argolida",3)

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constituency_freqs("Arcadia",3)

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constituency_freqs("Korinthia",3)

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constituency_freqs("Lakonia",3)

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constituency_freqs("Messinia",3)

South Aegean

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constituency_freqs("Dodecanese Islands",3)

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constituency_freqs("Cyclades",3)

Thessaly

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constituency_freqs("Karditsa",3)

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constituency_freqs("Larissa",3)

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constituency_freqs("Magnesia",3)

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constituency_freqs("Trikala",3)

Western Greece

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constituency_freqs("Achaia",3)

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#constituency_freqs("Aitoloakarnania",3)
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constituency_freqs("Ileia",3)

Western Macedonia

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constituency_freqs("Florina",3)

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constituency_freqs("Grevena",3)

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constituency_freqs("Kastoria",3)

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constituency_freqs("Kozani",3)

Acknowledgments

Image by Leonhard Niederwimmer from Pixabay

Data are from mine repository on GitHub. Original data are from Hellenic’s Parliament website

Αναφορές

Kunst, J. (2022). highcharter: A Wrapper for the Highcharts Library. Ανακτήθηκε από https://jkunst.com/highcharter/
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Αναφορά

Αναφορά BibTeX:
@online{2022,
  author = {, stesiam},
  title = {Περιγραφική Ανάλυση Ελληνικού Κοινοβουλίου},
  date = {2022-10-10},
  url = {https://stesiam.com/posts/2022-10-10-EDA-Greek-Parliament/},
  langid = {el}
}
Για απόδοση ευγνωμοσύνης, παρακαλούμε αναφερθείτε σε αυτό το έργο ως:
stesiam. (2022, October 10). Περιγραφική Ανάλυση Ελληνικού Κοινοβουλίου. Retrieved from https://stesiam.com/posts/2022-10-10-EDA-Greek-Parliament/