README.Rmd

---
title: "README"
author: "Zoe Meers & Robert Hickman"
date: "19/06/2018"
output: html_document
---

```{r}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE)
source("R/parliament_data.R")
source("R/helper_funcs.R")
load("data/election_data.rda")

#extra libraries for munging and plotting
library(dplyr)
library(ggplot2)
```

# Parliament plots

This package attempts to implement "parliament plots" - visual representations of the composition of legislatures that display seats color-coded by party. The input is a data frame containing one row per party, with columns representing party name/label and number of seats,
respectively.

Inspiration from this package comes from: [parliamentdiagram](https://github.com/slashme/parliamentdiagram), which
is used on Wikipedia, [parliament-svg](https://github.com/juliuste/parliament-svg), which is a
javascript clone, and [a discussion on StackOverflow](http://stackoverflow.com/questions/42729174/creating-a-half-donut-or-parliamentary-seating-chart), which provided some of the code for part for the "arc" representations used in this package.

Unique parliament layouts:
==========================

Monkey Cage article :
<https://www.washingtonpost.com/news/monkey-cage/wp/2017/03/04/these-5-designs-influence-every-legislature-in-the-world-and-tell-you-how-each-governs/?utm_term=.e1e1c1c3c37b>


## Semicircle parliament

### EU, France, United States, and so on...

#### Data

```{r}
#filter the election data for the most recent US House of Representatives
us_congress <- election_data %>%
  filter(country == "USA" &
    year == "2016" &
    house == "Representatives")

#convert this into coordinates for plotting using parliament_data()
us_congress <- parliament_data(election_data = us_congress,
  type = "semicircle",
  total_seats = sum(us_congress$seats),
  parl_rows = 10,
  party_names = us_congress$party_short,
  party_seats = us_congress$seats)

#do the same for the Senate
us_senate <- election_data %>%
  filter(country == "USA" &
    year == "2016" &
    house == "Senate")

us_senate <- parliament_data(
  election_data = us_senate,
  type = "semicircle",
  total_seats = sum(us_senate$seats),
  parl_rows = 4,
  party_names = us_senate$party_short,
  party_seats = us_senate$seats)
```

#### Plot

```{r}
#plot the congress data
ggplot(us_congress, aes(x, y, colour = party_short)) +
  #plot the seats as dots
  geom_parliament_seats() + 
  #highlight the government with black encircling
  geom_highlight_government(government == 1) +
  #other aesthetics
  theme_void() +
  labs(colour = "",
       title = "United States Congress",
       subtitle = "Government encircled in black.") +
  annotate("text", x=0, y=0,
           label=paste("Total:", sum(us_congress$seats[which(!duplicated(us_congress$party_long))]), "Reps"),
           fontface="bold", size=8) +
  scale_colour_manual(values = us_congress$colour,
                      limits = us_congress$party_short)
```

```{r}
#do the same for the Senate
ggplot(us_senate, aes(x, y, colour = party_long)) +
  geom_parliament_seats() + 
  geom_highlight_government(government == 1) +
  theme_void() +
  labs(colour = "", 
       title = "United States Senate",
       subtitle = "Government encircled in black.") +
  annotate("text", x=0, y=0,
           label=paste("Total:", sum(us_senate$seats[which(!duplicated(us_senate$party_long))]), "Reps"),
           fontface="bold", size=8) +
  scale_colour_manual(values = us_senate$colour, 
                      limits = us_senate$party_long)
```


```{r}
#filter the election data for the most recent German federal election
germany <- election_data %>%
  filter(year==2017 & country=="Germany") %>%
  #arrange by government and seats for the plot
  arrange(government, -seats)

germany <- parliament_data(election_data = germany, 
                           total_seats = sum(germany$seats), 
                           parl_rows = 10,
                           party_seats = germany$seats, 
                           type = 'semicircle') 

ggplot(germany, aes(x,y,colour=party_short))+
  geom_parliament_seats()+
  geom_highlight_government(government==1) + 
  labs(colour="Party", title="Germany 2017 Election Results") + 
  theme_void()+
  scale_colour_manual(values = germany$colour, limits=germany$party_short)
```


## Horseshoe parliament

### Australia, New Zealand

#### Data

```{r}
australia <- election_data %>%
  filter(year == 2016 &
           country == "Australia" &
           house == "Representatives") %>%
  arrange(-government, - seats)
  
australia <- parliament_data(election_data = australia,
  total_seats = sum(australia$seats),
  party_seats = australia$seats,
  parl_rows = 4,
  type = "horseshoe")
```

#### Plot

```{r}
ggplot(australia, aes(x, y, colour = party_long)) +
  geom_parliament_seats() + 
  theme_void() +
  geom_highlight_government(government == 1) + 
  labs(colour = "", title = "Australia House of Representatives",
    subtitle = "Government encircled in black.") +
  annotate("text", x = 0, y = 0, label=paste("Total: 150 MPs"), 
           fontface="bold", size = 12) +
  scale_colour_manual(values = aus$colour, limits = aus$party_long)
```