voting/LeftRight.qmd at master · kletts/voting · GitHub

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---
title: "Left Right Differences of Australian Parties"
subtitle: "2022 Federal Election Results"
author: "Christian Klettner"
format: html
editor: visual
execute:
  echo: false
  message: false
  warning: false
---

```{ojs}
Plot = import("https://cdn.jsdelivr.net/npm/@observablehq/plot/+esm")
```

```{r}
library(tidyverse)

rightness <- function(x) {
 right <- c("ALP"=1, "UAPP"=3, "ON"=3, "LP"=2,
            "GRN"=0, "LDP"=2, "CYA"=3, "AJP"= 0,
            "GVIC"=0,  "LNP"=2,"NP"=2, "GAP"=3)
 right <- c("ALP"=1, "UAPP"=3, "ON"=3,
            "GVIC"=0, "GRN"=0,
            "LP"=2, "LNP"=2, "NP"=2)
 ifelse(x %in% names(right), right[x], NA) }

data <- read_csv("data/prefflows2022.csv")
```

# Australia Preferential Voting Distribution

Left-right is scored as:

- `0` points: GRN & GVIC
- `1` points: ALP
- `2` points: LP, LNP & NP
- `3` points: UAPP, ON

Other parties and candidates are not scored. The score values therefore have no inherent meaning and the outcome should be interpreted as a relative rank.

Electorates and parties are scored based on the weighted average preference counts.


## Electoral ratings

```{r}
electorates <- data |>
  filter(CountNum==0) |>
  mutate(Right=rightness(PartyAb)) |>
  filter(!is.na(Right)) |>
  summarise(Right= sum(Right*Votes)/sum(Votes),
            Votes = sum(Votes),
            .by=c("StateAb", "DivisionId", "DivisionNm")) |>
  inner_join(data |>
               filter(CountNum==max(CountNum),
                      .by="DivisionNm") |>
               filter(Votes==max(Votes),
                      .by="DivisionNm") |>
               select(DivisionId, PartyAb, PartyNm),
             by="DivisionId")
ojs_define(elect = electorates)
```

```{ojs}
//| fig-cap: Left-Right Distribution of Electorates based on First Preferences
Plot.plot({
  height: 300,
  color: { legend: true },
  r: { range: [0, 8] },
  x: { axis: null, clip: false },
  y: { axis: null },
  clip: false,
  margin: 30,
  marks: [
    Plot.ruleY([0]),
    Plot.dotX(transpose(elect),
    Plot.dodgeY({x: "Right",
                 tip: true,
                 title: d => d.StateAb +  "\n" + d.DivisionNm,
                 fill: "PartyAb",  //"StateAb",
                 r: "Votes",
                 anchor: "middle"
                }))
  ]
})
```


## Candidates

```{r}
candidates <- data |>
  filter(CountNum > 0) |>
  filter(PrefFlow > 0) |>
  mutate(Right = rightness(PartyAb)) |>
  filter(!is.na(Right)) |>
  summarise(Right = sum(Right*PrefFlow)/sum(PrefFlow),
            Votes = sum(PrefFlow),
             .by="FromPartyAb") |>
  inner_join(
    data |>
      distinct(PartyAb, PartyNm) |>
      mutate(PartyNm = str_remove(PartyNm, "\\s\\(.+\\)")) |>
      summarise(PartyNm = head(PartyNm,1), .by="PartyAb"),
    by=c("FromPartyAb"="PartyAb")
  )
ojs_define(candi=candidates)
```


```{ojs}
//| fig-cap: Left-Right Distribution of Minor Parties and Independents based on Left-Right Preference Redistribution
Plot.plot({
  height: 300,
  color: { legend: false, scheme: "BuRd", type: "sequential", reverse: true },
  r: { range: [0, 50] },
  x: { axis: null, clip: false },
  y: { axis: null },
  //clip: false,
  margin: 30,
  marks: [
    Plot.ruleY([0]),
    Plot.dotX(transpose(candi).filter(d => !["ALP", "LP", "LNP", "NP"].includes(d.FromPartyAb)),
    Plot.dodgeY({x: "Right",
                 tip: true,
                 title: d => d.FromPartyAb + "\n" + d.PartyNm,
                 fill: "Right",
                 r: "Votes",
                 anchor: "middle"
                }))
  ]
})
```