Australian Federal Election 2022

Introduction

aec_read <- function(url_df) {
        mapping <- tibble(ID = c("24310", "27966"),
                      Year = c(2019, 2022))
        
        result <- read_csv(url_df,
                     skip = 1,
                     id = "ID",
                     show_col_types = FALSE) %>%
        mutate(ID = str_extract(ID, "[0-9]{5}"),
               across(starts_with("PartyAb"), as_factor)) %>%
        inner_join(mapping, by = "ID") %>%
        select(-ID)
        
        return(result)
}

tcp_url <- c(
        "https://results.aec.gov.au/24310/Website/Downloads/HouseTcpByCandidateByVoteTypeDownload-24310.csv",
        "https://tallyroom.aec.gov.au/Downloads/HouseTcpByCandidateByVoteTypeDownload-27966.csv"
        )


comb_tcp <- aec_read(tcp_url)

nc_url <- c("https://tallyroom.aec.gov.au/Downloads/HouseNonClassicDivisionsDownload-27966.csv",
        "https://results.aec.gov.au/24310/Website/Downloads/HouseNonClassicDivisionsDownload-24310.csv")


non_classic <- aec_read(nc_url)

Two Candiate Prefered Vote

tcp_long <- comb_tcp %>%
        group_by(Year, PartyAb) %>%
        summarise(PartyVote = sum(TotalVotes)) %>%
        summarise(PartyAb = PartyAb,
                  PartyVote = PartyVote,
                  TCP = round(100 * PartyVote / sum(PartyVote), 2))

tcp_summary <- tcp_long %>%
        pivot_wider(names_from = Year,
                    values_from = PartyVote:TCP,
                    values_fill = 0) %>%
        mutate(Swing = TCP_2022 - TCP_2019) %>%
        arrange(desc(TCP_2022))


knitr::kable(tcp_summary,
             col.names = c("Party", "TC Vote 2019", "TC Vote 2022",
                           "TCP 2019", "TCP 2022", "TCP Swing"),
             caption = "Two Candidate Vote - All Divisions"
             )

Two Candidate Vote - All Divisions

Party	TC Vote 2019	TC Vote 2022	TCP 2019	TCP 2022	TCP Swing
ALP	6414107	6476333	45.00	45.52	0.52
LP	4708843	4202555	33.04	29.54	-3.50
LNP	1624937	1550958	11.40	10.90	-0.50
IND	315239	721100	2.21	5.07	2.86
NP	779313	713303	5.47	5.01	-0.46
GRN	217663	387247	1.53	2.72	1.19
XEN	62124	71495	0.44	0.50	0.06
KAP	58231	59028	0.41	0.41	0.00
CLP	47415	44767	0.33	0.31	-0.02
ON	25521	0	0.18	0.00	-0.18

Two Candidate Prefered - Classic Divisions

classic_tcp <- comb_tcp %>%
        anti_join(non_classic,
                  by = c("Year", "DivisionNm")) %>%
        mutate(PartyAb = as_factor(PartyAb),
               PartyAb = fct_collapse(PartyAb,
                                      LNC = c("LP", "NP", "LNP", "CLP")))

ctcp <- classic_tcp %>%
        group_by(Year, PartyAb) %>%
        summarise(PartyVote = sum(TotalVotes)) %>%
        mutate(PartyPC = round(PartyVote / sum(PartyVote) * 100, 2))

ctcp_summary <- ctcp %>%
        select(-PartyVote) %>%
        pivot_wider(names_from = Year,
                    values_from = PartyPC,
                    values_fill = 0) %>%
        mutate(Swing = `2022` - `2019`)
        

knitr::kable(ctcp_summary, 
             col.names = c("Party", "2019", "2022", "Swing"),
             caption = "Classic Two Party Prefered")

Classic Two Party Prefered

Party	2019	2022	Swing
LNC	51.55	48.2	-3.35
ALP	48.45	51.8	3.35