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- library(dplyr)
- library(tidyr)
- dat <- read.csv("AdvancedVotesNewZealand.csv", stringsAsFactors = FALSE) %>%
- mutate(Party = gsub(" $", "", Party),
- Party = gsub("Mâori", "Māori", Party)) %>%
- pivot_wider(id_cols = c(Year, Party), names_from = Type, values_from = c(Votes, Seats)) %>%
- mutate(Votes_Other = Votes_Total - Votes_Advance,
- Advance_Proportion = Votes_Other / Votes_Total,
- Seat_Difference = Seats_Advance - Seats_Total)
-
- elec <- dat %>% group_by(Year) %>%
- summarise(Total_Votes = sum(Votes_Total),
- Total_Advance = sum(Votes_Advance),
- Total_Other = sum(Votes_Other),
- Total_Seats = sum(Seats_Total),
- Advance_Seats = sum(Seats_Advance),
- Seat_Differences = sum(abs(Seat_Difference)),
- .groups = "drop") %>%
- mutate(Prop_Advance = Total_Advance / Total_Other)
-
- partylev <- dat %>% left_join(elec, by = "Year") %>%
- mutate(PV_Prop_Total = Votes_Total / Total_Votes,
- PV_Prop_Advance = Votes_Advance / Total_Advance)
-
- partyav <- partylev %>% group_by(Party) %>%
- summarise(Total_Mean_Prop = mean(PV_Prop_Total),
- Advance_Mean_Prop = mean(PV_Prop_Advance),
- .groups = "drop") %>% arrange(desc(Total_Mean_Prop))
-
- partylev %>% select(Year:Party, Votes_Advance, Votes_Other, Seats_Advance, Seats_Total, PV_Prop_Advance, PV_Prop_Total) %>%
- pivot_longer(cols = Votes_Advance:PV_Prop_Total,
- names_to = c("Quantity", "Type"),
- names_pattern = "(.*)_([^_]*)$",
- values_to = "value") %>%
- pivot_wider(names_from = "Quantity", values_from = "value") %>% drop_na()
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