using geom_col to regulate don't knows to below the main plot

ordinalAndDontKnow.R

library(foreign)
library(dplyr)
library(tidyr)
library(ggplot2)
library(viridis)
library(ggpattern)
source("../theme.R")

sav23 <- suppressWarnings(read.spss("2_NZES23Release_100227.sav",
                                    to.data.frame = TRUE, add.undeclared.levels = "sort"))
meta23 <- data.frame(varnames = attributes(sav23)$names,
                     descriptions = attributes(sav23)$variable.labels,
                     stringsAsFactors = FALSE)
# Validated Party Vote mvpartyvote
# A11d: How much trust do you have in scientists? A11d
# Validated Vote Comprehensive Vote Weight vwgt (everyone has a weight)
# H27: Have you been vaccinated for COVID-19, and if so, how many times? H27

prosci <- sav23 |> 
  mutate(trust = case_when(A11d == "9. Don’t know" ~ "Don't Know/\nDidn't Answer",
                           is.na(A11d) ~ "Don't Know/\nDidn't Answer",
                           TRUE ~ gsub(".*\\. ","",A11d))) |> 
  summarise(.by=c(mvpartyvote, trust),
            weighted = sum(vwgt)) |> 
  group_by(mvpartyvote) |> 
  mutate(percentage= 100*weighted/sum(weighted),
         percent= ifelse(trust == "Don't Know/\nDidn't Answer", percentage * -1, percentage),
         party = ifelse(is.na(mvpartyvote),"unknown", gsub(".*\\. ","",mvpartyvote))) |> 
  ungroup()

# We have three columns we care about at this point, the x axis, the y axis,
# and the fill (the fill includes the don't know category + Likert/Ordinal data)

partorder <- prosci |> 
  filter(trust %in% c("Trust a lot", "Trust somewhat")) |> 
  summarise(.by=party,
            orderscore = sum(percent)) |> 
  arrange(desc(orderscore))

vpal <- viridis_pal(alpha = 1, 
                    begin = 0, 
                    end = .9, 
                    direction = -1, 
                    option = "C")(4)
vcol <- c("#EEEEEE",vpal)

factrst <- c("Don't Know/\nDidn't Answer", "Trust a lot", "Trust somewhat", "Don’t trust very much", "Don’t trust at all")

prosci |> 
  mutate(party = factor(party, levels=partorder$party),
         trust = factor(trust, levels = factrst[c(1,5,4,3,2)])) |> 
  ggplot(aes(x=party,y=percent)) + 
  geom_col_pattern(aes(fill = trust,
       pattern_alpha = trust),
   colour="black",linewidth=0.05,
   pattern = 'plasma',
   pattern_fill = "grey0") +
  theme_david() +
  scale_fill_manual(name="Trust level", values=vcol) +
  scale_pattern_alpha_manual(name="Trust level", values = c(.5,0,0,0,0)) +
  scale_y_continuous(breaks=c(-15,0,25,50,75,100),
                     labels=c(15,0,25,50,75,100)) +
  labs(title="NZES 2023: How much trust do you have in scientists? by party vote",
       subtitle="In order of trust, most trusting on left. Weighted percent.",
       y="Percent of voters", x="Party voted for",
       caption="Source: 2023 NZES")+
  theme(axis.line.y = element_blank(), axis.line.x = element_blank())+
  geom_hline(yintercept=0, linewidth=.3, colour="white")

ggsave(filename="~/Desktop/trustsci.jpg",dpi=300, 
       units="in", bg="white", height = 4.5, 
       width=8)