DHBmap

library(readr)
library(dplyr)
library(lubridate)
library(RcppRoll)
library(ggplot2)
library(ggthemes)
library(sf)
library(rmapshaper)
library(tidyr)
library(patchwork)

colset <- colorblind_pal()(6)

south_island <- c("Canterbury", "Nelson Marlborough", "South Canterbury",
                  "Southern", "West Coast")
DHBpop <- read_csv("../misc_data/DHBpop.csv") %>%
  arrange(DHB)

# daily full demographic csvs folder

cases_folder <- list.files("../daily_all_cases", pattern = "csv$", full.names = TRUE)
all_csvs <- file.info(cases_folder)
# find most recently created csv file
latest_csv <- row.names(all_csvs)[which.max(all_csvs$ctime)]
all_cases <-  read_csv(latest_csv, col_types= cols(
  `Report Date` = col_date(format = ""),
  .default = col_character()))

nz <- all_cases %>%
  filter(DHB != "Managed Isolation & Quarantine",
         is.na(Historical)) %>%
  count(DHB,Date = `Report Date`) %>%
  spread(key=DHB, value=n, fill = 0) %>%
  gather(key="DHB", value="n", Auckland:Whanganui) %>%
  arrange(DHB, desc(Date)) %>%
  group_by(DHB) %>%
  slice(2:9) %>% 
  summarise(tnew = paste("from", Date[4], "to", Date[1], "inclusive"),
            told = paste("previous 4 days", Date[8], "to", Date[5], "inclusive"),
            nnew = sum(n[1:4]),
            nold = sum(n[5:8])) %>%
  arrange(DHB) %>%
  mutate(name2 = DHBpop$DHB,
         population = DHBpop$pop2021,
         percent_new = 100 * nnew/population,
         diff_to_old = percent_new - 100 * nold/population)
# DHB boundaries map from Koordinates
DHB_map <- st_read("../misc_data/nzdhb.kml", quiet=TRUE)

# simplify the graph to what is visually useful
# this is a figure cm tall, and doesn't need life sized detail
DHBborder <- ms_simplify(DHB_map,  keep=0.05) %>%
  arrange(Name)

nzset <- bind_cols(nz, DHBborder)


# graphs, 4 figures patchworked together

gnd <- nzset %>% 
  filter(!(DHB %in% south_island))

g1g <- ggplot(gnd, aes(geometry=geometry, fill=percent_new)) + 
  geom_sf(size=0.1, colour="black") +
  scale_fill_gradient(limits=c(0,max(nzset$percent_new)),
                      low="white", high="darkred", name = "Percent") + 
  theme_void() + 
  labs(title="North Island, percent of DHB population\ninfected in four days",
        subtitle=nzset$tnew[1])
g1g
gsd <- nzset %>% 
  filter(DHB %in% south_island)

g2g <- ggplot(gsd, aes(geometry=geometry, fill=percent_new)) + 
  geom_sf(size=0.1, colour="black") +
  scale_fill_gradient(limits=c(0,max(nzset$percent_new)),
                      low="white", high="darkred", name = "Percent") + 
  theme_void() + 
  labs(title="South Island, percent of population\ninfected in four days",
       subtitle=nzset$tnew[1])
g2g

g3g <- ggplot(gnd, aes(geometry=geometry, fill=diff_to_old)) + 
  geom_sf(size=0.1, colour="black") +
  scale_fill_gradient2(limits=c(min(nzset$diff_to_old),max(nzset$percent_new)),
                      low="darkblue", mid="white", high="darkred", name = "Percent") + 
  theme_void() + 
  labs(title="NI, change from previous four days\nas percent of DHB population",
       subtitle=nzset$told[1])
g3g

g4g <- ggplot(gsd, aes(geometry=geometry, fill=diff_to_old)) + 
  geom_sf(size=0.1, colour="black") +
  scale_fill_gradient2(limits=c(min(nzset$diff_to_old),max(nzset$percent_new)),
                       low="darkblue", mid="white", high="darkred", name = "Percent") + 
  theme_void() + 
  labs(title="SI, change from previous four days\nas percent of DHB population",
       subtitle=nzset$told[1])
g4g
g5 <- (g1g + g2g) / (g3g + g4g)
ggsave(filename="~/Desktop/maps.png", plot=g5,dpi=72, units="in", 
       bg="white", height = 5.556 * 1.6, width=9.877* 1.6)