weekly_deaths <- linelist %>%
  group_by(epiweek = floor_date(date_onset, "week")) %>%  # create and group data by epiweek column
  summarise(                                              # create new summary data frame:
    n_known_outcome = sum(!is.na(outcome), na.rm=T),      # number of cases per group with known outcome
    n_death  = sum(outcome == "Death", na.rm=T),          # number of cases per group who died
    pct_death = 100*(n_death / n_known_outcome)           # percent of cases with known outcome who died
  )

deaths_plot <- ggplot(data = weekly_deaths)+            # begin with weekly deaths data
  geom_line(mapping = aes(x = epiweek, y = pct_death))  # make line 

deaths_plot   # print

deaths_plot %>% plotly::ggplotly()

# Make epidemic curve with incidence2 pacakge
p <- incidence2::incidence(
  linelist,
  date_index = date_onset,
  interval = "weeks",
  groups = outcome) %>% plot(fill = outcome)

# Plot interactively  
p %>% plotly::ggplotly()

# import data
facility_count_data <- rio::import(here::here("data", "malaria_facility_count_data.rds"))

# aggregate data into Weeks for Spring district
agg_weeks <- facility_count_data %>% 
  filter(District == "Spring",
         data_date < as.Date("2020-08-01")) %>% 
  mutate(week = aweek::date2week(
    data_date,
    start_date = "Monday",
    floor_day = TRUE,
    factor = TRUE)) %>% 
  group_by(location_name, week, .drop = F) %>%
  summarise(
    n_days          = 7,
    n_reports       = n(),
    malaria_tot     = sum(malaria_tot, na.rm = T),
    n_days_reported = length(unique(data_date)),
    p_days_reported = round(100*(n_days_reported / n_days))) %>% 
  right_join(tidyr::expand(., week, location_name)) %>% 
  mutate(week = aweek::week2date(week))

# create plot
metrics_plot <- ggplot(agg_weeks,
       aes(x = week,
           y = location_name,
           fill = p_days_reported))+
  geom_tile(colour="white")+
  scale_fill_gradient(low = "orange", high = "darkgreen", na.value = "grey80")+
  scale_x_date(expand = c(0,0),
               date_breaks = "2 weeks",
               date_labels = "%d\n%b")+
  theme_minimal()+ 
  theme(
    legend.title = element_text(size=12, face="bold"),
    legend.text  = element_text(size=10, face="bold"),
    legend.key.height = grid::unit(1,"cm"),
    legend.key.width  = grid::unit(0.6,"cm"),
    axis.text.x = element_text(size=12),
    axis.text.y = element_text(vjust=0.2),
    axis.ticks = element_line(size=0.4),
    axis.title = element_text(size=12, face="bold"),
    plot.title = element_text(hjust=0,size=14,face="bold"),
    plot.caption = element_text(hjust = 0, face = "italic")
    )+
  labs(x = "Week",
       y = "Facility name",
       fill = "Reporting\nperformance (%)",
       title = "Percent of days per week that facility reported data",
       subtitle = "District health facilities, April-May 2019",
       caption = "7-day weeks beginning on Mondays.")

metrics_plot # print

metrics_plot %>% 
  plotly::ggplotly() %>% 
  plotly::partial_bundle() %>% 
  plotly::config(displaylogo = FALSE, modeBarButtonsToRemove = plotly_buttons_remove)