library(tidytuesdayR)
library(tidyverse)
library(skimr)
library(ggthemes)
library(gt)
library(lubridate)
library(skimr)
library(lutz)
library(maps)
library(scales)
library(sf)
library(ggimage)

# Get the Data

# Read in with tidytuesdayR package 
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

#tuesdata <- tidytuesdayR::tt_load('2023-03-21')
tuesdata <- tidytuesdayR::tt_load(2023, week = 13)

transitions <- tuesdata$transitions
timezones <- tuesdata$timezones
timezone_countries <- tuesdata$timezone_countries
countries <- tuesdata$countries

transitions <- transitions %>%
  mutate(begin = as_datetime(begin), end = as_datetime(end))

num_zones <- timezone_countries %>%
  count(country_code, sort = TRUE)

num_zones %>% 
  filter(n > 1) %>%
  left_join(countries) %>%
  select(place_name, n) %>%
  filter(place_name != "NA") %>%
  gt() %>%
  cols_label(place_name = "Country", n = "Number of TZs") %>%
  opt_stylize(style = 6, color = "blue", add_row_striping = TRUE) %>%
  tab_header(title = "Countries with Multiple TZs") 

US_tz <- timezone_countries %>% 
  filter(country_code == "US") %>%
  left_join(timezones)

Joining with `by = join_by(zone)`

 tz_plot(US_tz$zone[21])

wd <- getwd()
filepath = file.path(wd)


make_filename = function(number){
  # doing this, putting it all on a single line or using pipe %>%
  # is just matter of style
  filename = paste("tzplot", number, sep="_")
  filename = paste0(filename, ".png")
  filename = file.path(filepath, filename)
  
  filename
}

#creating a variable to store the files name
US_tz <- US_tz %>%
  mutate(image_name = "tbd")

index <- 1
for (index in seq(1, nrow(US_tz))) {
  filename = make_filename(index)
  US_tz[index , "image_name"] <- filename
  # 1. Open jpeg file
  png(filename, width = 350, height = 350, bg = "transparent")
  # 2. Create the plot
  # you need to print the plot if you call it inside a loop
  print(tz_plot(US_tz$zone[index]))
  # 3. Close the file
  dev.off()
  index = index + 1
}

aspect_ratio <- 1.618  

us_tz_map <- map_data("world") %>% 
  ggplot(aes(long, lat)) +
  geom_polygon(aes(group = group), fill = "white", 
               color = "gray30", alpha = 0.9) +
  geom_image(aes(x = longitude, latitude, image = image_name), 
             data = US_tz, size = 0.025, by = "width",
             asp = aspect_ratio) +
  coord_sf() +
  labs(title = "The United States has 29 Timezone- Mostly Redunant",
       caption = "Data from: https://data.iana.org/time-zones/tz-link.html") +
  theme_void() +
  theme(aspect.ratio = 1/aspect_ratio,
    legend.position = "bottom",
    plot.background = element_rect(fill = "white", color = "white")
    )

ggsave("thumbnail.png", us_tz_map, width = 5 * aspect_ratio, height = 5)
us_tz_map

US_tz %>%
  select(zone, latitude, longitude) %>%
  arrange(longitude) %>%
  gt() %>%
  opt_stylize(style = 6, color = "blue", add_row_striping = TRUE) %>%
  tab_header(title = "Countries with Multiple TZs")