Day 1: points

I am supposed to be hiking the Routeburn Track next week (if the waist-deep snow melts and the Harris Saddle is open) so thought I would start the #30DayMapChallenge with some data about New Zealand Great Walks.

read data

library(googlesheets4)
library(tidyverse)
library(janitor)
library(rnaturalearth)
library(ggeasy)
library(ggsci)

bookings <- read_sheet("https://docs.google.com/spreadsheets/d/1v3ZIwjE3RgQ3y0seRQ1EAg1xj1VN0oVUHjGKm_-6bRY/edit?gid=0#gid=0", sheet = "gw_bookings") %>%
select(-"Total all Great Walks") %>%
  pivot_longer(names_to = "track", values_to = "count", "Abel Tasman Coast Track":"Whanganui Journey") 


tracks <- read_sheet("https://docs.google.com/spreadsheets/d/1v3ZIwjE3RgQ3y0seRQ1EAg1xj1VN0oVUHjGKm_-6bRY/edit?gid=0#gid=0", sheet = "gw_lat_long") %>%
  clean_names() 

wrangle data

tracks <- tracks %>%
 separate(start_lat_long, into = c("start_lat", "start_long"), ",") %>%
  separate(end_lat_long, into = c("end_lat", "end_long"), ",") %>%
mutate(track_format = stringr::str_extract(location, "\\([^)]+\\)$"), # extract string within last bracket
                track_format = stringr::str_remove_all(track_format, "[\\(\\)]"),  # remove brackets
                location = stringr::str_remove(location, "\\s*\\([^)]+\\)$")) %>% # make everything but the bracket content location
  mutate(start_location = str_extract(location, "^[^:]+"), # pull up to first : into start location
         end_location = str_extract(location, "(?<=;\\s)[^:]+")) %>% # pull text after ; space up to :
mutate(start_lat = as.numeric(start_lat), end_lat = as.numeric(end_lat), 
       start_long = as.numeric(start_long), end_long = as.numeric(end_long))
  

joined <- left_join(bookings, tracks, by = "track")

total_year <- joined %>%
  group_by(year, track, start_lat, start_long, track_format) %>%
  summarise(total_bookings = sum(count))

total23_24 <- total_year %>%
  filter(year == "2023-2024") %>%
  mutate(booking_category = cut(total_bookings, 
                                 breaks = c(0, 5000, 10000, Inf),
                                 labels = c("Low (0-5k)", "Medium (5-10k)", "High (>10k)"))) %>%
  mutate(track_format = fct_relevel(track_format, c("one way", "river journey", "loop track")))

plot data

nz <- ne_countries(country = "new zealand", returnclass = "sf", scale = "large")

# first plot from tracks data to check locations, includes all 11 tracks

ggplot() +
  geom_sf(data = nz) +
  geom_point(data = tracks, aes(x = start_long, y = start_lat, colour = track), size = 2) +
  coord_sf(xlim = c(166, 179), ylim = c(-47, -34)) +
  theme_minimal() +
 easy_remove_axes(which = c("both"), what = c("title")) +
  labs(title = "Where are the Great Walks of New Zealand?")

# then plot from 23-24 season from bookings joined data, only have booking data for 9 tracks


ggplot() +
  geom_sf(data = nz) +
  geom_point(data = total23_24, aes(x = start_long, y = start_lat, 
                                    colour = track_format, size = booking_category), shape = 20) +
  coord_sf(xlim = c(166, 179), ylim = c(-47, -34)) +
  theme_bw()  +
  scale_size_manual(values = c(3, 5, 7), name = "Visitors") +
   easy_remove_axes(which = c("both"), what = c("title")) + 
  scale_color_npg() +
  easy_add_legend_title("Track type") +
  guides(color = guide_legend(override.aes = list(size = 3))) + # Larger dots in the legend
  labs(title = "Which New Zealand Great Walk had the most traffic in the 2023-2024 season?", 
       subtitle = "There are 11 Great Walks in New Zealand. More than 14000 people walked the \nAbel Tasman Coast track in the 2023-2024 season. Tracks in Fiordland were also popular. \nThe Routeburn and Kepler tracks each saw between 6000 and 7000 visitors.", 
       caption = "Note: Visitor numbers for the Hump Ridge track and the \nLake Waikaremoana track were unavailable.")