Washington Trails

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# Washington Trails - Length and elevation gain of 1,958 hiking trails in Washington State
# Created by: reddit.com/user/brianhaas19

### Link to data
# https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-11-24

### Setup
library(tidyverse)
library(tidytuesdayR)
library(scales)
library(gganimate)
theme_set(theme_bw())

### Load data
tt <- tt_load("2020-11-24")
hike <- tt$hike_data

### Clean data
# The `length` variable needs to be separated into 2 variables; one numerical variable for the length in miles and
# one categorical variable for type of trip. The `gain`, `highpoint` and `rating` variables simply need to be converted
# to numeric variables. The post includes a script for cleaning the data:
hike <- hike %>%
  mutate(
    trip = case_when(
      grepl("roundtrip",length) ~ "roundtrip",
      grepl("one-way",length) ~ "one-way",
      grepl("of trails",length) ~ "trails"),

    length_total = as.numeric(gsub("(\\d+[.]\\d+).*","\\1", length)) * ((trip == "one-way") + 1),

    gain = as.numeric(gain),
    highpoint = as.numeric(highpoint),
    rating = as.numeric(rating),

    location_general = gsub("(.*)\\s[-][-].*","\\1",location)
  )

### Wrangle data
# The hikes will be plotted as triangles using `geom_poly`. The triangles will be have the origin (0, 0) as one point,
# (`length_total`, 0) as the second point, and (`length_total`, `gain`) as the third point. The data be wrangled into
# the appropriate format using `pivot_longer()`. In addition to one triangle to represent each hike, there will be a
# triangle representing the average hike. The data will be facetted by `location_general` and the average hike will be
# added to each facet. Using `free` scales looks the best but makes comparing across facets hard. The average hike being
# included on every facet will help with this.
hike_points <- hike %>%
  select(location_general, length_total, gain, highpoint) %>%
  mutate(
    id = row_number(),
    x1 = 0,
    y1 = 0,
    x2 = length_total,
    y2 = 0,
    x3 = length_total,
    y3 = gain
  ) %>%
  select(id, location_general, x1:y3)

# Pivot the table to long format once each for the x and y coordinates:
hike_x <- hike_points %>%
  pivot_longer(cols = c(x1, x2, x3), names_to = "x.position", values_to = "x") %>%
  select(id, location_general, x)
hike_y <- hike_points %>%
  pivot_longer(cols = c(y1, y2, y3), names_to = "y.position", values_to = "y") %>%
  select(id, y)

# Combine into one table of x-y coordinates:
hike_xy <- cbind(hike_x, select(hike_y, -id))

# Get the average for all of the data:
average_peak <- hike %>%
  summarise(mean_length_total = mean(length_total),
            mean_gain = mean(gain))
average_peak_points <- data.frame(
  id = c('1', '1', '1'),
  x = c(0, average_peak$mean_length_total, average_peak$mean_length_total),
  y = c(0, 0, average_peak$mean_gain)
)

### Visualize
# Colors (taken from the WTA website):
green <- "#4A7628"
grey <- "#231F20"
lightgrey <- "#F5F5F5"

# Plot
# Recommended parameters for R notebook: fig.height=9, fig.width=12
p <- ggplot(hike_xy, aes(x = x, y = y)) +
  geom_polygon(aes(group = id), color = green, fill = green, alpha = 0.1, size = 0.1) +
  geom_polygon(data = average_peak_points, aes(x, y, group = id), fill = grey, alpha = 0.4) +
  scale_y_continuous(labels = comma) +
  facet_wrap(~location_general, scales = "free") +
  labs(x = "Hike Length (miles)", y = "Hike Elevation Gain (ft)",
       title = expression(paste(bold("Washington Trails"), " - Length and elevation gain of 1,958 hiking trails in Washington State")),
       subtitle = "The grey shaded area shows what the overall average hike looks like compared to the hikes in that region.",
       caption = expression(paste("Created by: ", italic("reddit.com/user/brianhaas19"), "\tData source: ", italic("https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-11-24")))) +
  theme(panel.grid = element_blank(),
        plot.background = element_rect(fill = lightgrey),
        strip.background = element_rect(fill = grey),
        strip.text = element_text(color = "white"),
        plot.caption = element_text(hjust = 0))
p

# Uncomment this line to save to disk:
ggsave(str_c(getwd(), "/washington_hiking.png"),
       plot = p, height = 9, width = 12)
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