readme.md

Washington Hiking

The data this week comes from Washington Trails Association courtesy of the TidyX crew, Ellis Hughes and Patrick Ward!

A video going through this data can be found on YouTube.

Their scraping code can be found on GitHub.

Get the data here

# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# 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('2020-11-24')
tuesdata <- tidytuesdayR::tt_load(2020, week = 48)

hike_data <- tuesdata$hike_data

# Or read in the data manually

hike_data <- readr::read_rds(url('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-11-24/hike_data.rds'))

Data Dictionary

hike_data.csv

variable	class	description
name	character	Name of trail
location	character	Location of Trail
length	character	Length of trail (note that most have miles included)
gain	character	Gain in elevation (Feet above sea level)
highpoint	character	Highest point in feet above sea level
rating	character	User submitted rating (out of 5)
features	character	Features
description	character	Description of trail

Cleaning Script

library(rvest)
library(tidyverse)
library(here)
library(ggplot2)
library(plotly)


scrape_trails <- function(start_int){
  page_url <- paste0(
    "https://www.wta.org/go-outside/hikes?b_start:int=",
    start_int
  )
  
  page_html <- read_html(page_url)
  
  page_html %>% 
    
    html_nodes(".search-result-item") %>% 
    
    map(
      function(hike){
        
        hike_name <- hike %>% html_nodes(".listitem-title") %>% html_nodes("span") %>%  html_text()
        hike_location <- hike %>% html_node("h3") %>% html_text()
        
        hike_stats <- hike %>% html_node(".hike-stats")
        
        hike_length <- hike_stats %>% html_nodes(".hike-length") %>%html_nodes("span") %>%  html_text()
        hike_gain <- hike_stats %>% html_nodes(".hike-gain") %>%html_nodes("span") %>%  html_text()
        hike_highpoint <- hike_stats %>% html_nodes(".hike-highpoint") %>%html_nodes("span") %>%  html_text()
        hike_rating <- hike_stats %>% html_nodes(".hike-rating") %>%html_nodes(".current-rating") %>%  html_text()
        
        hike_desc <- hike %>% html_nodes(".listing-summary") %>% html_text()
        
        hike_features <- hike %>% html_nodes(".trip-features") %>% html_nodes("img") %>% html_attr("title") %>% list()
        
        tibble(
          name = hike_name,
          location = hike_location,
          length = hike_length,
          gain = hike_gain,
          highpoint = hike_highpoint,
          rating = hike_rating,
          features = hike_features,
          description = hike_desc
        )
      }) %>% 
    bind_rows() %>% 
    mutate(description = str_remove(description, "\n") %>% str_squish())
}

start_int <- c(1, seq(30, 3840, by = 30))

hike_data <- start_int %>% 
  map_dfr(scrape_trails)

saveRDS(hike_data,file = "2020/2020-11-24/hike_data.rds")

clean_hike_data <- hike_data %>% 
  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),
    
    location_general = gsub("(.*)\\s[-][-].*","\\1",location)
  )




hike_plot <- ggplot(clean_hike_data) + 
  geom_rect(aes(
    xmin = 0,
    xmax = length_total,
    ymin = 0,
    ymax = gain,
    label = name
  ),
  alpha = .4,
  fill = "#228B22",
  color = "#765C48"
  ) + 
  facet_wrap(
    ~ location_general,
    scales = "free_x"
  ) +
  labs(
    title = "Washington State Hikes",
    x = "Hike Length (miles)",
    y = "Hike Elevation Gain (ft)",
    caption = "Data from Washingon Trails Association (wta.org) | Viz by @ellis_hughes"
  )


ggplotly(hike_plot)