Last updated: 2021-09-08

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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 original scraping code can be found on GitHub. The Washington Trails Assocation website includes this leaflet-style map visual

Let’s setup with the tidyverse, a theming package hrbrthemes, some web scraping tools, and table building tools.

suppressPackageStartupMessages({
library(tidyverse)
library(hrbrthemes)
extrafont::loadfonts(quiet = TRUE)
library(rvest)
library(gt)
library(fontawesome)
library(htmltools)

library(ggridges)

})

source(here::here("code","_common.R"),
       verbose = FALSE,
       local = knitr::knit_global())
Registered S3 method overwritten by 'tune':
  method                   from   
  required_pkgs.model_spec parsnip
ggplot2::theme_set(theme_jim(base_size = 12))

The original dataset provided by R4DS:

tt <- tidytuesdayR::tt_load("2020-11-24")

    Downloading file 1 of 1: `hike_data.rds`

After visiting the Washington Trails Association web site, I had expected to find 3869 hikes. It turns out that the R4DS scraping script skips all trails that lack elevation, gain, length, or rating figures. For more advanced visuals, I decided to go back and re-visit the scraping algorithm to also gather the geospatial coordinates in the sub pages. The update is here:

hike_data_rds <- here::here("data/hike_data.rds")

if (!file.exists(hike_data_rds)) {
  
  hike_pages <- 1:129
  
  hike_data <- lapply( hike_pages - 1 , function(page){
    
    start_int <- 30 * page
    
    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(".item-header") %>% 
            html_nodes(".listitem-title") %>% 
            html_nodes("span") %>%  
            html_text()

          hike_page <- hike %>% 
            html_nodes(".item-header") %>% 
            html_nodes(".listitem-title") %>% 
            html_attr("href") 
          
          hike_location <- hike %>% 
            html_nodes(".item-header") %>% 
            html_nodes(".region") %>% 
            html_text()

          hike_desc <- hike %>% 
            html_nodes(".hike-detail") %>% 
            html_nodes(".listing-summary") %>% 
            html_text() %>% 
            stringr::str_trim(side = "both")
          
          hike_features <- hike %>% 
            html_nodes(".hike-detail") %>% 
            html_nodes(".trip-features") %>% 
            html_nodes("img") %>% 
            html_attr("title") %>% 
            list()
          
          tibble(
            name = hike_name,
            location = hike_location,
            features = hike_features,
            description = hike_desc,
            hike_page = hike_page
          )
        }) %>% 
      bind_rows() %>% 

      mutate(stats = map(hike_page, function(hike_page){
          print(hike_page)

          stats_html <- read_html(hike_page) 
          
          length <- stats_html %>% 
                    html_nodes(".hike-stat") %>%
                    html_nodes("#distance") %>%
                    html_nodes("span") %>%
                    html_text()
          
          elevation <- stats_html %>% 
                       html_nodes(".hike-stat") %>% 
                       `[[`(3)
          
          elevation_state <- case_when(
                sum(html_text(elevation) %>% str_detect(c("Gain","Point"))) == 2 ~ "both",
                html_text(elevation) %>% str_detect("Gain") ~ "gain",
                html_text(elevation) %>% str_detect("Point") ~ "highpoint",
                TRUE ~ "neither"
          )
          
          if ( elevation_state == "both") { 
            gain <- elevation %>%
                            html_nodes("span") %>% 
                            html_text() %>% 
                            `[[`(1)
            
            highpoint <- elevation %>%
                            html_nodes("span") %>% 
                            html_text() %>% 
                            `[[`(2)
            
            } else if ( elevation_state == "highpoint") {
             
             highpoint <- elevation %>%
                            html_nodes("span") %>% 
                            html_text() 
             
             gain <- NA_character_
             
           } else if ( elevation_state == "gain") {
             
             gain <- elevation %>%
                            html_nodes("span") %>% 
                            html_text() 
             
             highpoint <- NA_character_
             
           } else if (elevation_state == "neither") {
             highpoint <- NA_character_
             gain <- NA_character_
           }
         
      rating <- stats_html %>% 
                      html_nodes(".hike-stat") %>% 
                      `[[`(4) %>%
                      html_nodes(".current-rating") %>%
                      html_text()
            
      longitude <- if (is_empty(stats_html %>% 
                       html_nodes(".latlong") %>%
                       html_node("span") %>% 
                       html_text)) {NA_character_} else {
        
                      stats_html %>% 
                      html_nodes(".latlong") %>%
                      html_nodes("span") %>% 
                      html_text() %>% 
                      `[[`(1) }
      
      latitude <- if (is_empty(stats_html %>% 
                       html_nodes(".latlong") %>%
                       html_node("span") %>% 
                       html_text)) {NA_character_} else {
        
                      stats_html %>% 
                      html_nodes(".latlong") %>%
                      html_nodes("span") %>% 
                      html_text() %>% 
                      `[[`(2) }
      
    stats <- tibble(
        length = length,
        gain = gain,
        highpoint = highpoint,
        rating = rating,
        longitude = longitude,
        latitude = latitude)
      
    return(stats)
      }))
    
  }) %>% 
    bind_rows() %>% 
    unnest_wider(stats) 
  
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)) %>% 
   mutate(
    across(c(length, gain, highpoint, longitude, latitude, rating), parse_number))  
  write_rds(hike_data, file = hike_data_rds)
  
}else{
  
  hike_data <- read_rds(hike_data_rds)
  
}

We will further separate the location field into region and subregion, by the hyphen.

hike_data <- hike_data %>% 
   separate(location, into = c("region","subregion"), sep = " -- ", fill = "right") %>% 
   replace_na(list(subregion = "Other")) %>% 
   mutate(across(c(name, region, subregion), as_factor))

First, a table of summarized trail data by Region

rating_stars <- function(rating, max_rating = 5){
  rounded_rating <- floor(rating + 0.5)
  stars <- lapply(seq_len(max_rating), function(i){
    if (i <= rounded_rating) fontawesome::fa("star", fill = "orange") 
    else fontawesome::fa("star", fill = "grey")
  })
  label <- sprintf("%s out of %s", rating, max_rating)
  div_out <- div(title = label, "aria-label" = label, role = "img", stars)
  
  as.character(div_out) %>% 
    gt::html()
}

hike_data %>% 
  group_by(region, subregion) %>% 
  summarize(total_length = round(sum(length, na.rm = TRUE)),
            average_gain = round(mean(gain, na.rm = TRUE)),
            min_rating = round(min(rating, na.rm = TRUE),1),
            max_rating = round(max(rating, na.rm = TRUE),1),
            number = n(),
            .groups = "drop") %>% 
  mutate(subregion = fct_reorder(subregion, desc(number)),
         min_rating = map(min_rating, rating_stars),
         max_rating = map(max_rating, rating_stars)) %>%    
  select(region, subregion, number, total_length, average_gain, min_rating, max_rating) %>% 
  arrange(region, subregion) %>% 
  group_by(region) %>% 
  gt() %>% 
  tab_header(title = gt::html("<span style='color:darkgreen'>Washington State Hiking Trail Summary by Region</span>")) %>% 
  cols_label(subregion = "Region",
             number = "Number of Trails",
              total_length = md("Total Milage All Trails"),
              average_gain = md("Average Elevation Gain (Feet)"),
              min_rating = md("**Min Rating**"),
              max_rating = md("**Max Rating**")) %>% 
  opt_row_striping() %>% 
  tab_options(data_row.padding = px(3))
Washington State Hiking Trail Summary by Region
Region Number of Trails Total Milage All Trails Average Elevation Gain (Feet) Min Rating Max Rating
North Cascades
North Cascades Highway - Hwy 20 149 1272 2902
Mountain Loop Highway 147 907 2069
Methow/Sawtooth 92 550 2425
Pasayten 86 927 3008
Mount Baker Area 77 421 1756
Other 139 117 3754
Mount Rainier Area
Chinook Pass - Hwy 410 84 547 1611
SW - Longmire/Paradise 75 484 1810
NW - Carbon River/Mowich 39 411 2582
NE - Sunrise/White River 31 235 2402
SE - Cayuse Pass/Stevens Canyon 29 184 1567
Other 25 110 9300
Southwest Washington
Columbia River Gorge - WA 62 460 1618
Lewis River Region 38 269 1115
Vancouver Area 34 112 109
Columbia River Gorge - OR 34 134 1231
Long Beach Area 24 73 147
Other 1 2 NaN
Puget Sound and Islands
Seattle-Tacoma Area 266 902 273
Bellingham Area 89 378 662
San Juan Islands 47 142 543
Whidbey Island 33 157 356
Other 15 1242 120
Olympic Peninsula
Hood Canal 111 781 2206
Northern Coast 104 707 1967
Olympia 60 282 696
Pacific Coast 56 417 928
Kitsap Peninsula 28 103 332
Other 12 24 2250
Central Cascades
Stevens Pass - West 119 536 1638
Stevens Pass - East 96 798 2981
Entiat Mountains/Lake Chelan 95 695 2516
Leavenworth Area 70 389 2599
Blewett Pass 38 149 1171
Other 94 54 4238
Snoqualmie Region
Snoqualmie Pass 123 814 2330
Salmon La Sac/Teanaway 122 723 2318
North Bend Area 107 655 2014
Cle Elum Area 26 168 1986
Other 21 24 3018
South Cascades
Mount St. Helens 63 474 1548
Mount Adams Area 62 357 1324
White Pass/Cowlitz River Valley 62 501 1894
Goat Rocks 33 314 2018
Dark Divide 24 146 1536
Other 107 318 2322
Central Washington
Yakima 59 368 1221
Tri-Cities 28 148 528
Wenatchee 28 124 935
Potholes Region 23 85 315
Other 2 1 30
Grand Coulee 13 54 363
Eastern Washington
Spokane Area/Coeur d'Alene 108 638 831
Okanogan Highlands/Kettle River Range 54 412 1642
Palouse and Blue Mountains 54 548 1914
Selkirk Range 50 290 1580
Other 29 46 825
Issaquah Alps
Tiger Mountain 60 245 1297
Cougar Mountain 55 89 397
Squak Mountain 25 82 1424
Other 21 82 320
Taylor Mountain 12 34 475

Another table, adapted from Kaustav Sen’s post

thumbs_up <- function(value) {
  icon <- fa("thumbs-up", fill = "#38A605")
  div(icon) %>% 
    as.character() %>% 
    html()
}

bar_chart <- function(value, color = "#DFB37D"){
    glue::glue(
      "<span style=\"display: inline-block; 
      direction: ltr; border-radius: 4px; 
      padding-right: 2px; background-color: {color}; 
      color: {color}; width: {value}%\"> &nbsp; </span>"
    ) %>% 
    as.character() %>% 
    gt::html()
}


hike_data %>% 
  group_by(region) %>% 
  arrange(desc(rating)) %>% 
  slice(1:4) %>% 
  ungroup() %>% 
  select(-description, -gain, -highpoint, -latitude, -longitude,
         -hike_page, -subregion, -length_total, -trip) %>%
  mutate(rating = if_else(is.na(rating),0,rating), 
    length_bar = map(length, bar_chart),
    rating = map(rating, rating_stars),
    feature_present = 1,
    feature_present = map(feature_present, thumbs_up)
  ) %>% 
  unnest_longer(features) %>% 
  mutate(features = str_replace(features, pattern = "/.+", replacement = "")) %>% 
  pivot_wider(
    names_from = features,
    values_from = feature_present
  ) %>%
  group_by(region) %>% 
  arrange(-length) %>% 
  select(name, length, length_bar, rating, everything()) %>% 
  gt() %>% 
  cols_width(
    "name" ~ px(250),
    "length" ~ px(75),
    "length_bar" ~ px(140),
    "rating" ~ px(140),
    4:last_col() ~ px(120)
  ) %>% 
  tab_spanner(
    label = "Features",
    columns = 5:last_col()
  ) %>% 
  cols_label(
    name = "",
    length = md("Length<br/>(in miles)"),
    length_bar = "",
    rating = "Rating"
  ) %>% 
  tab_header(
    title = "Top Hiking Trails to visit in Washington",
    subtitle = "The 4 top rated trails in each of the 11 major regions of Washington State"
  ) %>% 
  tab_source_note(
    source_note = md("**Data:** Washington Trails Association | **Table:** Jim Gruman")
  ) %>% 
  tab_style(
    style = cell_text(
      font = google_font("Alegreya Sans SC"),
      weight = "bold"
    ),
    locations = list(cells_row_groups(), cells_column_spanners("Features"))
  ) %>% 
  tab_style(
    style = cell_fill(
      color = "#DFB37D",
      alpha = 0.5
    ),
    locations = cells_row_groups()
  ) %>% 
  tab_style(
    style = cell_text(
      font = google_font("Alegreya Sans"),
      v_align = "bottom",
      align = "center"
    ),
    locations = cells_column_labels(columns = everything())
  ) %>% 
  tab_style(
    style = cell_text(
      font = google_font("Alegreya Sans"),
    ),
    locations = cells_body("name")
  ) %>% 
  tab_style(
    style = cell_text(
      font = google_font("Alegreya Sans"),
      align = "left",
      size = px(30)
    ),
    locations = cells_title("subtitle")
  ) %>% 
  tab_style(
    style = cell_text(
      size = "medium"
    ),
    locations = cells_body(columns = 4:last_col())
  ) %>%
  tab_style(
    style = cell_text(
      align = "left"
    ),
    locations = cells_body(columns = "length_bar")
  ) %>%
  tab_style(
    style = cell_text(
      font = google_font("Alegreya Sans SC"),
      align = "left",
      weight = "bold",
      size = px(50)
    ),
    locations = cells_title("title")
  ) %>% 
  opt_table_font(font = google_font("Fira Mono")) %>% 
  tab_options(
    table.border.top.color = "white",
  )
Top Hiking Trails to visit in Washington
The 4 top rated trails in each of the 11 major regions of Washington State
Length
(in miles)		 Rating Features
Wildflowers Ridges Wildlife Mountain views Summits Dogs allowed on leash Fall foliage Rivers Lakes NA Dogs not allowed Established campsites Old growth Good for kids Waterfalls Coast
Central Cascades
Icicle Divide: Stevens Pass to Fourth of July 45.00  
Little Annapurna 7.30  
Blackbird Island - Waterfront Park 2.00  
Bygone Byways Interpretive Trail 1.00  
Eastern Washington
Kettle Crest Trail 44.00  
Liberty Lake Regional Park - Liberty Lake Loop Trail 8.40  
Wenatchee Guard Station 5.80  
Columbia Mountain 5.40  
Mount Rainier Area
Eastside Loop 36.00  
Indian Bar - Summerland Traverse 34.00  
Wonderland Trail - Mowich Lake to Sunrise via Spray Park 20.50  
Old Mine Trail 3.40  
North Cascades
Seven Pass Loop (Pasayten) 26.60  
Glacier Peak Meadows 25.00  
North Lake 10.60  
Eldorado Peak 10.00  
Olympic Peninsula
Capitol State Forest - McKenny Trail 13.80  
Lyre Conservation Area 2.50  
High Ridge Trail 0.80  
Illahee State Park 0.50  
Snoqualmie Region
Esmeralda Peak Loop 12.10  
Mount Stuart 11.10  
Tanner Landing Park 1.00  
Roaring Creek 0.60  
Central Washington
Ahtanum State Forest - Whites Ridge 10.90  
Candy Mountain Trail 3.60  
East Rim Waterworks Canyon 3.00  
Ohme Gardens County Park 1.00  
South Cascades
Plains of Abraham - Windy Pass Loop 9.00  
Fossil Trail 8.00  
Mountain Climbers Trail 6.80  
Bench Lake Loop 3.40  
Southwest Washington
Lewis and Clark Discovery Trail 7.00  
Cussed Hollow 5.00  
La Center Bottoms 0.66  
Cape Disappointment State Park - Cape Disappointment Lighthouse 0.60  
Puget Sound and Islands
Turtleback Mountain Preserve: Turtlehead Summit 5.70  
Mount Grant Preserve 4.60  
Leque Island - Stanwood Levee Trail 1.40  
Nathan Chapman Memorial Trail 0.60  
Issaquah Alps
Coyote Creek 1.10  
Paw Print Connector 1.10  
Nike Horse Trail 0.60  
Long View Peak 0.30  
Data: Washington Trails Association | Table: Jim Gruman

What is the distribution of hiking trail lengths around the state regions?

hike_data %>% 
  filter(!is.na(length)) %>% 
  ggplot(aes(length, region, fill = region)) +
  geom_density_ridges(alpha = 0.9, show.legend = FALSE) +
  scale_x_log10(labels = scales::comma_format(accuracy = 1)) +
  coord_cartesian(clip = "off") +
  labs(x = "Trail Lengths in Miles, Log Scale", y = NULL, 
       title = "Trail Lenths Available by Region", 
       subtitle = "The Cascades have more of the Longer Trails", 
       caption = "@jim_gruman | Data: TidyX")

Let’s take a closer look at the features of each trail and the relationship with length:

hikes_complete <- hike_data %>%
    filter(length > 0.1) %>%
    mutate(hikeID = row_number(),
           length = log10(length),
           length = length %/% 0.2 * 0.2) %>%
    select(hikeID, length, features) %>%
    unnest(features) %>%
    count(length, features) %>%
    complete(length, features, fill = list(n = 0)) %>%
    group_by(length) %>%
    mutate(length_total = sum(n)) %>%
    ungroup()

hikes_complete
# A tibble: 240 x 4
   length features                  n length_total
    <dbl> <chr>                 <dbl>        <dbl>
 1   -0.8 Coast                     3          104
 2   -0.8 Dogs allowed on leash    18          104
 3   -0.8 Dogs not allowed          3          104
 4   -0.8 Established campsites     0          104
 5   -0.8 Fall foliage              8          104
 6   -0.8 Good for kids            29          104
 7   -0.8 Lakes                     4          104
 8   -0.8 Mountain views            7          104
 9   -0.8 Old growth                4          104
10   -0.8 Ridges/passes             2          104
# ... with 230 more rows
slopes <- hikes_complete %>%
    nest_by(features) %>%
    mutate(model = list(glm(cbind(n, length_total) ~ length, 
                            family = "binomial", 
                            data = data))) %>%
    summarize(broom::tidy(model)) %>%
    ungroup %>%
    filter(term == "length") %>%
    mutate(p.value = p.adjust(p.value)) %>%
    filter(p.value < 0.05) %>%
    arrange(estimate)
hikes_complete %>%
    mutate(length_percent = n / length_total) %>%
    inner_join(slopes) %>%
    mutate(features = fct_reorder(features, -estimate)) %>%
    ggplot(aes(length, length_percent)) +
    geom_smooth(method = "lm") +
    geom_point() +
    facet_wrap(~features) +
    scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
    labs(x = "Length of each trail (log10 scale)",
         y = "% of trails with each feature",
         title = "Which features of Washington hikes are associated with longer trails?",
         caption = "@jim_gruman | Data: TidyX")


sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggridges_0.5.3    htmltools_0.5.2   fontawesome_0.2.2 gt_0.3.1         
 [5] rvest_1.0.1       hrbrthemes_0.8.0  forcats_0.5.1     stringr_1.4.0    
 [9] dplyr_1.0.7       purrr_0.3.4       readr_2.0.1       tidyr_1.1.3      
[13] tibble_3.1.4      ggplot2_3.3.5     tidyverse_1.3.1   workflowr_1.6.2  

loaded via a namespace (and not attached):
  [1] readxl_1.3.1       backports_1.2.1    systemfonts_1.0.2 
  [4] workflows_0.2.3    plyr_1.8.6         selectr_0.4-2     
  [7] tidytuesdayR_1.0.1 splines_4.1.1      listenv_0.8.0     
 [10] usethis_2.0.1      digest_0.6.27      foreach_1.5.1     
 [13] yardstick_0.0.8    viridis_0.6.1      parsnip_0.1.7.900 
 [16] fansi_0.5.0        magrittr_2.0.1     checkmate_2.0.0   
 [19] tune_0.1.6         tzdb_0.1.2         recipes_0.1.16    
 [22] globals_0.14.0     modelr_0.1.8       gower_0.2.2       
 [25] extrafont_0.17     extrafontdb_1.0    hardhat_0.1.6     
 [28] rsample_0.1.0      dials_0.0.9.9000   colorspace_2.0-2  
 [31] textshaping_0.3.5  haven_2.4.3        xfun_0.25         
 [34] crayon_1.4.1       jsonlite_1.7.2     survival_3.2-11   
 [37] iterators_1.0.13   glue_1.4.2         gtable_0.3.0      
 [40] ipred_0.9-11       Rttf2pt1_1.3.9     future.apply_1.8.1
 [43] scales_1.1.1       infer_1.0.0        DBI_1.1.1         
 [46] Rcpp_1.0.7         viridisLite_0.4.0  GPfit_1.0-8       
 [49] lava_1.6.10        prodlim_2019.11.13 httr_1.4.2        
 [52] ellipsis_0.3.2     farver_2.1.0       pkgconfig_2.0.3   
 [55] nnet_7.3-16        sass_0.4.0         dbplyr_2.1.1      
 [58] utf8_1.2.2         here_1.0.1         labeling_0.4.2    
 [61] tidyselect_1.1.1   rlang_0.4.11       DiceDesign_1.9    
 [64] later_1.3.0        munsell_0.5.0      cellranger_1.1.0  
 [67] tools_4.1.1        cachem_1.0.6       cli_3.0.1         
 [70] generics_0.1.0     broom_0.7.9        evaluate_0.14     
 [73] fastmap_1.1.0      yaml_2.2.1         ragg_1.1.3        
 [76] knitr_1.33         fs_1.5.0           nlme_3.1-152      
 [79] future_1.22.1      whisker_0.4        xml2_1.3.2        
 [82] compiler_4.1.1     rstudioapi_0.13    curl_4.3.2        
 [85] reprex_2.0.1       lhs_1.1.1          bslib_0.3.0       
 [88] stringi_1.7.4      highr_0.9          gdtools_0.2.3     
 [91] lattice_0.20-44    Matrix_1.3-4       commonmark_1.7    
 [94] styler_1.5.1       conflicted_1.0.4   vctrs_0.3.8       
 [97] tidymodels_0.1.3   pillar_1.6.2       lifecycle_1.0.0   
[100] furrr_0.2.3        jquerylib_0.1.4    httpuv_1.6.2      
[103] R6_2.5.1           promises_1.2.0.1   gridExtra_2.3     
[106] parallelly_1.27.0  codetools_0.2-18   MASS_7.3-54       
[109] assertthat_0.2.1   rprojroot_2.0.2    withr_2.4.2       
[112] mgcv_1.8-36        parallel_4.1.1     hms_1.1.0         
[115] grid_4.1.1         rpart_4.1-15       timeDate_3043.102 
[118] class_7.3-19       rmarkdown_2.10     git2r_0.28.0      
[121] pROC_1.18.0        lubridate_1.7.10