Background

# install.packages(c("tidyverse", "httr", "XML"))
library(tidyverse)
  # `tidyverse` for data cleaning, usability, and output
library(httr)
library(XML)
  # `httr` and `XML` for data import

This is all drawn from the Wikipedia article on the Auckland Volcanic Field. I’m using this as practice in extracting data from the web, making it pretty and presentable, all while having a semi-practical use to import geospatial specific points for all of the volcanoes. (Anti-skill loss project my beloved <3).

# Import data from Wikipedia
# From user:schnee response on https://stackoverflow.com/questions/7407735/importing-wikipedia-tables-in-r

url <- "https://en.m.wikipedia.org/w/index.php?title=Auckland_volcanic_field&oldid=1146734511"
  # This is a permanent link to the exact data I used (accessed on 2023-07-17), use the following at your own risk:
  # url <- "https://en.m.wikipedia.org/wiki/Auckland_volcanic_field"

r <- GET(url)

doc <- readHTMLTable(
  doc=content(r, "text"), header = T)

Now that we have imported the data, let’s clean it up a little bit.

volcanoes <- 
  tibble(doc[[2]]) %>% 
  
  # Cleaning headers -----------------------------------------------------------
  rename(
    Volcanoes = `Volcanoes\n`,
    Age = `Age (thousand years)[38]`,
    Height = `Height\n`,
    Location = `Location (Coordinates)\n`,
    Refs = `Refs\n`,
    Images = `Images\n`
  ) %>% 
  
  # Feel free to keep the Refs & Images, I don't find them useful --------------
  select(!c(Refs, Images)) %>% 
  
  # Cleaning Height data -------------------------------------------------------
  separate(col = Height, into = c("Height (m)", NA, "Height (ft)", NA, "Quarried")) %>% 
  mutate(
    `Height (m)` = as.integer(`Height (m)`),
    `Height (ft)` = as.integer(`Height (ft)`),
    Quarried = ifelse(Quarried == "quarried", "T", "F"),
    Quarried = replace_na(Quarried, "F"),
    Quarried = as.logical(Quarried)
      # I don't know if multiple mutates in a row is kosher, but it works for me
  ) %>% 
  
  # Cleaning coordinates, only need one of these after all ---------------------
  separate(col = Location, into = c(NA, NA, "Location"), sep = "/") %>% 
    
  # Cleaning age ---------------------------------------------------------------
  separate(col = Age, into = c("Age", "Uncertainty (±)"), sep = "±")
  
  # Manually update the values bc it's simpler than finding a particular function to do it
  volcanoes[53,2] <- "30.55"
  volcanoes[53,3] <- "0.45"
  volcanoes[3,2] <- "130"
  volcanoes[27,2] <- "45"
  volcanoes[31,2] <- "130"
  volcanoes[33,2] <- "0.62"
  volcanoes[47,2] <- "28.0"
    # Yes, this is horribly messy and gross. How'd you know?

  volcanoes <- volcanoes %>% 
  mutate(
    Age = as.numeric(Age),
    Age = Age * 1000,
    Age = as.integer(Age),
    `Uncertainty (±)` = as.numeric(`Uncertainty (±)`),
    `Uncertainty (±)` = `Uncertainty (±)` * 1000,
    `Uncertainty (±)` = as.integer(`Uncertainty (±)`)
  )
  
  # That one name that wouldn't parse perfectly
  volcanoes[52,1] <- "Whakamuhu / Saint Heliers / Glover Park"

# Data output for however you want it ------------------------------------------
write_csv(volcanoes, "volcanoes.csv")
  
# Print dataset ================================================================

knitr::kable(volcanoes)
Volcanoes Age Uncertainty (±) Height (m) Height (ft) Quarried Location
Albert Park Volcano 145000 4000 NA NA FALSE -36.8486; 174.7673
Ash Hill 31800 400 30 98 FALSE -37.002754; 174.867545
Boggust Park Crater 130000 NA 14 46 FALSE -36.955413; 174.813552
Cemetery Crater NA NA 33 108 FALSE -36.989828; 174.841082
Crater Hill 30400 800 NA NA FALSE -36.986546; 174.827135
Grafton Volcano 106500 NA 82 269 FALSE -36.858440; 174.763624
Hampton Park 57000 32000 43 141 FALSE -36.950925; 174.89544
Kohuora 33700 2400 37 121 FALSE -36.97873; 174.842691
Māngere Lagoon 59500 NA 20 66 FALSE -36.95702; 174.77763
Matanginui / Green Mount 19600 6600 78 256 FALSE -36.939911; 174.898267
Matukutureia / McLaughlins Mountain 48200 6400 73 240 FALSE -37.013511; 174.845974
Maungakiekie / One Tree Hill 67000 12000 182 597 FALSE -36.90000; 174.78306
Maungarahiri / Little Rangitoto 24600 600 75 246 FALSE -36.875407; 174.809636
Maungarei / Mount Wellington 10000 1000 135 443 FALSE -36.89306; 174.846556
Maungataketake / Elletts Mountain 88900 4800 76 249 FALSE -36.994635; 174.747548
Maungauika / North Head 87500 15200 50 160 FALSE -36.827751; 174.81205
Maungawhau / Mount Eden 28000 600 196 643 FALSE -36.877; 174.764
Motukorea / Browns Island 24400 600 68 223 FALSE -36.8306; 174.8948
Mount Robertson / Sturges Park 24300 800 78 256 FALSE -36.948477; 174.841726
Ōhinerau / Mount Hobson 34200 1800 143 469 FALSE -36.877814; 174.786156
Ohuiarangi / Pigeon Mountain 23400 800 55 180 FALSE -36.888846; 174.903116
Ōrākei Basin 126000 6000 54 177 FALSE -36.867124; 174.81308
Ōtāhuhu / Mount Richmond 30200 4200 50 160 FALSE -36.932562; 174.839451
Ōtuataua 24200 1800 64 210 FALSE -36.98611; 174.75417
Ōwairaka / Te Ahi-kā-a-Rakataura / Mount Albert 119200 5600 135 443 FALSE -36.890475; 174.720097
Puhinui Craters NA NA 24 79 FALSE -37.01465; 174.83296
Pukaki Lagoon 45000 NA 37 121 FALSE -36.982998; 174.810226
Pukeiti 23700 NA 30 98 FALSE -36.983756; 174.757183
Pukekawa / Auckland Domain 106000 8000 77 253 FALSE -36.859158; 174.775808
Pukewīwī / Puketāpapa / Mount Roskill 105300 6200 110 360 FALSE -36.912286; 174.737371
Pukewairiki 130000 NA 35 115 FALSE -36.944078; 174.865887
Pupuke 193200 5600 34 112 FALSE -36.780115; 174.766184
Rangitoto Island 620 NA 260 850 FALSE -36.786742; 174.860115
Rarotonga / Mount Smart 20100 200 87 285 TRUE -36.91833; 174.81250
Styaks Swamp 19100 NA 16 52 FALSE -36.936138; 174.900155
Takaroro / Mount Cambria 42300 22000 30 98 TRUE -36.824444; 174.801933
Takarunga / Mount Victoria 34800 4000 87 285 FALSE -36.8266; 174.7990
Taurere / Taylors Hill 30200 200 56 184 FALSE -36.864223; 174.869943
Te Apunga-o-Tainui / McLennan Hills 41300 2400 45 148 TRUE -36.929208; 174.846468
Te Hopua-a-Rangi / Gloucester Park 31000 NA 12 39 FALSE -36.9295; 174.784734
Te Kopua Kai-a-Hiku / Panmure Basin 25200 1800 35 115 FALSE -36.90495; 174.849343
Te Kopua-o-Matakamokamo / Tank Farm / Tuff Crater 181000 2000 46 151 FALSE -36.8020; 174.7533
Onepoto 187600 NA 46 151 FALSE -36.80818; 174.75085
Te Kōpuke / Tītīkōpuke / Mount St John 75300 3400 126 413 FALSE -36.883431; 174.780196
Te Motu-a-Hiaroa / Puketutu 29800 4400 65 213 FALSE -36.965186; 174.747248
Te Pane-o-Mataaho / Māngere Mountain 59000 20000 106 348 FALSE -36.9496; 174.7831
Te Pou Hawaiki 28000 NA 95 312 TRUE -36.88247; 174.766726
Te Puke ō Tara / Otara Hill 56500 NA 89 292 TRUE -36.947105; 174.898363
Te Tātua-a-Riukiuta / Three Kings 31000 1800 133 436 FALSE -36.902926; 174.754651
Te Tauoma / Purchas Hill 10900 200 50 160 TRUE -36.887138; 174.847476
Waitomokia / Mt Gabriel 20300 200 22 72 TRUE -36.976981; 174.770336
Whakamuhu / Saint Heliers / Glover Park 161000 36000 65 213 FALSE -36.846911; 174.867662
Wiri Mountain / Matukutūruru 30550 450 80 260 TRUE -37.007334; 174.858441

Now what?

Well, isn’t that the question. For me, I created this because I plan on visiting all 53(!) historic volcanoes in Auckland. It seems fun and it seems like a great way to explore the city. I went through the hassle of manually making this into a Google Maps list, but it’s deeply inaccurate (I attached each point to a nearby place rather than its coordinates) so I used the code below in Volcanoes to visit generator to add it to a Google My Maps. However, the points within the wikipedia table seem a little inaccurate… I will report back from my adventures with any important changes.

Feel free to play with this, if you want a neat small dataset or some practice in R by trying to reproduce this.

Some fun stats (now easy to do with the data already in R):

Number of volcanoes Total Height (m) Total Height (ft) Mean Age (thousand years) Number of Quarried sites
53 3669 12016 60 8

Anyways. Have fun, enjoy the data! I might leave an update on any eventual updates, or perhaps a little table that is updated with info on the sites I have visited so far.

Version 1.1: Added volcano tracker.

Volcano Tracker

# A previous Alice did not have a better way of recording which volcanoes she had visited yet
volcanoes <- read_csv("volcanoes.csv")
visited <- tibble(
  Volcanoes = c("Maungauika / North Head", "Takarunga / Mount Victoria", "Albert Park Volcano", "Maungakiekie / One Tree Hill", "Te Kōpuke / Tītīkōpuke / Mount St John", "Ōhinerau / Mount Hobson", "Ohuiarangi / Pigeon Mountain", "Te Kopua-o-Matakamokamo / Tank Farm / Tuff Crater", "Onepoto", "Maungarei / Mount Wellington", "Taurere / Taylors Hill", "Whakamuhu / Saint Heliers / Glover Park", "Maungawhau / Mount Eden", "Takaroro / Mount Cambria", "Te Pou Hawaiki", "Te Tātua-a-Riukiuta / Three Kings", "Te Hopua-a-Rangi / Gloucester Park", "Te Pane-o-Mataaho / Māngere Mountain", "Māngere Lagoon", "Ōrākei Basin", "Maungarahiri / Little Rangitoto", "Pukekawa / Auckland Domain", "Grafton Volcano", "Ash Hill", "Wiri Mountain / Matukutūruru", "Matukutureia / McLaughlins Mountain", "Puhinui Craters", "Pukewīwī / Puketāpapa / Mount Roskill", "Ōwairaka / Te Ahi-kā-a-Rakataura / Mount Albert"),
  Visited = T, # Redundant column for future filters or whatever idk
  `Date visited` = c("2023-07-11", "2023-07-11", "2023-07-17", "2023-07-22", "2023-07-22", "2023-07-22", "2023-07-30", "2023-08-03", "2023-08-03", "2023-08-27", "2023-08-27", "2023-08-27", "2023-09-01", "2023-09-17", "2023-09-27", "2023-10-13", "2023-10-14", "2023-10-14", "2023-10-14", "2023-11-04", "2023-11-04", "2023-10-26", "2023-08-05", "2023-11-06", "2023-11-06", "2023-11-06", "2023-11-06", "2023-11-13", "2023-11-13")
)

bigvisited <- inner_join(volcanoes, visited) %>% 
  select(Volcanoes, `Date visited`, Age, `Height (ft)`, Location) %>% 
  mutate(
    `Date visited` = as.Date(`Date visited`)
  )

summaryvisited <- inner_join(volcanoes, visited) %>% 
  summarize(
    Location = paste0("n = ", n(), " / ", nrow(volcanoes), " (", round((n() / nrow(volcanoes)) * 100), "%)"),
    `Height (ft)` = sum(`Height (ft)`, na.rm = T),
    Age = as.integer(mean(Age, na.rm = T)),
    Volcanoes = "Mean Age / Total Height"
  )

knitr::kable(
  bind_rows(bigvisited, summaryvisited) %>% 
    arrange(`Date visited`, Volcanoes)
)
Volcanoes Date visited Age Height (ft) Location
Maungauika / North Head 2023-07-11 87500 160 -36.827751; 174.81205
Takarunga / Mount Victoria 2023-07-11 34800 285 -36.8266; 174.7990
Albert Park Volcano 2023-07-17 145000 NA -36.8486; 174.7673
Maungakiekie / One Tree Hill 2023-07-22 67000 597 -36.90000; 174.78306
Te Kōpuke / Tītīkōpuke / Mount St John 2023-07-22 75300 413 -36.883431; 174.780196
Ōhinerau / Mount Hobson 2023-07-22 34200 469 -36.877814; 174.786156
Ohuiarangi / Pigeon Mountain 2023-07-30 23400 180 -36.888846; 174.903116
Onepoto 2023-08-03 187600 151 -36.80818; 174.75085
Te Kopua-o-Matakamokamo / Tank Farm / Tuff Crater 2023-08-03 181000 151 -36.8020; 174.7533
Grafton Volcano 2023-08-05 106500 269 -36.858440; 174.763624
Maungarei / Mount Wellington 2023-08-27 10000 443 -36.89306; 174.846556
Taurere / Taylors Hill 2023-08-27 30200 184 -36.864223; 174.869943
Whakamuhu / Saint Heliers / Glover Park 2023-08-27 161000 213 -36.846911; 174.867662
Maungawhau / Mount Eden 2023-09-01 28000 643 -36.877; 174.764
Takaroro / Mount Cambria 2023-09-17 42300 98 -36.824444; 174.801933
Te Pou Hawaiki 2023-09-27 28000 312 -36.88247; 174.766726
Te Tātua-a-Riukiuta / Three Kings 2023-10-13 31000 436 -36.902926; 174.754651
Māngere Lagoon 2023-10-14 59500 66 -36.95702; 174.77763
Te Hopua-a-Rangi / Gloucester Park 2023-10-14 31000 39 -36.9295; 174.784734
Te Pane-o-Mataaho / Māngere Mountain 2023-10-14 59000 348 -36.9496; 174.7831
Pukekawa / Auckland Domain 2023-10-26 106000 253 -36.859158; 174.775808
Maungarahiri / Little Rangitoto 2023-11-04 24600 246 -36.875407; 174.809636
Ōrākei Basin 2023-11-04 126000 177 -36.867124; 174.81308
Ash Hill 2023-11-06 31800 98 -37.002754; 174.867545
Matukutureia / McLaughlins Mountain 2023-11-06 48200 240 -37.013511; 174.845974
Puhinui Craters 2023-11-06 NA 79 -37.01465; 174.83296
Wiri Mountain / Matukutūruru 2023-11-06 30550 260 -37.007334; 174.858441
Pukewīwī / Puketāpapa / Mount Roskill 2023-11-13 105300 360 -36.912286; 174.737371
Ōwairaka / Te Ahi-kā-a-Rakataura / Mount Albert 2023-11-13 119200 443 -36.890475; 174.720097
Mean Age / Total Height NA 71926 7613 n = 29 / 53 (55%)

Tracker last updated 2023-11-19.

Version 1.3 added to visit csv generator for maps.

Volcanoes to visit generator

You know how I mentioned wanting to have a nice way to export the location of the volcanoes I still had to visit so I’d be able to figure out where to go next a bit easier? Now is the time!

volcanoes <- volcanoes %>% 
  mutate(
    Lat = str_split_i(Location, ";", 1),
    Long = str_split_i(Location, ";", 2)
  )

left_join(volcanoes, visited) %>% 
  filter(is.na(Visited)) %>% 
  write_csv(., "tovisit.csv")

left_join(volcanoes, visited) %>% 
  write_csv(., "visited.csv")

And, now that we have that with Lat and Long specified in columns, we can import it into our mapping software of choice. A neat thing I found is that you can re-import specific layers, which is nice once a couple of volcanoes are ticked off the list.

Version 1.4 added a leaflet map of the volcanoes to the site.

Map

library(sf)
library(leaflet)

volcanoes <- read_csv("visited.csv") %>% 
  mutate(
    Visited = ifelse(is.na(Visited), F, Visited)
  )

cooords <- volcanoes %>% 
  select(Long, Lat) %>% 
  as.matrix()

coords_rows <- vector(mode = "list", length = nrow(cooords))
for (rows in 1:nrow(cooords)) {
  coords_rows[rows] <- list(cooords[rows, ])
}

geovolcanoes <- map_vec(coords_rows, \(x){st_sfc(st_point(x), crs = 4326)}) %>% cbind(volcanoes, .) %>% st_as_sf()

icons <- awesomeIcons(
  library = "ion",
  icon = "",
  markerColor = sapply(volcanoes$Visited, \(x){if(x == T){"darkblue"}else{"lightgray"}})
)

leaflet(data = geovolcanoes) %>% 
  addTiles(urlTemplate = "https://cartodb-basemaps-{s}.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png",
           attribution = "Map tiles by Carto, under CC BY 3.0. Data by OpenStreetMap, under ODbL.") %>% 
  addAwesomeMarkers(~Long, ~Lat, icon = icons, label = ~Volcanoes) %>% 
  addLegend(
    position = "bottomright",
    colors = c("darkblue", "lightgray"),
    labels = c("Visited", "Unvisited")
  )

Code availability

The source code for this project is available at https://github.com/legallyahc/volcanic, with all work done right in this RMarkdown file (dataset.rmd)!