The Great DataTas plot-a-thon

Datatas is organising a Data Visualisation competition: The Great DataTas plot-a-thon We will be using Tidy Tuesday data from 2020-09-22 for our competition. We have chosen a non-scientific dataset for participants to create beautiful plots, so anyone can enter our competition.

Our rules for entry are:

  • Entries should have code and final figure available in a public GitHub repository (contact us if you need help with this).
  • Entries can use any programming language.
  • Entries must have a Creative Commons license because we will share them via our social media and GitHub repo.
  • Editing software is allowed but discouraged.
  • External supporting data is allowed and encouraged.
  • Entries must be submitted by the end of day of Sunday, November 21.
  • Provide your twitter handle.

The criteria for choosing the winner are:

  • Three independent judges from EchoView, BOM and UTAS/CLEX will be judging the entries.
  • Figures must be reproducible.
  • Scripts must be easy to read and understand.
  • Figures must convey main message clearly.
  • Scripts must be developed by person submitting the entry, no copying.
Picture of the Coveted Trophy

Event Timing

We will be having an info session on Wednesday, November 10th to explain all details of this competition. Join us in person at IMAS or online to find out how you can participate.

  • Competition starts at 4pm November 10th following the DataTas event
  • Entries are due midnight (11:59 pm) on November 21st, giving you 10 days to put together your entry. During those ten days, we encourage you to work with others and share ideas, but your final entry must be your own work.
  • The winners will be announced at our final event of the year, on Thursday, November 25th at 2pm in the IMAS Aurora Lecture Theatre. Only one person can win the DataTas trophy and be crowned the plot-a-thon champion!

About the Data (From The Tidy Tuesday Page)

Picture of the Himalayas from Wikipedia

Himalayan Climbing Expeditions

The data this week comes from The Himalayan Database.

The Himalayan Database is a compilation of records for all expeditions that have climbed in the Nepal Himalaya. The database is based on the expedition archives of Elizabeth Hawley, a longtime journalist based in Kathmandu, and it is supplemented by information gathered from books, alpine journals and correspondence with Himalayan climbers.

The data cover all expeditions from 1905 through Spring 2019 to more than 465 significant peaks in Nepal. Also included are expeditions to both sides of border peaks such as Everest, Cho Oyu, Makalu and Kangchenjunga as well as to some smaller border peaks. Data on expeditions to trekking peaks are included for early attempts, first ascents and major accidents.

h/t to Alex Cookson for sharing and cleaning this data!

This blog post by Alex Cookson explores the data in greater detail.

I don't want to underplay that there are some positives and some awful negatives for native Sherpa climbers. One-third of Everest deaths are Sherpa Climbers.

Also National Geographic has 5 Ways to help the Sherpas of Everest.

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('2020-09-22')
tuesdata <- tidytuesdayR::tt_load(2020, week = 39)

climbers <- tuesdata$climbers

# Or read in the data manually

members <- readr::read_csv('')
expeditions <- readr::read_csv('')
peaks <- readr::read_csv('')

For other languages, see the additional files in this repository.

Data Dictionary


peak_idcharacterUnique identifier for peak
peak_namecharacterCommon name of peak
peak_alternative_namecharacterAlternative name of peak (for example, the "Mount Everest" is "Sagarmatha" in Nepalese)
height_metresdoubleHeight of peak in metres
climbing_statuscharacterWhether the peak has been climbed
first_ascent_yeardoubleYear of first successful ascent, if applicable
first_ascent_countrycharacterCountry name(s) of expedition members part of the first ascent. Can have multiple values if members were from different countries. Country name is as of date of ascent (for example, "W Germany" for ascents before 1990).
first_ascent_expedition_idcharacterUnique identifier for expedition. Can be linked to expeditions or members tables.


expedition_idcharacterUnique identifier for expedition. Can be linked to peaks or members tables.
peak_idcharacterUnique identifier for peak. Can be linked to peaks table.
peak_namecharacterCommon name for peak
yeardoubleYear of expedition
seasoncharacterSeason of expedition (Spring, Summer, etc.)
basecamp_datedateDate of expedition arrival at basecamp
highpoint_datedateDate of expedition summiting the peak for the first time or, if peak wasn't reached, date of reaching its highpoint
termination_datedateDate the expedition was terminated
termination_reasoncharacterPrimary reason the expedition was terminated. There are two possibilities for a successful expeditions, depending on whether the main peak or a sub-peak was summitted.
highpoint_metresdoubleElevation highpoint of the expedition
membersdoubleNumber of expedition members. For expeditions in Nepal, this is usually the number of foreigners listed on the expedition permit. For expeditions in China, this is usually the number of non-hired members.
member_deathsdoubleNumber of expeditions members who died
hired_staffdoubleNumber of hired staff who went above basecamp
hired_staff_deathsdoubleNumber of hired staff who died
oxygen_usedlogicalWhether oxygen was used by at least one member of the expedition
trekking_agencycharacterName of the trekking agency


expedition_idcharacterUnique identifier for expedition. Can be linked to peaks or members tables.
member_idcharacterUnique identifier for the person. This is not consistent across expeditions, so you cannot use a single member_id to look up all expeditions a person was part of.
peak_idcharacterUnique identifier for peak. Can be linked to peaks table.
peak_namecharacterCommon name for peak
yeardoubleYear of expedition
seasoncharacterSeason of expedition (Spring, Summer, etc.)
sexcharacterSex of the person
agedoubleAge of the person. Depending on the best available data, this could be as of the summit date, the date of death, or the date of arrival at basecamp.
citizenshipcharacterCitizenship of the person
expedition_rolecharacterRole of the person on the expedition
hiredlogicalWhether the person was hired by the expedition
highpoint_metresdoubleElevation highpoint of the person
successlogicalWhether the person was successful in summitting a main peak or sub-peak, depending on the goal of expedition
solologicalWhether the person attempted a solo ascent
oxygen_usedlogicalWhether the person used oxygen
diedlogicalWhether the person died
death_causecharacterPrimary cause of death
death_height_metresdoubleHeight at which the person died
injuredlogicalWhether the person was injured
injury_typecharacterPrimary cause of injury
injury_height_metresdoubleHeight at which the injury occurred

Cleaning Script (R Only, see files in repo for Python and R.)

# Libraries

# Peaks
peaks <- read_csv("./himalayan-expeditions/raw/peaks.csv") %>%
    peak_id = PEAKID,
    peak_name = PKNAME,
    peak_alternative_name = PKNAME2,
    height_metres = HEIGHTM,
    climbing_status = PSTATUS,
    first_ascent_year = PYEAR,
    first_ascent_country = PCOUNTRY,
    first_ascent_expedition_id = PEXPID
  ) %>%
    climbing_status = case_when(
      climbing_status == 0 ~ "Unknown",
      climbing_status == 1 ~ "Unclimbed",
      climbing_status == 2 ~ "Climbed"

# Create small dataframe of peak names to join to other dataframes
peak_names <- peaks %>%
  select(peak_id, peak_name)

# Expeditions
expeditions <- read_csv("./himalayan-expeditions/raw/exped.csv") %>%
  left_join(peak_names, by = c("PEAKID" = "peak_id")) %>%
    expedition_id = EXPID,
    peak_id = PEAKID,
    year = YEAR,
    season = SEASON,
    basecamp_date = BCDATE,
    highpoint_date = SMTDATE,
    termination_date = TERMDATE,
    termination_reason = TERMREASON,
    # Highpoint of 0 is most likely missing value
    highpoint_metres = ifelse(HIGHPOINT == 0, NA, HIGHPOINT),
    members = TOTMEMBERS,
    member_deaths = MDEATHS,
    hired_staff = TOTHIRED,
    hired_staff_deaths = HDEATHS,
    oxygen_used = O2USED,
    trekking_agency = AGENCY
  ) %>%
    termination_reason = case_when(
      termination_reason == 0 ~ "Unknown",
      termination_reason == 1 ~ "Success (main peak)",
      termination_reason == 2 ~ "Success (subpeak)",
      termination_reason == 3 ~ "Success (claimed)",
      termination_reason == 4 ~ "Bad weather (storms, high winds)",
      termination_reason == 5 ~ "Bad conditions (deep snow, avalanching, falling ice, or rock)",
      termination_reason == 6 ~ "Accident (death or serious injury)",
      termination_reason == 7 ~ "Illness, AMS, exhaustion, or frostbite",
      termination_reason == 8 ~ "Lack (or loss) of supplies or equipment",
      termination_reason == 9 ~ "Lack of time",
      termination_reason == 10 ~ "Route technically too difficult, lack of experience, strength, or motivation",
      termination_reason == 11 ~ "Did not reach base camp",
      termination_reason == 12 ~ "Did not attempt climb",
      termination_reason == 13 ~ "Attempt rumoured",
      termination_reason == 14 ~ "Other"
    season = case_when(
      season == 0 ~ "Unknown",
      season == 1 ~ "Spring",
      season == 2 ~ "Summer",
      season == 3 ~ "Autumn",
      season == 4 ~ "Winter"

members <-
  read_csv("./himalayan-expeditions/raw/members.csv", guess_max = 100000) %>%
  left_join(peak_names, by = c("PEAKID" = "peak_id")) %>%
    expedition_id = EXPID,
    member_id = paste(EXPID, MEMBID, sep = "-"),
    peak_id = PEAKID,
    year = MYEAR,
    season = MSEASON,
    sex = SEX,
    age = CALCAGE,
    citizenship = CITIZEN,
    expedition_role = STATUS,
    hired = HIRED,
    # Highpoint of 0 is most likely missing value
    highpoint_metres = ifelse(MPERHIGHPT == 0, NA, MPERHIGHPT),
    success = MSUCCESS,
    solo = MSOLO,
    oxygen_used = MO2USED,
    died = DEATH,
    death_cause = DEATHTYPE,
    # Height of 0 is most likely missing value
    death_height_metres = ifelse(DEATHHGTM == 0, NA, DEATHHGTM),
    injured = INJURY,
    injury_type = INJURYTYPE,
    # Height of 0 is most likely missing value
    injury_height_metres = ifelse(INJURYHGTM == 0, NA, INJURYHGTM)
  ) %>%
    season = case_when(
      season == 0 ~ "Unknown",
      season == 1 ~ "Spring",
      season == 2 ~ "Summer",
      season == 3 ~ "Autumn",
      season == 4 ~ "Winter"
    age = ifelse(age == 0, NA, age),
    death_cause = case_when(
      death_cause == 0 ~ "Unspecified",
      death_cause == 1 ~ "AMS",
      death_cause == 2 ~ "Exhaustion",
      death_cause == 3 ~ "Exposure / frostbite",
      death_cause == 4 ~ "Fall",
      death_cause == 5 ~ "Crevasse",
      death_cause == 6 ~ "Icefall collapse",
      death_cause == 7 ~ "Avalanche",
      death_cause == 8 ~ "Falling rock / ice",
      death_cause == 9 ~ "Disappearance (unexplained)",
      death_cause == 10 ~ "Illness (non-AMS)",
      death_cause == 11 ~ "Other",
      death_cause == 12 ~ "Unknown"
    injury_type = case_when(
      injury_type == 0 ~ "Unspecified",
      injury_type == 1 ~ "AMS",
      injury_type == 2 ~ "Exhaustion",
      injury_type == 3 ~ "Exposure / frostbite",
      injury_type == 4 ~ "Fall",
      injury_type == 5 ~ "Crevasse",
      injury_type == 6 ~ "Icefall collapse",
      injury_type == 7 ~ "Avalanche",
      injury_type == 8 ~ "Falling rock / ice",
      injury_type == 9 ~ "Disappearance (unexplained)",
      injury_type == 10 ~ "Illness (non-AMS)",
      injury_type == 11 ~ "Other",
      injury_type == 12 ~ "Unknown"
    death_cause = ifelse(died, death_cause, NA_character_),
    death_height_metres = ifelse(died, death_height_metres, NA),
    injury_type = ifelse(injured, injury_type, NA_character_),
    injury_height_metres = ifelse(injured, injury_height_metres, NA)

### Write to CSV
write_csv(expeditions, "./himalayan-expeditions/expeditions.csv")
write_csv(members, "./himalayan-expeditions/members.csv")
write_csv(peaks, "./himalayan-expeditions/peaks.csv")

GitHub - datatas/The-Great-DataTas-plot-a-thon at
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