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.
  Optional:
• 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.

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)

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('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-22/members.csv')
expeditions <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-22/expeditions.csv')
peaks <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-22/peaks.csv')

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

Data Dictionary

peaks.csv

expeditions.csv

members.csv

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

# Libraries
library(tidyverse)
library(janitor)


# Peaks
peaks <- read_csv("./himalayan-expeditions/raw/peaks.csv") %>%
  transmute(
    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
  ) %>%
  mutate(
    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")) %>%
  transmute(
    expedition_id = EXPID,
    peak_id = PEAKID,
    peak_name,
    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
  ) %>%
  mutate(
    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")) %>%
  transmute(
    expedition_id = EXPID,
    member_id = paste(EXPID, MEMBID, sep = "-"),
    peak_id = PEAKID,
    peak_name,
    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)
  ) %>%
  mutate(
    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 pythonawesome.com

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