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International Mathematical Olympiad (IMO) Data

The data for this week comes from International Mathematical Olympiad (IMO).

The International Mathematical Olympiad (IMO) is the World Championship Mathematics Competition for High School students and is held annually in a different country. The first IMO was held in 1959 in Romania, with 7 countries participating. It has gradually expanded to over 100 countries from 5 continents. The competition consists of 6 problems and is held over two consecutive days with 3 problems each.

  1. How have country rankings shifted over time?
  2. What is the distribution of participation by gender? What's the distribution of top scores?
  3. How does team size or team composition (e.g., number of first-time participants vs. veterans) relate to overall country performance?

Thank you to Havisha Khurana for curating this week's dataset, and to Emi Tanaka for catching the bug in the original script!

The Data

# Option 1: tidytuesdayR package 
## install.packages("tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2024-09-24')
## OR
tuesdata <- tidytuesdayR::tt_load(2024, week = 39)

country_results_df <- tuesdata$country_results_df
individual_results_df <- tuesdata$individual_results_df
timeline_df <- tuesdata$timeline_df

# Option 2: Read directly from GitHub

country_results_df <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-09-24/country_results_df.csv')
individual_results_df <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-09-24/individual_results_df.csv')
timeline_df <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-09-24/timeline_df.csv')

How to Participate

  • Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
  • Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
  • Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.
  • Submit your own dataset!

Data Dictionary

country_results_df.csv

variable class description
year integer Year of IMO
country character Participating country
team_size_all integer Participating contestants
team_size_male integer Male contestants
team_size_female integer Female contestants
p1 integer Score on problem 1
p2 integer Score on problem 2
p3 integer Score on problem 3
p4 integer Score on problem 4
p5 integer Score on problem 5
p6 integer Score on problem 6
p7 integer Score on problem 7
awards_gold integer Number of gold medals
awards_silver integer Number of silver medals
awards_bronze integer Number of bronze medals
awards_honorable_mentions integer Number of honorable mentions
leader character Leader of country team
deputy_leader character Deputy leader of country team

individual_results_df.csv

variable class description
year integer Year of IMO
contestant character Participant's name
country character Participant's country
p1 integer Score on problem 1
p2 integer Score on problem 2
p3 integer Score on problem 3
p4 integer Score on problem 4
p5 integer Score on problem 5
p6 integer Score on problem 6
total integer Total score on all problems
individual_rank integer Individual rank
award character Award won

timeline_df.csv

variable class description
edition integer Edition of International Mathematical Olympiad (IMO)
year integer Year of IMO
country character Host country
city character Host city
countries integer Number of participating countries
all_contestant integer Number of participating contestants
male_contestant integer Number of participating male contestants
female_contestant integer Number of participating female contestants
start_date Date Start date of IMO
end_date Date End date of IMO

Cleaning Script

# Scraping IMO results data

library(tidyverse)
library(rvest)
library(janitor)
library(httr2)

timeline_df <- read_html("https://www.imo-official.org/organizers.aspx") %>%
  html_table() %>%
  .[[1]] %>%
  clean_names() %>%
  rename(
    "all_contestant" = contestants,
    "male_contestant" = contestants_2,
    "female_contestant" = contestants_3,
    "edition" = number
  ) %>%
  filter(edition != "#") %>%
  mutate(
    start_date = paste0(gsub("(.*)(-)(.*)", "\\1", date),year),
    end_date = paste0(gsub("(.*)(-)(.*)", "\\3", date),year),
    across(
      c(start_date, end_date),
      ~as.Date(.x, format = "%d.%m.%Y")
    ),
    across(
      c(edition, year, countries, all_contestant, male_contestant, female_contestant),
      as.integer
    )
  ) %>%
  select(-date) %>%
  # only keeping records till current year
  filter(year < 2025)

# circulate through country results link and rbind tables
scrape_country <- function(year) {
  paste0("https://www.imo-official.org/year_country_r.aspx?year=", year) %>%
    read_html() %>%
    html_table() %>%
    .[[1]] %>%
    clean_names() %>%
    filter(country != "Country") %>%
    mutate(year = year, .before = "country") 
}

country_results_df <- map_df(timeline_df$year, scrape_country) %>%
  select(
    year,
    country,
    team_size_all = team_size,
    team_size_male = team_size_2,
    team_size_female = team_size_3,
    starts_with("p"),
    awards_gold = awards,
    awards_silver = awards_2,
    awards_bronze = awards_3,
    awards_honorable_mentions = awards_4,
    leader,
    deputy_leader
  ) %>% 
  mutate(
    across(
      c(team_size_all:awards_honorable_mentions),
      as.integer
    )
  )


# circulate through individual results link and rbind tables
scrape_individual <- function(year) {
  # These can time out, so we'll use httr2 to retry.
  paste0("https://www.imo-official.org/year_individual_r.aspx?year=", year) %>%
    httr2::request() %>%
    httr2::req_retry(max_tries = 3) %>%
    httr2::req_perform() %>%
    httr2::resp_body_html() %>%
    html_table() %>%
    .[[1]] %>%
    clean_names() %>%
    mutate(year = year, .before = "contestant") 
}

individual_results_df <- map_df(timeline_df$year, scrape_individual) %>%
  select(
    year:p6, p7, total,
    individual_rank = number_rank,
    award
  ) %>%
  mutate(
    across(
      c(year, p1:individual_rank),
      as.integer
    )
  )