Leap Day

Happy Leap Day! This week’s data comes from the February 29 article on Wikipedia.

February 29 is a leap day (or “leap year day”), an intercalary date added periodically to create leap years in the Julian and Gregorian calendars.

One event that’s missing from Wikipedia’s list: R version 1.0 was released on February 29, 2000.

Which cohort of leap day births is most represented in Wikipedia’s data? Are any years surprisingly underrepresented compared to nearby years? What other patterns can you find in the data?

The Data

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

tuesdata <- tidytuesdayR::tt_load('2024-02-27')
## OR
tuesdata <- tidytuesdayR::tt_load(2024, week = 9)

events <- tuesdata$events
births <- tuesdata$births
deaths <- tuesdata$deaths

# Option 2: Read directly from GitHub

events <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-02-27/events.csv')
births <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-02-27/births.csv')
deaths <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-02-27/deaths.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.

Data Dictionary

events.csv

variable	class	description
year	integer	Year of the event.
event	character	A short, free-text description of the event.

births.csv

variable	class	description
year_birth	integer	Year in which this person was born.
person	character	The name of the person.
description	character	A short description of the person.
year_death	integer	Year in which this person died.

deaths.csv

variable	class	description
year_death	integer	Year in which this person died.
person	character	The name of the person.
description	character	A short description of the person.
year_birth	integer	Year in which this person was born.

Cleaning Script

library(tidyverse)
library(rlang)
library(rvest)
library(here)

working_dir <- here::here("data", "2024", "2024-02-27")

feb29 <- "https://en.wikipedia.org/wiki/February_29"

# Read the HTML once so we don't have to keep hitting it.
feb29_html <- rvest::read_html(feb29)

# Find the headers. We'll use these to figure out which bullets are "inside"
# each header, since nothing "contains" them to make it easy.
h2s <- feb29_html |> 
  rvest::html_elements("h2") |> 
  rvest::html_text2() |> 
  stringr::str_remove("\\[edit\\]")

# We'll get all bullets that are after each header. We can then subtract out
# later lists to figure out what's under a particular header.
bullets_after_headers <- purrr::map(
  h2s,
  \(this_header) {
    this_selector <- glue::glue("h2:contains('{this_header}') ~ ul > li")
    feb29_html |> 
      rvest::html_elements(this_selector) |> 
      rvest::html_text2() |> 
      # Remove footnotes.
      stringr::str_remove_all("\\[\\d+\\]")
  }
) |> 
  rlang::set_names(h2s)

# Subtract subsequent bullets from each set.
bullets_in_headers <- purrr::map2(
  bullets_after_headers[-length(h2s)],
  bullets_after_headers[-1],
  setdiff
)

# The three sets we care about (Events, Births, Deaths) each have their own
# format.
events <- tibble::tibble(events = bullets_in_headers[["Events"]]) |> 
  tidyr::separate_wider_regex(
    "events",
    patterns = c(
      year = "^\\d+",
      " – ",
      event = ".*"
    )
  )
births <- tibble::tibble(births = bullets_in_headers[["Births"]]) |> 
  tidyr::separate_wider_regex(
    "births",
    patterns = c(
      year_birth = "^\\d+",
      " – ",
      person = ".*"
    )
  ) |> 
  tidyr::separate_wider_regex(
    "person",
    patterns = c(
      person = "[^(]*",
      "\\(d\\. ",
      "(?:February 29, )*",
      year_death = "\\d+",
      "\\)\\.?"
    ),
    too_few = "align_start"
  ) |> 
  tidyr::separate_wider_regex(
    "person",
    patterns = c(
      person = "[^,]*",
      ", ",
      description = ".*"
    ),
    too_few = "align_start"
  )

deaths <- tibble::tibble(deaths = bullets_in_headers[["Deaths"]]) |> 
  tidyr::separate_wider_regex(
    "deaths",
    patterns = c(
      year_death = "^\\d+",
      " – ",
      person = ".*"
    )
  ) |> 
  tidyr::separate_wider_regex(
    "person",
    patterns = c(
      person = "[^(]*",
      "\\(b\\. ",
      "(?:February 29, )*",
      year_birth = "\\d+",
      "\\)\\.?"
    ),
    too_few = "align_start"
  ) |> 
  tidyr::separate_wider_regex(
    "person",
    patterns = c(
      person = "[^,]*",
      ", ",
      description = ".*"
    ),
    too_few = "align_start"
  )

readr::write_csv(
  events,
  fs::path(working_dir, "events.csv")
)
readr::write_csv(
  births,
  fs::path(working_dir, "births.csv")
)
readr::write_csv(
  deaths,
  fs::path(working_dir, "deaths.csv")
)