Exploring the lifespans of historical figures born on a Leap Day

A contribution to the 2024-02-27 #tidytuesday social data project

Photo by Nick Hillier

library(tidyverse)
library(skimr)
library(plotly)
library(here)

Background

Happy belated Leap Day! This week’s #tidytuesday is focused on significant historical events and people who were born or died on a Leap Day. The aim of this post is to contribute a couple data visualizations to this social data project. Specifically, I used plotly and Tableau to create my contributions.

data_births <- read_csv(
  here(
    "blog",
    "posts",
    "2024-02-27-tidytuesday-2024-02-27-leap-day",
    "births.csv"
  )
)

Let’s do a quick glimpse() and skim() of our data, just so we get an idea of what we’re working with here.

glimpse(data_births)

Rows: 121
Columns: 4
$ year_birth  <dbl> 1468, 1528, 1528, 1572, 1576, 1640, 1692, 1724, 1736, 1792, 1812, 1828, 1836, …
$ person      <chr> "Pope Paul III", "Albert V", "Domingo Báñez", "Edward Cecil", "Antonio Neri", …
$ description <chr> NA, "Duke of Bavaria", "Spanish theologian", "1st Viscount Wimbledon", "Floren…
$ year_death  <dbl> 1549, 1579, 1604, 1638, 1614, 1704, 1763, 1822, 1784, 1868, 1880, 1921, 1908, …

skim(data_births)

Data summary

Name	data_births
Number of rows	121
Number of columns	4
_______________________
Column type frequency:
character	2
numeric	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
person	0	1.00	6	29	0	121	0
description	1	0.99	12	95	0	107	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year_birth	0	1.00	1919.90	101.01	1468	1920	1944.0	1976	2004	▁▁▁▁▇
year_death	65	0.46	1933.61	126.53	1549	1920	1989.5	2013	2023	▁▁▁▁▇

Data description

This week’s data comes from the February 29th Wikipedia page. Three data sets are made available, one focused on significant events, as well as births and deaths of historical figures that occurred on a Leap Day. Given what’s available, I was interested in exploring the age and lifespan of the historical figures born on a Leap Day. Here’s the wrangling code I created to explore the data.

data_age <- data_births |>
  mutate(
    is_alive = ifelse(is.na(year_death), 1, 0),
    year_death = ifelse(is.na(year_death), 2024, year_death),
    age = year_death - year_birth
  ) |>
  arrange(desc(age)) |> 
  relocate(person, description, year_birth, year_death, age)

data_age$person <- factor(data_age$person, levels = data_age$person[order(data_age$year_birth)])

What are the lifespans of historical figures born on a leap day?

To explore this question, I decided to create a dumbbell chart. In the chart, the blue dots represent the person’s birth year. The black dot represents the year the person died. Absence of the black dot indicates a person is still alive, while the grey line represents the person’s lifespan. If you hover over the dots, a tool tip with information about each person is shown.

not_alive <- data_age |> filter(is_alive == 0)

plot_ly(
  data_age, 
  color = I("gray80"),
  text = ~paste(
    person, "<br>",
    "Age: ", age, "<br>",
    description 
  ),
  hoverinfo = "text"
) |>
  add_segments(x = ~year_birth, xend = ~year_death, y = ~person, yend = ~person, showlegend = FALSE) |>
  add_markers(x = ~year_birth, y = ~person, color = I("#0000FF"), name = "Birth year") |>
  add_markers(data = not_alive, x = ~year_death, y = ~person, color = I("black"), name = "Year passed") |>
  layout(
    title = list(
      text = "<b>Lifespans of historical figures born on a Leap Day</b>",
      xanchor = "center",
      yanchor = "top",
      font = list(family = "arial", size = 24)
    ),
    xaxis = list(
      title = "Year born | Year died"
    ),
    yaxis = list(
      title = ""
    )
  )

An attempt using Tableau

I also created a version of this visualization using Tableau. You can view my attempt here. I was required to make a few concessions with this attempt, as I was unable to have as much fine control of the plot elements as I would have liked. However, I’m happy with what turned out.