Tidy Tuesday 27 Feb 2024: Leap Years

R
tidy tuesday
leap years
Authors

Myriam Scansetti

Antony Clark

Jon Minton

Nicoloas Christofidis

Brendan Clarke

Kennedy Owuso-Afriyie

Emu the cat

Published

February 28, 2024

Introduction

The latest TidyTuesday dataset was on births, deaths and other historical events that occurred in leap years, i.e. those years that include 29 February (such as 2024!). Further details are here.

Myriam led the session, and Antony provided additional code for performing text field analysis after the session.

Also, Emu the cat had the following contribution to make:

43e’/;£@@@@@@@@@@.1

The session

We started by loading some packages

# Option 1: tidytuesdayR package 
## install.packages("tidytuesdayR")
## install.packages("waldo")
## install.packages("tidytext")
## install.packages("textdata")
 
library(tidytuesdayR)
library(tidyverse)
library(waldo)
library(tidytext)
library(textdata)

We then had two ways of loading the data, in this case three datasets. As usual I’m switching to the url-based approach for the blog post

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

We noticed the births data include mention of at least one Pope. We wanted to explore more and less robust ways of finding popes in the births and deaths dataset

We could start by just looking for whether the word Pope is in the person field of births

str_detect(births$person, "Pope")
  [1]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[121] FALSE

We then used a little expression to make the query not case sensitive:

deaths %>% filter(str_detect(person, "(?i)Pope"))
# A tibble: 1 × 4
  year_death person        description year_birth
       <dbl> <chr>         <chr>            <dbl>
1        468 Pope Hilarius <NA>                NA

Another approach is to use ignore_case in the regex() function:

deaths %>% filter(str_detect(person, regex("pope", ignore_case = TRUE)))
# A tibble: 1 × 4
  year_death person        description year_birth
       <dbl> <chr>         <chr>            <dbl>
1        468 Pope Hilarius <NA>                NA

The only persons with pope in their name appear to be actual popes, not people who just happen to have the letters ‘pope’ in their surname.

Next we looked at number of events by year. We used two tidyverse approaches to producing this, one using group_by and summarise, the other using count.

number_events <- events %>% 
  group_by(year) %>% 
  summarise(n= n())
number_events
# A tibble: 29 × 2
    year     n
   <dbl> <int>
 1   888     1
 2  1504     1
 3  1644     1
 4  1704     1
 5  1712     1
 6  1720     1
 7  1768     1
 8  1796     1
 9  1892     1
10  1908     1
# ℹ 19 more rows
number_events_2 <- events %>% 
  count(year)
number_events_2
# A tibble: 29 × 2
    year     n
   <dbl> <int>
 1   888     1
 2  1504     1
 3  1644     1
 4  1704     1
 5  1712     1
 6  1720     1
 7  1768     1
 8  1796     1
 9  1892     1
10  1908     1
# ℹ 19 more rows

We then tried different comparator functions to see if they all agreed the contents were identical, with some mixed and confusing results:

waldo::compare(number_events, number_events_2)
✔ No differences

waldo says they are the same.

identical(number_events, number_events_2)
[1] FALSE

identical says they are not identical

setequal(number_events, number_events_2)
[1] TRUE

But setequal doesn’t find differences

all.equal(number_events, number_events_2)
[1] "Attributes: < Names: 1 string mismatch >"                                              
[2] "Attributes: < Length mismatch: comparison on first 2 components >"                     
[3] "Attributes: < Component \"class\": Lengths (3, 4) differ (string compare on first 3) >"
[4] "Attributes: < Component \"class\": 3 string mismatches >"                              
[5] "Attributes: < Component 2: Modes: numeric, externalptr >"                              
[6] "Attributes: < Component 2: Lengths: 29, 1 >"                                           
[7] "Attributes: < Component 2: target is numeric, current is externalptr >"

All equal reports a number of differences, related to the attributes (metadata) between the two objects being compared.

Curiouser and Curiouser…

Now let’s plot the number of events over time

number_events %>% 
  ggplot(aes(x = year, y = n))+
  geom_col()

We wanted to know if there was anyone who was both recorded as being born and dying in a leap year:

person_bd <- births %>% 
  inner_join(deaths, by = "person")

person_bd
# A tibble: 1 × 7
  year_birth.x person      description.x year_death.x year_death.y description.y
         <dbl> <chr>       <chr>                <dbl>        <dbl> <chr>        
1         1812 James Miln… Scottish-Aus…         1880         1880 Scottish-Aus…
# ℹ 1 more variable: year_birth.y <dbl>

One person (born in Scotland!)

We then looked text analysis, and in particular sentiment analysis of the content of the descriptio field:

births %>%
  unnest_tokens(word, description) %>%
  anti_join(get_stopwords()) %>%
  left_join(get_sentiments("afinn"))
events %>%
  unnest_tokens(word, event) %>%
  anti_join(get_stopwords()) %>%
  left_join(get_sentiments("afinn"))

Here’s the words in the afinn object with the highest (most positive) sentiment

get_sentiments("afinn") %>%
  arrange(desc(value))

And here’s an exploration of average sentiment by (leap)year based on the events description field:

events |>
  unnest_tokens(word, event) |>
  anti_join(get_stopwords()) |>
  right_join(get_sentiments("afinn")) |>
  group_by(year) |>
  summarise(mean_sentiment = mean(value)) |>
  ggplot(aes(x = year, y = mean_sentiment)) +
  geom_point() +
  geom_smooth()

Antony’s script

Load libraries

library(tidyverse)
library(lubridate)
library(countrycode)

some extra data sets re nationalities

demonym <- readr::read_csv("https://raw.githubusercontent.com/knowitall/chunkedextractor/master/src/main/resources/edu/knowitall/chunkedextractor/demonyms.csv",
                           col_names = c("demonym","geography"))

demonym$demonym <- tolower(demonym$demonym)
demonym$geography <- tolower(demonym$geography)
country <- tibble(country=countrycode::codelist$country.name.en)

Load data

# tuesdata <- tidytuesdayR::tt_load('2024-02-27')

# list2env(tuesdata,.GlobalEnv)

glimpse(events)
Rows: 37
Columns: 2
$ year  <dbl> 888, 1504, 1644, 1704, 1712, 1720, 1768, 1796, 1892, 1908, 1912,…
$ event <chr> "Odo, count of Paris, is crowned king of West Francia (France) b…
glimpse(births)
Rows: 121
Columns: 4
$ year_birth  <dbl> 1468, 1528, 1528, 1572, 1576, 1640, 1692, 1724, 1736, 1792…
$ person      <chr> "Pope Paul III", "Albert V", "Domingo Báñez", "Edward Ceci…
$ description <chr> NA, "Duke of Bavaria", "Spanish theologian", "1st Viscount…
$ year_death  <dbl> 1549, 1579, 1604, 1638, 1614, 1704, 1763, 1822, 1784, 1868…
glimpse(deaths)
Rows: 62
Columns: 4
$ year_death  <dbl> 468, 992, 1460, 1528, 1592, 1600, 1604, 1712, 1744, 1792, …
$ person      <chr> "Pope Hilarius", "Oswald of Worcester", "Albert III", "Pat…
$ description <chr> NA, "Anglo-Saxon archbishop and saint", "Duke of Bavaria-M…
$ year_birth  <dbl> NA, 925, 1401, 1504, 1536, 1529, 1530, 1653, 1683, 1728, 1…

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?

how many popes?

births %>% 
  mutate(is_pope = grepl("pope",tolower(paste(person,description)))) %>% 
  count(is_pope)
# A tibble: 2 × 2
  is_pope     n
  <lgl>   <int>
1 FALSE     120
2 TRUE        1

count births by century —-

getCenturyCorrected <- function(year) {
  if (year %% 100 == 0) {
    century <- year / 100
  } else {
    century <- ceiling(year / 100)
  }
  return(century)
}

getCenturyCorrected(1900)
[1] 19
getCenturyCorrected(1901)
[1] 20
births %>% 
  mutate(century=sapply(year_birth,getCenturyCorrected)) %>% 
  count(century)
# A tibble: 7 × 2
  century     n
    <dbl> <int>
1      15     1
2      16     4
3      17     2
4      18     3
5      19    11
6      20    99
7      21     1

do count() and summarise(n=n()) give identical dataframes? Not always —-

x <- births %>% count(year_birth)
y <- births %>% group_by(year_birth) %>% summarise(n=n())

identical(attributes(x), attributes(y))
[1] FALSE
names(x)==names(y)
[1] TRUE TRUE
identical(
  x,
  y
)
[1] FALSE

a rough stab (clearly flawed) at parsing nationality —-

births_nationality <-
  bind_rows(
    births %>%
      tidytext::unnest_tokens(word, description) %>%
      anti_join(tidytext::get_stopwords(), "word") %>%
      left_join(
        demonym,
        by = c(word = "geography"),
        relationship = "many-to-many"
      ) %>%
      left_join(demonym, by = "demonym"),
    
    births %>%
      tidytext::unnest_tokens(word, description) %>%
      anti_join(tidytext::get_stopwords(), "word") %>%
      left_join(demonym, c(word = "demonym")) %>%
      left_join(demonym, "geography", relationship = "many-to-many")
  )

births_nationality %>% count(geography) %>% arrange(-n)
# A tibble: 35 × 2
   geography         n
   <chr>         <int>
 1 <NA>            732
 2 united states   216
 3 australia        40
 4 england          39
 5 canada           24
 6 zealand          12
 7 spain            10
 8 wales             8
 9 france            6
10 turkey            6
# ℹ 25 more rows

Now a pretty wordcloud

events %>% 
  unnest_tokens(word, event) %>% 
  anti_join(get_stopwords(),"word") %>% 
  count(word) %>% 
  {wordcloud::wordcloud(words = .$word, 
            freq = .$n, min.freq = 1,
            max.words = 20, random.order = FALSE, rot.per = 0.35, 
            colors = RColorBrewer::brewer.pal(8, "Dark2"))}

A neutral word should have a sentiment score of 0, not NA. Let’s make that change…

afinn_sentiments <- get_sentiments('afinn')

events %>%
  unnest_tokens(word, event) %>%
  anti_join(get_stopwords(),"word") %>%
  left_join(afinn_sentiments,"word") %>%
  # filter(!is.na(value)) %>%
  replace_na(list(value=0)) %>%
  mutate(century = sapply(year,getCenturyCorrected)) %>%
  group_by(century) %>%
  summarise(mean_sentiment = mean(value)) %>%
  ggplot(aes(x=century,y=mean_sentiment))+geom_line()

Footnotes

  1. I don’t think even regex can help us with this one.↩︎