readme.md

US State Names

Happy Colorado Day! The data this week comes from three Wikipedia articles: List of states and territories of the United States, List of demonyms for US states and territories, and List of state and territory name etymologies of the United States.

A number of past TidyTuesdays (such as 2018/2018-04-03, 2019/2019-01-29, 2020/2020-03-10, and 2022/2022-11-08 have state columns. Might joining in this state date offer any insights to those datasets?

The Data

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

tuesdata <- tidytuesdayR::tt_load('2023-08-01')
## OR
tuesdata <- tidytuesdayR::tt_load(2023, week = 31)

states <- tuesdata$states
state_name_etymology <- tuesdata$state_name_etymology

# Option 2: Read directly from GitHub

states <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-08-01/states.csv')
state_name_etymology <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-08-01/state_name_etymology.csv')

Data Dictionary

states.csv

variable	class	description
state	character	The name of the state. The Wikipedia article has footnotes pointing out that Kentucky, Massachusetts, Pennsylvania, and Virginia are officially "commonwealths" in their full official names, not states.
postal_abbreviation	character	The 2-letter postal abbreviation.
capital_city	character	The capital of the state.
largest_city	character	The largest city in the state as of 2020.
admission	date	The date when the state was admitted to the union, or when it ratified the US Constitution.
population_2020	integer	The population as of the 2020 census.
total_area_mi2	integer	The total area of the state in square miles.
total_area_km2	integer	The total area of the state in square kilometers.
land_area_mi2	integer	The area of the state that is on land in square miles.
land_area_km2	integer	The area of the state that is on land in square kilometers.
water_area_mi2	integer	The area of the state occupied by water in square miles.
water_area_km2	integer	The area of the state occupied by water in square kilometers.
n_representatives	integer	The number of representatives the state has in the US House of Representatives.
demonym	character	The official name of a resident of the state.

state_name_etymology.csv

variable	class	description
state	character	The name of the state.
date_named	date	The date on which the name (or a version of the name that led to the modern version) was first attested. Note: Colorado is shown as occurring on January 1, but only the year is known.
language	character	The language from which the state name is believed to have been derived. Note that some states have more than one entry, in more than one row!
words_in_original	character	The word or words in the original language that became the state name. This field has footnotes in the original Wikipedia article.
meaning	character	The meaning of the original words. This field has footnotes in the original Wikipedia article.

Cleaning Script

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

# Load basic state data from Wikipedia.
states <- read_html("https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States") |> 
  html_table(header = TRUE) |> 
  # Try out the new-ish use of _ for subsetting with the base pipe.
  _[[2]][-1,]
colnames(states) <- c(
  "state",
  "postal_abbreviation",
  "capital_city",
  "largest_city",
  "admission",
  "population_2020",
  "total_area_mi2",
  "total_area_km2",
  "land_area_mi2",
  "land_area_km2",
  "water_area_mi2",
  "water_area_km2",
  "n_representatives"
)
glimpse(states)

# Clean types.
states <- states |> 
  mutate(
    across(
      c(population_2020, contains("area"), n_representatives),
      ~ parse_number(.x) |> as.integer()
    ),
    admission = parse_date(admission, format = "%b %d, %Y"),
    state = str_remove(state, fixed("[B]"))
  )

state_demonyms <- read_html("https://en.wikipedia.org/wiki/List_of_demonyms_for_US_states_and_territories") |> 
  html_table() |> 
  _[[1]][-1,1:2]
colnames(state_demonyms) <- c("state", "demonym")
states <- states |> 
  left_join(state_demonyms, by = "state")

# Confirm that the join worked!

states |> 
  filter(is.na(demonym))

state_name_etymology <- read_html("https://en.wikipedia.org/wiki/List_of_state_and_territory_name_etymologies_of_the_United_States") |> 
  html_table() |> 
  _[[1]]
colnames(state_name_etymology) <- c(
  "state",
  "date_named",
  "language",
  "words_in_original",
  "meaning"
)
state_name_etymology <- state_name_etymology |>
  mutate(
    date_named = if_else(
      date_named == "1743",
      "January 1, 1743",
      date_named
    ) |> 
      parse_date(format = "%B %d, %Y"),
    # We remove footnotes, but will reference them in the dictionary.
    across(
      c(words_in_original, meaning),
      ~ str_remove_all(.x, "\\[\\d+\\]")
    )
  )

# Confirm that the tables match up.
states |> 
  full_join(state_name_etymology, by = "state") |> 
  filter(is.na(date_named))
states |> 
  full_join(state_name_etymology, by = "state") |> 
  filter(is.na(admission))

write_csv(
  states,
  here(
    "data",
    "2023",
    "2023-08-01",
    "states.csv"
  )
)
write_csv(
  state_name_etymology,
  here(
    "data",
    "2023",
    "2023-08-01",
    "state_name_etymology.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.