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Groundhog predictions

Happy Groundhog Day! This week we're exploring Groundhog Day Predictions from groundhog-day.com! See if you can find a better way to present the annual data than their table of predictions by year! For anyone not familiar with the Groundhog Day tradition, if the groundhog sees its shadow and goes back into its burrow, that is a prediction of six more weeks of winter. Otherwise spring will come early. We attempted to provide weather data to accompany this dataset, but so far we've been unsuccessful. Watch for a follow-up dataset in the future!

Note: "Oil Springs Ollie" (groundhog #55) has been succeeded by "Heaven's Wildlife Harvey" (groundhog #70).

The Data

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

tuesdata <- tidytuesdayR::tt_load('2024-01-30')
## OR
tuesdata <- tidytuesdayR::tt_load(2024, week = 5)

groundhogs <- tuesdata$groundhogs
predictions <- tuesdata$predictions

# Option 2: Read directly from GitHub

groundhogs <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-01-30/groundhogs.csv')
predictions <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-01-30/predictions.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

groundhogs.csv

variable class description
id integer A numeric id for this groundhog.
slug character The name of the groundhog, in simplified kebab case.
shortname character A short version of the name of the groundhog or groundhog substitute.
name character The full name of the groundhog or groundhog substitute.
city character The city in which the prediction takes place.
region character The state or province of the prediction.
country character The country of the prediction (USA or Canada).
latitude double The latitude of the city.
longitude double The longitude of the city.
source character A url with information about this groundhog.
current_prediction character A url with information about the most recent prediction.
is_groundhog logical A logical value indicating whether this predictor is a living groundhog.
type character A short description of the type of animal or other thing that is said to make the prediction.
active logical A logical value indicating whether this predictor is active (as of 2023).
description character A free-text description of the predictor.
image character A URL with an image of the predictor.
predictions_count integer The number of predictions available for this predictor.

predictions.csv

variable class description
id integer A numeric id for this groundhog.
year integer The year of the prediction.
shadow logical Whether the groundhog saw its shadow, and thus predicts 6 more weeks of winter.
details character Free text with more information about this prediction.

Cleaning Script

library(tidyverse)
library(here)
library(fs)
library(httr2)

working_dir <- here::here("data", "2024", "2024-01-30")

groundhogs_all <- httr2::request("https://groundhog-day.com/api/v1/groundhogs/") |> 
  httr2::req_perform() |> 
  httr2::resp_body_json() |> 
  _$groundhogs |> 
  tibble::tibble(data = _) |> 
  tidyr::unnest_wider(data)

groundhogs <- groundhogs_all |> 
  dplyr::select(
    -predictions,
    -successor, # Only 1 groundhog has a successor, we'll mention it in the post.
    -contact
  ) |> 
  janitor::clean_names() |> 
  dplyr::mutate(
    # At least one has a stray comma at the end
    coordinates = stringr::str_remove(coordinates, ",$")
  ) |> 
  tidyr::separate_wider_delim(
    coordinates,
    ",",
    names = c("latitude", "longitude")
  ) |> 
  dplyr::mutate(
    # Correct types
    latitude = as.double(latitude),
    longitude = as.double(longitude),
    is_groundhog = as.logical(is_groundhog),
    active = as.logical(active)
  )

predictions <- groundhogs_all |> 
  dplyr::select(id, predictions) |> 
  tidyr::unnest_longer(predictions) |> 
  tidyr::unnest_wider(predictions) |> 
  dplyr::mutate(shadow = as.logical(shadow))

readr::write_csv(
  groundhogs,
  fs::path(working_dir, "groundhogs.csv")
)
readr::write_csv(
  predictions,
  fs::path(working_dir, "predictions.csv")
)