Long Beach Animal Shelter

This week we’re exploring the Long Beach Animal Shelter Data!

The dataset comes from the City of Long Beach Animal Care Services via the {animalshelter} R package.

This dataset comprises of the intake and outcome record from Long Beach Animal Shelter.

• How has the number of pet adoptions changed over the years?
• Which type of pets are adopted most often?

Thank you to Lydia Gibson for curating this week’s dataset.

The Data

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

tuesdata <- tidytuesdayR::tt_load('2025-03-04')
## OR
tuesdata <- tidytuesdayR::tt_load(2025, week = 9)

longbeach <- tuesdata$longbeach

# Option 2: Read directly from GitHub

longbeach <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-03-04/longbeach.csv')

# Using Python
# Option 1: pydytuesday python library
## pip install pydytuesday

import pydytuesday

# Download files from the week, which you can then read in locally
pydytuesday.get_date('2025-03-04')

# Option 2: Read directly from GitHub and assign to an object

longbeach = pandas.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-03-04/longbeach.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 Quarto report, a shiny app, or some other piece of data-science-related output, using R, Python, or another programming language.
• Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.
• Submit your own dataset!

PydyTuesday: A Posit collaboration with TidyTuesday

• Exploring the TidyTuesday data in Python? Posit has some extra resources for you! Have you tried making a Quarto dashboard? Find videos and other resources in Posit’s PydyTuesday repo.
• Share your work with the world using the hashtags #TidyTuesday and #PydyTuesday so that Posit has the chance to highlight your work, too!
• Deploy or share your work however you want! If you’d like a super easy way to publish your work, give Connect Cloud a try.

Data Dictionary

longbeach.csv

variable	class	description
animal_id	character	Unique identification for each animal.
animal_name	character	Name of the Animal (Blank value means name not known). Animals with “*” are given by shelter staff.
animal_type	factor	Species name of the animal.
primary_color	factor	The predominant color of the animal.
secondary_color	factor	Additional coloring, less predominant than the primary color.
sex	factor	Altered Sex of the animal.
dob	date	Date of Birth (if blank, DOB unknown).
intake_date	date	Date on which Animal was brought to the shelter .
intake_condition	factor	Condition of animal at intake.
intake_type	factor	The reason for intake such as stray capture, wildlife captures, adopted but returned, owner surrendered etc.
intake_subtype	factor	The method or secondary manner in which the animal was admitted to the shelter.
reason_for_intake	factor	The reason an owner surrendered their animal.
outcome_date	date	Exit or Outcome date such as date of adoption or date animal died.
crossing	character	Intersection/Cross street of intake or capture.
jurisdiction	factor	Geographical jurisdiction of where an animal originated.
outcome_type	factor	Outcome associated with animal - adopted, died, euthanized etc.
outcome_subtype	factor	Secondary manner in which the animal left the shelter, usually used to identify which program, group, or other data useful in measuring program efficiency.
latitude	double	The latitude of the crossing.
longitude	double	The longitude of the crossing.
outcome_is_dead	logical	Whether animal is dead at outcome.
was_outcome_alive	logical	Whether animal was alive at outcome.
geopoint	character	Latitude and longitude of crossing.

Cleaning Script

# Clean data provided by {animalshelter} R package (https://emilhvitfeldt.github.io/animalshelter/). No cleaning was necessary.
# install.packages("devtools")
# devtools::install_github("EmilHvitfeldt/animalshelter")
library(dplyr)
library(animalshelter)

longbeach <- animalshelter::longbeach |>
  dplyr::mutate(
    was_outcome_alive = as.logical(was_outcome_alive),
    dplyr::across(
      c(
        "animal_type",
        "primary_color",
        "secondary_color",
        "sex",
        "intake_condition",
        "intake_type",
        "intake_subtype",
        "reason_for_intake",
        "jurisdiction",
        "outcome_type",
        "outcome_subtype"
      ),
      as.factor
    )
  ) |> 
    dplyr::select(-"intake_is_dead")