Pokemon

This week we are exploring Pokemon! This dataset is sourced from {pokemon} (CRAN | github), an R package which provides Pokemon information in both English and Brazilian Portuguese.

This package provides a dataset of Pokemon information in both English and Brazilian Portuguese. The dataset contains 949 rows and 22 columns, including information such as the Pokemon’s name, ID, height, weight, stats, type, and more.

Thank you to Frank Hull for curating this week’s dataset.

The Data

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

tuesdata <- tidytuesdayR::tt_load('2025-04-01')
## OR
tuesdata <- tidytuesdayR::tt_load(2025, week = 13)

pokemon_df <- tuesdata$pokemon_df

# Option 2: Read directly from GitHub

pokemon_df <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-04-01/pokemon_df.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('2025-04-01')

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

pokemon_df = pandas.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-04-01/pokemon_df.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

pokemon_df.csv

variable	class	description
id	integer	The unique ID of each Pokemon.
pokemon	character	The name of each pokemon.
species_id	integer	The species ID of each Pokemon.
height	double	The height of each pokemon.
weight	double	The weight of each pokemon.
base_experience	integer	The base experience of each Pokemon.
type_1	character	The primary type.
type_2	character	The secondary type.
hp	integer	The HP (hit points).
attack	integer	The attack points.
defense	integer	The defense points.
special_attack	integer	The special attack points.
special_defense	integer	The special defense points.
speed	integer	The speed.
color_1	character	The primary color of each pokemon.
color_2	character	The secondary color of each pokemon.
color_f	character	The final color of each pokemon.
egg_group_1	character	The primary egg group.
egg_group_2	character	The secondary egg group.
url_icon	character	The URL for the icon of each Pokemon (if available). Note that these are missing the starting “https:”.
generation_id	integer	The generation ID of each Pokemon.
url_image	character	The URL for the image of each Pokemon.

Cleaning Script

# Clean data provided by https://github.com/williamorim/pokemon. No cleaning was necessary.

# install.packages("pokemon")
pokemon_df <- pokemon::pokemon