palmerpenguins R Package

The package provides two datasets. After loading the library, you can access them using data(package = 'palmerpenguins') or by calling them directly if they are attached to the search path.

• penguins: A simplified version of the data suitable for exploration and visualization. It contains 8 variables including species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, sex, and year.
• penguins_raw: The original dataset containing all variables and original names as downloaded.

library(palmerpenguins)
data(package = 'palmerpenguins')

# View the simplified dataset
head(penguins)

# View the raw dataset
head(penguins_raw)

The package provides two primary datasets for exploration:

1. penguins: A simplified version of the raw data, ideal for most data exploration and visualization tasks. Use ?penguins in R for detailed documentation.
2. penguins_raw: The original dataset containing all variables and original names as downloaded. Use ?penguins_raw in R for detailed documentation.

Both datasets include information on different penguin species collected from various islands in the Palmer Archipelago, Antarctica.

library(palmerpenguins)

# Access the simplified dataset
head(penguins)

# Access the raw dataset
head(penguins_raw)

You can install the stable version of the package from CRAN using install.packages(). Alternatively, if you need the development version, you can install it directly from GitHub using the remotes package.

# Install released version from CRAN
install.packages("palmerpenguins")

# Install development version from GitHub
# install.packages("remotes")
remotes::install_github("allisonhorst/palmerpenguins")

The penguins dataset is designed for use with tidyverse for quick summarization and ggplot2 for visualization.

To summarize data (e.g., counting species or calculating means), use dplyr verbs like count() and summarize(). To visualize, use ggplot2 to map variables like flipper_length_mm, body_mass_g, or bill_length_mm to aesthetics.

library(tidyverse)
library(palmerpenguins)

# Summarize species counts
penguins %>% 
  count(species)

# Calculate mean for all numeric columns by species
penguins %>% 
  group_by(species) %>% 
  summarize(across(where(is.numeric), mean, na.rm = TRUE))

# Visualize relationship between flipper length and body mass
ggplot(data = penguins, 
       aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point(aes(color = species, shape = species), size = 3, alpha = 0.8) +
  theme_minimal()

If you use the package in your research or projects, you should cite it properly. You can generate the citation information directly in R using the citation() function.

citation("palmerpenguins")