# Palmer Archipelago Penguins Data in the palmerpenguins R Package - An Alternative to Anderson’s Irises

In 1935, Edgar Anderson collected size measurements for 150 flowers from three species of Iris on the Gaspé Peninsula in Quebec, Canada. Since then, Anderson’s Iris observations have become a classic dataset in statistics, machine learning, and data science teaching materials. It is included in the base R datasets package as iris, making it easy for users to access without knowing much about it. However, the lack of data documentation, presence of non-intuitive variables (e.g. “sepal width”), and perfectly balanced groups with zero missing values make iris an inadequate and stale dataset for teaching and learning modern data science skills. Users would benefit from working with a more representative, real-world environmental dataset with a clear link to current scientific research. Importantly, Anderson’s Iris data appeared in a 1936 publication by R. A. Fisher in the Annals of Eugenics (which is often the first-listed citation for the dataset), inextricably linking iris to eugenics research. Thus, a modern alternative to iris is needed. In this paper, we introduce the palmerpenguins R package , which includes body size measurements collected from 2007 - 2009 for three species of Pygoscelis penguins that breed on islands throughout the Palmer Archipelago, Antarctica. The penguins dataset in palmerpenguins provides an approachable, charismatic, and near drop-in replacement for iris with topical relevance for polar climate change and environmental impacts on marine predators. Since the release on CRAN in July 2020, the palmerpenguins package has been downloaded over 462,000 times, highlighting the demand and widespread adoption of this viable iris alternative. We directly compare the iris and penguins datasets for selected analyses to demonstrate that R users, in particular teachers and learners currently using iris, can switch to the Palmer Archipelago penguins for many use cases including data wrangling, visualization, linear modeling, multivariate analysis (e.g., PCA), cluster analysis and classification (e.g., by k-means).

Allison M. Horst (University of California Santa Barbara)https://bren.ucsb.edu/ , Alison Presmanes Hill (Voltron Data)https://voltrondata.com/ , Kristen B. Gorman (University of Alaska Fairbanks)https://www.uaf.edu/cfos/
2022-06-21

## Introduction

In 1935, American botanist Edgar Anderson measured petal and sepal structural dimensions (length and width) for 50 flowers from three Iris species: Iris setosa, Iris versicolor, and Iris virginica . The manageable but non-trivial size (5 variables and 150 total observations) and characteristics of Anderson’s Iris dataset, including linear relationships and multivariate normality, have made it amenable for introducing a wide range of statistical methods including data wrangling, visualization, linear modeling, multivariate analyses, and machine learning. The Iris dataset is built into a number of software packages including the auto-installed datasets package in R (as iris, R Core Team 2021), Python’s scikit-learn machine learning library , and the SAS Sashelp library (SAS Institute, Cary NC), which has facilitated its widespread use. As a result, eighty-six years after the data were initially published, the Iris dataset remains ubiquitous in statistics, computational methods, software documentation, and data science courses and materials.

There are a number of reasons that modern data science practitioners and educators may want to move on from iris. First, the dataset lacks metadata , which does not reinforce best practices and limits meaningful interpretation and discussion of research methods, analyses, and outcomes. Of the five variables in iris, two (Sepal.Width and Sepal.Length) are not intuitive for most non-botanists. Even with explanation, the difference between petal and sepal dimensions is not obvious. Second, iris contains equal sample sizes for each of the three species (n = 50) with no missing values, which is cleaner than most real-world data that learners are likely to encounter. Third, the single factor (Species) in iris limits options for analyses. Finally, due to its publication in the Annals of Eugenics by statistician R.A. Fisher , iris is burdened by a history in eugenics research, which we are committed to addressing through the development of new data science education products as described below.

Given the growing need for fresh data science-ready datasets, we sought to identify an alternative dataset that could be made easily accessible for a broad audience. After evaluating the positive and negative features of iris in data science and statistics materials, we established the following criteria for a suitable alternative:

• Available by appropriate license like a Creative Commons 0 license (CC0 “no rights reserved”)
• Feature intuitive subjects and variables that are interesting and understandable to learners across disciplines
• Manageable (but not trivial) in size
• Minimal data cleaning and pre-processing required for most analyses
• Real-world (not manufactured) modern data
• Provides similar opportunities for teaching and learning R, data science, and statistical skills
• Can easily replace iris for most use cases

Here, we describe an alternative to iris that largely satisfies these criteria: a refreshing, approachable, and charismatic dataset containing real-world body size measurements for three Pygoscelis penguin species that breed throughout the Western Antarctic Peninsula region, made available through the United States Long-Term Ecological Research (US LTER) Network. By comparing data structure, size, and a range of analyses side-by-side for the two datasets, we demonstrate that the Palmer Archipelago penguin data are an ideal substitute for iris for many use cases in statistics and data science education.

## Data source

Body size measurements (bill length and depth, flipper length - flippers are the modified “wings” of penguins used for maneuvering in water, and body mass), clutch (i.e., egg laying) observations (e.g., date of first egg laid, and clutch completion), and carbon (13C/12C, $$\delta$$13C) and nitrogen (15N/14N, $$\delta$$15N) stable isotope values of red blood cells for adult male and female Adélie (P. adeliae), chinstrap (P. antarcticus), and gentoo (P. papua) penguins on three islands (Biscoe, Dream, and Torgersen) within the Palmer Archipelago were collected from 2007 - 2009 by Dr. Kristen Gorman in collaboration with the Palmer Station LTER, part of the US LTER Network. For complete data collection methods and published analyses, see Gorman et al. (2014). Throughout this paper, penguins species are referred to as “Adélie”, “Chinstrap”, and “Gentoo”.

The data in the palmerpenguins R package are available for use by CC0 license (“No Rights Reserved”) in accordance with the Palmer Station LTER Data Policy and the LTER Data Access Policy, and were imported from the Environmental Data Initiative (EDI) Data Portal at the links below:

## The palmerpenguins R package

R users can install the palmerpenguins package from CRAN:

install.packages("palmerpenguins")

Information, examples, and links to community-contributed materials are available on the palmerpenguins package website: allisonhorst.github.io/palmerpenguins/. See the Appendix for how Python and Julia users can access the same data.

The palmerpenguins R package contains two data objects: penguins_raw and penguins. The penguins_raw data consists of all raw data for 17 variables, recorded completely or in part for 344 individual penguins, accessed directly from EDI (penguins_raw properties are summarized in Appendix B). We generally recommend using the curated data in penguins, which is a subset of penguins_raw retaining all 344 observations, minimally updated (Appendix A) and reduced to the following eight variables:

• species: a factor denoting the penguin species (Adélie, Chinstrap, or Gentoo)
• island: a factor denoting the Palmer Archipelago island in Antarctica where each penguin was observed (Biscoe Point, Dream Island, or Torgersen Island)
• bill_length_mm: a number denoting length of the dorsal ridge of a penguin bill (millimeters)
• bill_depth_mm: a number denoting the depth of a penguin bill (millimeters)
• flipper_length_mm: an integer denoting the length of a penguin flipper (millimeters)
• body_mass_g: an integer denoting the weight of a penguin’s body (grams)
• sex: a factor denoting the sex of a penguin sex (male, female) based on molecular data
• year: an integer denoting the year of study (2007, 2008, or 2009)

The same data exist as comma-separated value (CSV) files in the package (“penguins_raw.csv” and “penguins.csv”), and can be read in using the built-in path_to_file() function in palmerpenguins. For example,

library(palmerpenguins)
df <- read.csv(path_to_file("penguins.csv"))

will read in “penguins.csv” as if from an external file, thus automatically parsing species, island, and sex variables as characters instead of factors. This option allows users opportunities to practice or demonstrate reading in data from a CSV, then updating variable class (e.g., characters to factors).

## Comparing iris and penguins

The penguins data in palmerpenguins is useful and approachable for data science and statistics education, and is uniquely well-suited to replace the iris dataset. Comparisons presented are selected examples for common iris uses, and are not exhaustive.

Table 1: Overview comparison of penguins and iris dataset features and characteristics.
Feature iris penguins
Year(s) collected 1935 2007 - 2009
Dimensions (col x row) 5 x 150 8 x 344
Variable classes double (4), factor (1) double (2), int (3), factor (3)
Missing values? no (n = 0; 0.0%) yes (n = 19; 0.7%)

### Data structure and sample size

Both iris and penguins are in tidy format with each column denoting a single variable and each row containing measurements for a single iris flower or penguin, respectively. The two datasets are comparable in size: dimensions (columns × rows) are 5 × 150 and 8 × 344 for iris and penguins, respectively, and sample sizes within species are similar (Tables 1 & 2).

Notably, while sample sizes in iris across species are all the same, sample sizes in penguins differ across the three species. The inclusion of three factor variables in penguins (species, island, and sex), along with year, create additional opportunities for grouping, faceting, and analysis compared to the single factor (Species) in iris.

Unlike iris, which contains only complete cases, the penguins dataset contains a small number of missing values (nmissing = 19, out of 2,752 total values). Missing values and unequal sample sizes are common in real-world data, and create added learning opportunity to the penguins dataset.

Table 2: Grouped sample size for iris (by species; n = 150 total) and penguins (by species and sex; n = 344 total). Data in penguins can be further grouped by island and study year.
iris sample size (by species)
penguins sample size (by species and sex)
Iris species Sample size Penguin species Female Male NA
setosa 50 Adélie 73 73 6
versicolor 50 Chinstrap 34 34 0
virginica 50 Gentoo 58 61 5

### Continuous quantitative variables

Distributions, relationships between variables, and clustering can be visually explored between species for the four structural size measurements in penguins (flipper length, body mass, bill length and depth; Figure 2) and iris (sepal width and length, petal width and length; Figure 3).

Figure 2: Distributions and correlations for numeric variables in the penguins data (flipper length (mm), body mass (g), bill length (mm) and bill depth (mm)) for the three observed species: Gentoo (green, triangles); Chinstrap (blue, circles); and Adélie (orange, squares). Significance indicated for bivariate correlations: *p < 0.05; **p < 0.01; ***p < 0.001.

Figure 3: Distributions and correlations for numeric variables in iris (petal length (cm), petal width (cm), sepal length (cm) and sepal width (cm)) for the three included iris species: Iris setosa (light gray, circles); Iris versicolor (dark gray, triangles); and Iris virginica (black, squares). Significance indicated for bivariate correlations: *p < 0.05; **p < 0.01; ***p < 0.001.

Both penguins and iris offer numerous opportunities to explore linear relationships and correlations, within and across species (Figures 2 & 3). A bivariate scatterplot made with the iris dataset reveals a clear linear relationship between petal length and petal width. Using penguins (Figure 4), we can create a uniquely similar scatterplot with flipper length and body mass. The overall trend across all three species is approximately linear for both iris and penguins. Teachers may encourage students to explore how simple linear regression results and predictions differ when the species variable is omitted, compared to, for example, multiple linear regression with species included (Figure 4).

Figure 4: Representative linear relationships for (A): penguin flipper length (mm) and body mass (g) for Adélie (orange circles), Chinstrap (blue triangles), and Gentoo (green squares) penguins; (B): iris petal length (cm) and width (cm) for Iris setosa (light gray circles), Iris versicolor (dark gray triangles) and Iris virginica (black squares). Within-species linear model is visualized for each penguin or iris species.

Notably, distinctions between species are clearer for iris petals - particularly, the much smaller petals for Iris setosa - compared to penguins, in which Adélie and Chinstrap penguins are largely overlapping in body size (body mass and flipper length), and are both generally smaller than Gentoo penguins.

Simpson’s Paradox is a data phenomenon in which a trend observed between variables is reversed when data are pooled, omitting a meaningful variable. While often taught and discussed in statistics courses, finding a real-world and approachable example of Simpson’s Paradox can be a challenge. Here, we show one (of several possible - see Figure 2) Simpson’s Paradox example in penguins: exploring bill dimensions with and without species included (Figure 5). When penguin species is omitted (Figure 5A), bill length and depth appear negatively correlated overall. The trend is reversed when species is included, revealing an obviously positive correlation between bill length and bill depth within species (Figure 5B).