Morphological Separation in Bill Dimensions Across Penguin Species

2D density contours with individual observations for the three species

TidyTuesday

2025

Exploring morphological differences between Adelie, Chinstrap, and Gentoo penguin species through bill dimensions analysis. This visualization demonstrates how bill length and depth measurements create distinct clusters that allow for clear species differentiation in the Palmer Archipelago penguin dataset.

Author

Steven Ponce

Published

April 16, 2025

Figure 1: A scatterplot showing bill dimensions (length vs. depth in mm) for three penguin species with density contours. The plot clearly displays three distinct clusters: Adelie penguins (orange, n=152) with shorter, deeper bills centered around 40mm length; Chinstrap penguins (blue, n=68) with medium-length bills around 49mm and greater depth; and Gentoo penguins (green, n=124) with longer, shallower bills around 48mm length and 15mm depth. Diamond markers indicate species means.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse,      # Easily Install and Load the 'Tidyverse'
    ggtext,         # Improved Text Rendering Support for 'ggplot2'
    showtext,       # Using Fonts More Easily in R Graphs
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    camcorder       # Record Your Plot History
    )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  8,
    height =  8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code

penguins <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-04-15/penguins.csv') |>
    clean_names()

3. Examine the Data

Show code

glimpse(penguins)
skim(penguins)

4. Tidy Data

Show code

### |-  tidy data ----
species_stats <- penguins |>
    group_by(species) |>
    summarize(
        mean_bill_len = mean(bill_len, na.rm = TRUE),
        mean_bill_dep = mean(bill_dep, na.rm = TRUE),
        n = n(),
        .groups = 'drop'
    )
# label positions
label_positions <- data.frame(
    species = c("Adelie", "Chinstrap", "Gentoo"),
    x_pos = c(38.5, 47, 48),  
    y_pos = c(20.3, 19.5, 14.5),  
    n = c(152, 68, 124)
)

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = c(
        "Adelie" = "#FF8C00",      
        "Chinstrap" = "#4169E1",   
        "Gentoo" = "#2E8B57"      
    )
)

### |-  titles and caption ----
title_text <- str_wrap("Morphological Separation in Bill Dimensions Across Penguin Species",
                       width = 45)
subtitle_text <- str_glue("2D density contours with individual observations for the three species")

# Create caption
caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 15,
    source_text =  "{ basepenguins } R package" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
    base_theme,
    theme(
        # Text styling 
        plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
        plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
        
        # Axis elements
        axis.title = element_text(color = colors$text, face = "bold", size = rel(0.8)),
        axis.text = element_text(color = colors$text, size = rel(0.7)),
        
        # Grid elements
        panel.grid.minor = element_line(color = "gray50", linewidth = 0.05),
        panel.grid.major = element_line(color = "gray50", linewidth = 0.02),
        
        # Legend elements
        legend.position = "plot",
        legend.direction = "horizontal",
        legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"),
        legend.text = element_text(family = fonts$text, size = rel(0.7)),
        
        # two-row legend
        legend.box.spacing = unit(0.4, "cm"),
        legend.key.width = unit(1.5, "cm"),
        legend.spacing.x = unit(0.2, "cm"),
 
        legend.box = "horizontal",
        legend.box.just = "left",
        
        # Plot margins 
        plot.margin = margin(t = 20, r = 20, b = 20, l = 20),
    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot  ----
p <- ggplot(penguins, aes(x = bill_len, y = bill_dep, color = species)) +
    # Geoms
    geom_point(
        alpha = 0.12, size = 1.5
        ) +
    geom_density_2d(
        size = 0.8, 
        alpha = 0.9,
        bins = 6,
        lineend = "round",
        linejoin = "round"
    ) +
    geom_point(
        data = species_stats,
        aes(x = mean_bill_len, y = mean_bill_dep),
        size = 4,
        shape = 18
    ) +
    geom_label(
        data = label_positions,
        aes(x = x_pos, y = y_pos, 
            label = paste0(species, "\nn = ", n),
            color = species),
        fill = "white",
        alpha = 0.9,
        fontface = "bold",
        label.size = 0.2,  
        label.padding = unit(0.4, "lines"),
        label.r = unit(0.15, "lines")  
    ) +
    # Scales
    scale_color_manual(values = colors$palette) +
    scale_x_continuous(limits = c(30, 62)) +
    scale_y_continuous(limits = c(13, 21.5)) +
    # Labs
    labs(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        x = "Bill Length (mm)",
        y = "Bill Depth (mm)"
        ) +
    # Theme
    theme(
        plot.title = element_text(
            size = rel(1.85),
            family = fonts$title,
            face = "bold",
            color = colors$title,
            lineheight = 1.1,
            margin = margin(t = 5, b = 5)
        ),
        plot.subtitle = element_text(
            size = rel(0.95),
            family = fonts$subtitle,
            color = alpha(colors$subtitle, 0.9),
            lineheight = 1.2,
            margin = margin(t = 5, b = 10)
        ),
        plot.caption = element_markdown(
            size = rel(0.65),
            family = fonts$caption,
            color = colors$caption,
            hjust = 0.5,
            margin = margin(t = 10)
        )
    )

7. Save

Show code

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 15, 
  width = 8,
  height = 8
)

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] camcorder_0.1.0 here_1.0.1      glue_1.8.0      scales_1.3.0   
 [5] skimr_2.1.5     janitor_2.2.0   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0
[21] pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  tzdb_0.4.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         parallel_4.4.0     gifski_1.32.0-1    fansi_1.0.6       
[13] pkgconfig_2.0.3    ggplotify_0.1.2    lifecycle_1.0.4    farver_2.1.2      
[17] compiler_4.4.0     munsell_0.5.1      repr_1.1.7         codetools_0.2-20  
[21] snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10        crayon_1.5.3      
[25] pillar_1.9.0       MASS_7.3-60.2      magick_2.8.5       commonmark_1.9.2  
[29] tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4      labeling_0.4.3    
[33] rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0        
[37] colorspace_2.1-1   cli_3.6.3          magrittr_2.0.3     base64enc_0.1-3   
[41] utf8_1.2.4         withr_3.0.2        bit64_4.5.2        timechange_0.3.0  
[45] rmarkdown_2.29     bit_4.5.0          hms_1.1.3          evaluate_1.0.1    
[49] knitr_1.49         markdown_1.13      gridGraphics_0.5-1 rlang_1.1.4       
[53] isoband_0.2.7      gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6        
[57] renv_1.0.3         svglite_2.1.3      rstudioapi_0.17.1  vroom_1.6.5       
[61] jsonlite_1.8.9     R6_2.5.1           fs_1.6.5           systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_2025_15.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:
- TidyTuesday 2025 Week 15: Base R Penguins

--- title: "Morphological Separation in Bill Dimensions Across Penguin Species" subtitle: "2D density contours with individual observations for the three species" description: "Exploring morphological differences between Adelie, Chinstrap, and Gentoo penguin species through bill dimensions analysis. This visualization demonstrates how bill length and depth measurements create distinct clusters that allow for clear species differentiation in the Palmer Archipelago penguin dataset." author: "Steven Ponce" date: "2025-04-16" categories: ["TidyTuesday", "2025"] tags: [ "ggplot2", "palmerpenguins", "density contours", "morphology", "species classification", "bivariate analysis", "Antarctic wildlife", "ornithology", "multivariate visualization", "ecological data" ] image: "thumbnails/tt_2025_15.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true # filters: # - social-share # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/TidyTuesday/2025/tt_2025_15.html" # description: "See how Antarctic penguin species form distinct clusters based solely on bill dimensions. This visualization of the Palmer Penguins dataset reveals clear morphological separation between Adelie, Chinstrap, and Gentoo penguins using density contours and individual observations." # # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![A scatterplot showing bill dimensions (length vs. depth in mm) for three penguin species with density contours. The plot clearly displays three distinct clusters: Adelie penguins (orange, n=152) with shorter, deeper bills centered around 40mm length; Chinstrap penguins (blue, n=68) with medium-length bills around 49mm and greater depth; and Gentoo penguins (green, n=124) with longer, shallower bills around 48mm length and 15mm depth. Diamond markers indicate species means. ](tt_2025_15.png){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 1. Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ if (!require("pacman")) install.packages("pacman") pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs janitor, # Simple Tools for Examining and Cleaning Dirty Data skimr, # Compact and Flexible Summaries of Data scales, # Scale Functions for Visualization glue, # Interpreted String Literals here, # A Simpler Way to Find Your Files camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 8, height = 8, units = "in", dpi = 320 ) # Source utility functions suppressMessages(source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/utils/image_utils.R")) source(here::here("R/themes/base_theme.R")) ``` #### 2. Read in the Data ```{r} #| label: read #| include: true #| eval: true #| warning: false penguins <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-04-15/penguins.csv') |> clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(penguins) skim(penguins) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- tidy data ---- species_stats <- penguins |> group_by(species) |> summarize( mean_bill_len = mean(bill_len, na.rm = TRUE), mean_bill_dep = mean(bill_dep, na.rm = TRUE), n = n(), .groups = 'drop' ) # label positions label_positions <- data.frame( species = c("Adelie", "Chinstrap", "Gentoo"), x_pos = c(38.5, 47, 48), y_pos = c(20.3, 19.5, 14.5), n = c(152, 68, 124) ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = c( "Adelie" = "#FF8C00", "Chinstrap" = "#4169E1", "Gentoo" = "#2E8B57" ) ) ### |- titles and caption ---- title_text <- str_wrap("Morphological Separation in Bill Dimensions Across Penguin Species", width = 45) subtitle_text <- str_glue("2D density contours with individual observations for the three species") # Create caption caption_text <- create_social_caption( tt_year = 2025, tt_week = 15, source_text = "{ basepenguins } R package" ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( # Text styling plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)), plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)), # Axis elements axis.title = element_text(color = colors$text, face = "bold", size = rel(0.8)), axis.text = element_text(color = colors$text, size = rel(0.7)), # Grid elements panel.grid.minor = element_line(color = "gray50", linewidth = 0.05), panel.grid.major = element_line(color = "gray50", linewidth = 0.02), # Legend elements legend.position = "plot", legend.direction = "horizontal", legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"), legend.text = element_text(family = fonts$text, size = rel(0.7)), # two-row legend legend.box.spacing = unit(0.4, "cm"), legend.key.width = unit(1.5, "cm"), legend.spacing.x = unit(0.2, "cm"), legend.box = "horizontal", legend.box.just = "left", # Plot margins plot.margin = margin(t = 20, r = 20, b = 20, l = 20), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot(penguins, aes(x = bill_len, y = bill_dep, color = species)) + # Geoms geom_point( alpha = 0.12, size = 1.5 ) + geom_density_2d( size = 0.8, alpha = 0.9, bins = 6, lineend = "round", linejoin = "round" ) + geom_point( data = species_stats, aes(x = mean_bill_len, y = mean_bill_dep), size = 4, shape = 18 ) + geom_label( data = label_positions, aes(x = x_pos, y = y_pos, label = paste0(species, "\nn = ", n), color = species), fill = "white", alpha = 0.9, fontface = "bold", label.size = 0.2, label.padding = unit(0.4, "lines"), label.r = unit(0.15, "lines") ) + # Scales scale_color_manual(values = colors$palette) + scale_x_continuous(limits = c(30, 62)) + scale_y_continuous(limits = c(13, 21.5)) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = "Bill Length (mm)", y = "Bill Depth (mm)" ) + # Theme theme( plot.title = element_text( size = rel(1.85), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.95), family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 1.2, margin = margin(t = 5, b = 10) ), plot.caption = element_markdown( size = rel(0.65), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( plot = p, type = "tidytuesday", year = 2025, week = 15, width = 8, height = 8 ) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in [`tt_2025_15.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/tt_2025_15.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data Sources: - TidyTuesday 2025 Week 15: [Base R Penguins](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-04-01) :::