Bird Abundance

Figure 1: The image displays the abundance of birds for the 15 most visited National Parks in the USA

This document analyzes a dataset of species from the 15 most visited National Parks in the USA provided by #TidyTuesday. The main focus of this analysis will be on birds.

Setting up

# Loading packages -------------------------------------------------------------
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
  ggeasy,           # Makes theming plots easier
  glue,             # Interpreted String Literals
  ggfx              # Pixel Filters for "ggplot2" and "grid"
)

# Visualization Parameters -----------------------------------------------------
# Plot aesthetics
title_col    <- "gray20"           
subtitle_col <- "gray20"     
caption_col  <- "gray30"   
text_col     <- "gray20"  

# Icons
tt <- str_glue("#TidyTuesday: { 2024 } Week { 41 } &bull; Source: National Park Species<br>")
li <- str_glue("<span style='font-family:fa6-brands'>&#xf08c;</span>")
gh <- str_glue("<span style='font-family:fa6-brands'>&#xf09b;</span>")

# Text
title_text <- str_glue("Abundance of birds per national park")
caption_text  <- str_glue("{tt} {li} c-monaghan &bull; {gh} c-monaghan &bull; #rstats #ggplot2")

# Fonts
font_add("fa6-brands", here::here("fonts/6.4.2/Font Awesome 6 Brands-Regular-400.otf"))
font_add_google("Oswald", regular.wt = 400, family = "title")
font_add_google("Noto Sans", regular.wt = 400, family = "caption")
font_add_google("Merriweather Sans", regular.wt = 400, family = "text")
showtext_auto(enable = TRUE)

# Theme
theme_set(theme_minimal(base_size = 14))

theme_update(
  plot.title.position   = "plot",
  plot.caption.position = "plot",
  panel.background      = element_rect(fill = "white", color = "white"),
  panel.grid            = element_blank(),
  panel.grid.major.x    = element_blank(),
  axis.text.x           = element_text(size = 12, family = "text"),
  axis.text.y           = element_blank(),
  legend.position       = "bottom"
)

# Variables --------------------------------------------------------------------
# Paths
path <- "posts/2024/"
folder <- "2024-10-07-TT-W41/"

# Reading in data --------------------------------------------------------------
data <- tidytuesdayR::tt_load(2024, week = 41)

species <- data$most_visited_nps_species_data

Species data

The dataset includes various information on animal and plant species from 15 of the most visited parks in the USA. In particular, we are interested in the abundance of birds in each park.

Calculating abundance

bird_abundance <- species %>%
  select(ParkName, CategoryName) %>%
  group_by(ParkName, CategoryName) %>%
  summarise(Abundance = n()) %>%
  rename(Park_name = ParkName, Species = CategoryName)  %>%
  mutate(Park_name = as.factor(Park_name)) %>%
  filter(Species == "Bird")

bird_abundance %>%
  head()

# A tibble: 6 × 3
# Groups:   Park_name [6]
  Park_name                     Species Abundance
  <fct>                         <chr>       <int>
1 Acadia National Park          Bird          364
2 Bryce Canyon National Park    Bird          218
3 Cuyahoga Valley National Park Bird          246
4 Glacier National Park         Bird          277
5 Grand Canyon National Park    Bird          456
6 Grand Teton National Park     Bird          266

Visualising bird abundance

bird_abundance_plot <- bird_abundance %>%
  ggplot() +
  
  # Add horizontal reference lines at intervals of 125
  geom_hline(
    data = data.frame(y = c(0:4) * 125), 
    aes(yintercept = y), 
    color = "lightgrey") + 
  
  # Create a bar plot in polar coordinates
  geom_col(
    aes(x = reorder(str_wrap(Park_name, 16), Abundance),
        y = Abundance,
        fill = Abundance),
    position = "dodge2", show.legend = TRUE, alpha = 0.9) +
  
  # Use polar coordinates 
  coord_polar() + 
  
  # Set y-axis limits, expand to control padding, and custom breaks for reference lines
  scale_y_continuous(
    limits = c(-200, 500),
    expand = c(0, 0),
    breaks = c(0, 100, 200, 300, 400)
  ) + 
  
  # Apply a color gradient for the bars based on abundance levels
  scale_fill_gradientn(
    "Abundance", # Title
    colours = c( "#e9b91c","#db9a17","#ce7b12","#be471b", "#ae1324") # Custom colours
  ) +
  
  # Customize the legend
  guides(
    fill = guide_colorsteps(
      barwidth = 15, 
      barheight = 1, 
      title.position = "top", 
      title.hjust = .5
    )) +
  
  # Add titles and labels
  labs(
    title = title_text,
    caption = caption_text, 
    x = NULL,
    y = NULL) +
  
  # Apply theme customization
  theme(    
    # Title
    plot.title = element_text(
      size = rel(5),
      family = "title",
      face = "bold",
      colour = title_col,
      lineheight = 1.1,
      hjust = 0.5,
      margin = margin(t = 5, b = 5)),
    # Caption
    plot.caption = element_markdown(
      size = rel(1.25),
      family = "caption",
      colour = caption_col,
      lineheight = 1.1,
      hjust = 0.5,
      margin = margin(t = 5, b = 5)),
    legend.title = element_text(size = 20),
    legend.text = element_text(size = 15)
  )

bird_abundance_plot

Saving

# Saving plot
ggsave(
  filename = here::here(path, folder, "tt_2024_w41.png"),
  plot = bird_abundance_plot,
  width = 9,
  height = 6,
  units = "in",
  dpi = 320
  )

# Thumbnail  
magick::image_read(here::here(path, folder, "tt_2024_w41.png")) |> 
  magick::image_resize(geometry = "800") |> 
  magick::image_write(here::here(path, "thumbnails/tt_2024_w41_thumb.png"))

Appendix

Expand for Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14 ucrt)
 os       Windows 11 x64 (build 22631)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  English_Ireland.utf8
 ctype    English_Ireland.utf8
 tz       Europe/Dublin
 date     2024-10-09
 pandoc   3.2 @ C:/Coding/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
 quarto   1.5.57

─ Packages ───────────────────────────────────────────────────────────────────
 package     * version date (UTC) lib source
 dplyr       * 1.1.4   2023-11-17 [1] CRAN (R 4.4.1)
 forcats     * 1.0.0   2023-01-29 [1] CRAN (R 4.4.1)
 ggeasy      * 0.1.4   2023-03-12 [1] CRAN (R 4.4.1)
 ggfx        * 1.0.1   2022-08-22 [1] CRAN (R 4.4.1)
 ggplot2     * 3.5.1   2024-04-23 [1] CRAN (R 4.4.1)
 ggtext      * 0.1.2   2022-09-16 [1] CRAN (R 4.4.1)
 glue        * 1.7.0   2024-01-09 [1] CRAN (R 4.4.1)
 lubridate   * 1.9.3   2023-09-27 [1] CRAN (R 4.4.1)
 purrr       * 1.0.2   2023-08-10 [1] CRAN (R 4.4.1)
 readr       * 2.1.5   2024-01-10 [1] CRAN (R 4.4.1)
 sessioninfo * 1.2.2   2021-12-06 [1] CRAN (R 4.4.1)
 showtext    * 0.9-7   2024-03-02 [1] CRAN (R 4.4.1)
 showtextdb  * 3.0     2020-06-04 [1] CRAN (R 4.4.1)
 stringr     * 1.5.1   2023-11-14 [1] CRAN (R 4.4.1)
 sysfonts    * 0.8.9   2024-03-02 [1] CRAN (R 4.4.1)
 tibble      * 3.2.1   2023-03-20 [1] CRAN (R 4.4.1)
 tidyr       * 1.3.1   2024-01-24 [1] CRAN (R 4.4.1)
 tidyverse   * 2.0.0   2023-02-22 [1] CRAN (R 4.4.1)

 [1] C:/Coding/R-4.4.1/library

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