Wingspan

The R {wingspan} package makes available information about the birds and bonus cards in the boardgame wingspan

Wingspan (the boardgame) was designed by Elizabeth Hargrave, with art by Natalia Rojas, Ana Maria Martinez Jaramillo, and Beth Sobel

The boardgame is a competitive, medium-weight, card-driven, engine-building board game, and is absolutely delightful!

This package just provides stats and data on the cards in the box - it won’t really help you to play the game, and doesn’t include any of the amazing artwork; it’s just the facts about all birds and bonus cards as collected by TawnyFrogmouth at BoardGameGeek (original data).

What’s in the box

• birds a data.frame of information about each of the cards in the base game and the expansions (europe and oceania)
• bonus_cards information about the cards used to give you a bonus at the end of the game (based upon the numbers of birds of different types you accumulate during the game)

Installation

You can install from GitHub with:

# install.package('remotes')
remotes::install_github('coolbutuseless/wingspan')

Example Bird Data

Some of the stats for the 2 birds at the bottom of this image are shown below.

library(ape)
library(vegan)
#> Loading required package: permute
#> Loading required package: lattice
#> This is vegan 2.5-7
library(dplyr)
library(wingspan)

wingspan::birds %>% 
  filter(common_name %in% c('Barn Swallow', 'Peregrine Falcon')) %>%
  select(common_name, scientific_name, points = victory_points, predator, 
         egg_capacity, nest_type, total_food_cost) %>%
  knitr::kable()

common_name	scientific_name	points	predator	egg_capacity	nest_type	total_food_cost
Barn Swallow	Hirundo rustica	1	FALSE	3	Wild	1
Peregrine Falcon	Falco peregrinus	5	TRUE	2	Platform	2

Radial Dendrogram

Thanks to hbgoldspiel on twitter for this great dendogram grouping birds by their similarity.

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Replace NAs
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
birds2 <- birds %>%
  tidyr::replace_na(list(
    power_color    = 'None',
    power_category = 'None',
    nest_type      = 'Misc',
    wingspan       = 0
  ))


#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Numeric matrix of traits
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
birds_mat <- as.data.frame(model.matrix(
  lm(
    victory_points ~ set + power_color + power_category + predator + 
      flocking + bonus_card + nest_type + egg_capacity + wingspan + 
      forest + grassland + wetland + invertebrate + seed + fish + fruit +
      rodent + nectar + any_food - 1, data = birds2
  )
))

rownames(birds_mat) <- birds$common_name
birds_mat$points    <- birds$victory_points

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Calcualte Distance matrix
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
birds_norm <- vegan::decostand(birds_mat, "normalize")
birds_ch   <- vegan::vegdist(birds_norm, 'euc')
attr(birds_ch, 'labels') <- rownames(birds_mat)


#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Compute Ward's minimumvariance clustering
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
birds_ch_ward <- hclust(birds_ch, method = 'ward.D2')

plot(ape::as.phylo(birds_ch_ward), type = "fan", cex = 1)

The full dendogram is huge, but a glimpse at detail below shows that flightless birds have been grouped together. Also there’s a grouping of condors and vultures.

Download PDF version of dendogram

Example Summary Plot

library(ggplot2)

ggplot(birds, aes(as.factor(total_food_cost), victory_points)) + 
  geom_boxplot() + 
  theme_bw() + 
  labs(
    x = "Total food cost to play bird card",
    y = "Bird value (Victory points at end of game)",
    title = "Birds with higher total food cost score more victory points"
  ) + 
  scale_y_continuous(breaks = 0:10)

Acknowledgements

• TawnyFrogmouth at BoardGameGeek for collating and sharing the data
• Stonemeier games for releasing such a delicious game
• R Core for developing and maintaining the language.
• CRAN maintainers, for patiently shepherding packages onto CRAN and maintaining the repository