In russHyde/polyply: Manipulate Multiple Data-frames In A Pipeline

The datasets provided here were obtained from a few sources.

Body-Brain sizes were obtained from the MASS package, cleaned-up and tidy-fied as follows:

data("Animals", package = "MASS")

animals <- Animals %>%
  tibble::rownames_to_column(var = "common_name") %>%
  dplyr::mutate(
    common_name = str_replace(
      common_name, "Dipliodocus", "Diplodocus"
    )
  )

Mappings from the common-name to the Genus-species[-subspecies] tuple were obtained haphazardly by web-searching. If they're wrong, feel free to submit an issue / pull-request. Similarly, if you can find any mention of the 'Potar Monkey' in a source other than the R-documentation, or MASS, or Rousseeuw and Leroy you're doing better than me - please add it's "[Genus] [species]".

species <- c("Aplodontia rufa",
            "Bos taurus",
            "Canis lupus",
            "Capra hircus",
            "Cavia porcellus",
            "Diplodocus longus",
            "Elephas maximus",
            "Equus africanus asinus",
            "Equus ferus caballus",
            NA,
            "Felis silvestris",
            "Giraffa camelopardalis",
            "Gorilla gorilla",
            "Homo sapiens",
            "Loxodonta africana",
            "Triceratops horridus",
            "Macaca mulatta",
            "Macropus giganteus",
            "Mesocricetus auratus",
            "Mus musculus",
            "Oryctolagus cuniculus",
            "Ovis aries",
            "Panthera onca",
            "Pan troglodytes",
            "Rattus norvegicus",
            "Brachiosaurus altithorax",
            "Talpa europaea",
            "Sus scrofa"
)

common_to_species <- tibble::data_frame(
  common_name = animals$common_name,
  species = species
)

The brains/body-size dataset is bizarre: there should maybe be a few more birds to bridge the gap between the dinosaurs and all the mammals.

We obtained the taxonomic tree for most of the species using the package taxize and the NCBI database.

All the animals for which taxonomies were obtained were mammals, so in terms of

"Do Kings Play Chess On Fine Green Silk?"

we stopped at "On" (Order).

# Returns a list: entries are data-frames if taxonomic data returns
taxon_data <- taxize::classification(
  x = common_to_species$species,
  get = "order",
  db = "ncbi"
)

# We kept only the species that returned taxonomic data and extracted the
# 'family' and 'order' for each
taxonomy <- Filter(is.data.frame, taxon_data) %>%
  dplyr::bind_rows(.id = "species") %>%
  dplyr::select(-id) %>%
  dplyr::filter(rank %in% c("family", "order")) %>%
  tidyr::spread(key = rank, value = name)