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Summarize mammal diversity by country, continent, or subregion

Source: R/mdd-distribution-summary.R
mdd_distribution_summary.Rd

[Experimental]

This function is experimental and may change in future releases.

Wrapper around mdd_distribution_summary_raw() that optionally excludes domesticated species and species considered widespread at the requested geographic level.

Usage

mdd_distribution_summary(
  level = c("country", "continent", "subregion"),
  checklist = NULL,
  exclude_domesticated = FALSE,
  exclude_widespread = FALSE,
  widespread_threshold = NULL
)

Arguments

level

Geographic level to summarize. Use "country", "continent", or "subregion".

checklist

Optional checklist data frame. Defaults to mdd_checklist.

exclude_domesticated

Logical. If TRUE, drop rows where domestic == 1.

exclude_widespread

Logical. If TRUE, drop species whose distribution spans more than widespread_threshold units at the chosen level.

widespread_threshold

Optional threshold used to define widespread species. If NULL, a level-specific default is used.

Value

A tibble with one row per geographic unit and the columns region, orders, families, genera, living_species, extinct_species, and total_species.

Examples

mdd_distribution_summary(
  level = "country",
  checklist = dplyr::slice(mdd_checklist, 1:50)
)
#> # A tibble: 28 × 7
#>    region    orders families genera living_species extinct_species total_species
#>    <chr>      <int>    <int>  <int>          <int>           <int>         <int>
#>  1 Brazil         1        1      7             21               0            21
#>  2 Peru           2        2      9             21               0            21
#>  3 Colombia       2        2      8             17               0            17
#>  4 Ecuador        2        2      6             17               0            17
#>  5 Venezuela      2        2      7             14               0            14
#>  6 Bolivia        2        2      7             12               0            12
#>  7 Costa Ri…      1        1      5              9               0             9
#>  8 Guyana         1        1      6              9               0             9
#>  9 Argentina      1        1      5              8               0             8
#> 10 French G…      1        1      5              8               0             8
#> # ℹ 18 more rows