compute_column: Compute a column from species traits

Description Usage Arguments Details Value Examples

View source: R/compute_column.R

Description

This is a simple helper function which allows you to compute a column based on a row (a single species) from the output of find_species_traits or Databases::search(). For example, you may search multiple databases for body mass information, and have multiple bodymass values for a single species. You can use this helper function to calculate a single value for the species.

Usage

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compute_column(input, calculator)

Arguments

input

A dataframe (usually returned from find_species_traits or Databases::search()

calculator

A function that calculates a value for the species. It takes a single argument, which is a list where the name of the list is the same as the column names in the input dataframe, and the values are the values for this species.

Details

Alternatively, you may need to process trait information in some way. This function can also help you do that.

Value

A vector with a length equivalent to the number of rows in the input dataframe, where each value is the result of applying the calculator function to the row of the dataframe.

Examples

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input <- data.frame( 
  species_name = c( "Equus quagga", "Ursus maritimus", "Tachyglossus aculeatus" ),
  bodymass_from_database1 = c( 25, 120, 80 ),
  bodymass_from_database2 = c( 27, 126, 79 )
)

input$mean_bodymass = compute_column( input, function( species ) {
  bodymass_values = c( species[["bodymass_from_database1"]], species[["bodymass_from_database2"]] )
  return( mean( bodymass_values ) )
})

print(input)
#             species_name bodymass_from_database1 bodymass_from_database2 mean_bodymass
# 1           Equus quagga                      25                      27          26.0
# 2        Ursus maritimus                     120                     126         123.0
# 3 Tachyglossus aculeatus                      80                      79          79.5

conservationscience/functionaltraits documentation built on May 22, 2020, 3:25 a.m.