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Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public. Build Status codecov MADtraits - Make A Database of Traits

Will Pearse (will.pearse@usu.edu)

Part of the MAD world of packages that Make A Database from existing data. Use of MADtraits, and all MADworld packages, requires you to cite the underlying trait data it downloads - the function citations will give you this citation information for whatever data you are working with.

Installation

# install.packages("devtools") # (If devtools not installed)
library(devtools)
install_github("willpearse/MADtraits")

Getting started

Pick a directory on your hard-drive that you can use as a 'cache' to store data downloaded from individual papers/repositories using MADtraits. Mine, for example, is ~/Code/MADtraits/cache. This is optional, but recommended, as otherwise it will take a very long time to use MADtraits every time you use it. Once you've chosen that, run the following:

library(MADtraits)
data <- MADtraits("~/Code/MADtraits/cache")

This will take a while the first time, but as long as you always use that same cache location, it will be almost instantaneous after that.

Once you have that data, you can optionally 'clean' it harmonising species' and trait names, and matching (as best possible) the units across different measurements (e.g., converting all weights from kg to g, picking units on the basis of the most commonly used one in the data). Note that the nomenclature used in MADtraits isn't guaranteed to be the one you prefer - read on to learn more about the internal structure of MADtraits to do such cleaning for yourself.

clean.data <- clean.MADtraits(data)

You can now subset your data according to particular species or traits like this:

clean.data[c("quercus_robur","quercus_ilex"), "height"]

MADtraits structure

A MADtraits data object is really just data.frames in a list: one for continuous data, and the other for categorical data. Knowing this, you can maniuplate the data however you want once you've downloaded it using something like aggreate or apply to average across species/traits.

str(clean.data)

Note that the last column in each of the data.frames is special: it's metadata. This is set of key:value pairs, separated by ;, that allow you to extract additional information about each trait observation (e.g., the latitude at which it was recorded).

Contributing to MADtraits and its internals

Thank you for your interest in the package! We have a detailed set of instructions for how the package works up available online https://github.com/willpearse/MADtraits/wiki. Please follow those instructions!

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Make A Database of trait data

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