MADcomm - Make A Database of ecological community data

Will Pearse ([email protected])

Part of the MAD world of packages that Make A Database from existing data. Use of MADcomm, 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/MADcomm")

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 MADcomm. Mine, for example, is ~/Code/MADcomm/cache. This is optional, but recommended, as otherwise it will take a very long time to use MADcomm every time you use it. Once you've chosen that, run the following:

library(MADcomm)
data <- MADcomm("~/Code/MADcomm/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 to harmonisee species' names across sites. Note that the nomenclature used in MADcomm isn't guaranteed to be the one you prefer - read on to learn more about the internal structure of MADcomm to do such cleaning for yourself. Also note that, right now, this function doesn't do anything - but it will soon, so please get into the habit of using it.

clean.data <- clean.MADcomm(data)

You can now subset your data according to particular set of species or sites:

# Grab the first 10 sites in the dataset
clean.data[sites(clean.data)[1:10],]

# Grab only the data with Will's favourite oaks
clean.data[,c("quercus_robur","quercus_ilex")]

MADcomm structure

A MADcomm data object is really just four data.frames in a list: (1:data) the species seen at each site, (2:spp.metadata) meta-data information about species, (3:site.metadata) meta-data information about sites (location, etc.), and (4:study.metadata) meta-data information about each study.

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 MADcomm 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/MADcomm/wiki. Please follow those instructions!