An R package for reading, writing, integrating and publishing data using the Ecological Metadata Language (EML) format.
- Note: This package is still in active development and not yet submitted to CRAN. Functions and documentation may be incomplete and subject to change.
- Maintainer: Carl Boettiger
- Authors: Carl Boettiger, Scott Chamberlain, Hilmar Lapp, Kseniia Shumelchyk, Rutger Vos
- License: BSD-3
- Issues: Bug reports, feature requests, and development discussion.
An extensive and rapidly growing collection of richly annotated phylogenetics data is now available in the NeXML format. NeXML relies on state-of-the-art data exchange technology to provide a format that can be both validated and extended, providing a data quality assurance and and adaptability to the future that is lacking in other formats Vos et al 2012.
The development version of RNeXML is available on Github. With the devtools package installed on your system, RNeXML can be installed using:
library(devtools)
install_github("RNeXML", "ropensci")
library(RNeXML)Read in a nexml file into the ape::phylo format:
f <- system.file("examples", "comp_analysis.xml", package = "RNeXML")
nexml <- nexml_read(f)
tr <- get_trees(nexml) # or: as(nexml, 'phylo')
plot(tr)
Write an ape::phylo tree into the nexml format:
data(bird.orders)
nexml_write(bird.orders, "test.xml")## [1] "test.xml"
A key feature of NeXML is the ability to formally validate the construction of the data file against the standard (the lack of such a feature in nexus files had lead to inconsistencies across different software platforms, and some files that cannot be read at all). While it is difficult to make an invalid NeXML file from RNeXML, it never hurts to validate just to be sure:
nexml_validate("test.xml")## [1] TRUE
Extract metadata from the NeXML file:
birds <- nexml_read("test.xml")
get_taxa(birds)## [1] "Struthioniformes" "Tinamiformes" "Craciformes"
## [4] "Galliformes" "Anseriformes" "Turniciformes"
## [7] "Piciformes" "Galbuliformes" "Bucerotiformes"
## [10] "Upupiformes" "Trogoniformes" "Coraciiformes"
## [13] "Coliiformes" "Cuculiformes" "Psittaciformes"
## [16] "Apodiformes" "Trochiliformes" "Musophagiformes"
## [19] "Strigiformes" "Columbiformes" "Gruiformes"
## [22] "Ciconiiformes" "Passeriformes"
get_metadata(birds)## $`cc:license`
## [1] "http://creativecommons.org/publicdomain/zero/1.0/"
Add basic additional metadata:
nexml_write(bird.orders, file = "meta_example.xml", title = "My test title",
description = "A description of my test", creator = "Carl Boettiger <cboettig@gmail.com>",
publisher = "unpublished data", pubdate = "2012-04-01")## [1] "meta_example.xml"
By default, RNeXML adds certain metadata, including the NCBI taxon id numbers for all named taxa. This acts a check on the spelling and definitions of the taxa as well as providing a link to additional metadata about each taxonomic unit described in the dataset.
We can also add arbitrary metadata to a NeXML tree by define meta objects:
modified <- meta(property = "prism:modificationDate", content = "2013-10-04")Advanced use requires specifying the namespace used. Metadata follows the RDFa conventions. Here we indicate the modification date using the prism vocabulary. This namespace is included by default, as it is used for some of the basic metadata shown in the previous example. We can see from this list:
RNeXML:::nexml_namespaces## nex
## "http://www.nexml.org/2009"
## xsi
## "http://www.w3.org/2001/XMLSchema-instance"
## xml
## "http://www.w3.org/XML/1998/namespace"
## cdao
## "http://www.evolutionaryontology.org/cdao/1.0/cdao.owl#"
## xsd
## "http://www.w3.org/2001/XMLSchema#"
## dc
## "http://purl.org/dc/elements/1.1/"
## dcterms
## "http://purl.org/dc/terms/"
## prism
## "http://prismstandard.org/namespaces/1.2/basic/"
## cc
## "http://creativecommons.org/ns#"
## ncbi
## "http://www.ncbi.nlm.nih.gov/taxonomy#"
## tc
## "http://rs.tdwg.org/ontology/voc/TaxonConcept#"
This next block defines a resource (link), described by the rel attribute as a homepage, a term in the foaf vocabulalry. Becuase foaf is not a default namespace, we will have to provide its URL in the full definition below.
website <- meta(href = "http://carlboettiger.info", rel = "foaf:homepage")Here we create a history node using the skos namespace. We can also add id values to any metadata element to make the element easier to reference externally:
history <- meta(property = "skos:historyNote", content = "Mapped from the bird.orders data in the ape package using RNeXML",
id = "meta123")Once we have created the meta elements, we can pass them to our nexml_write function, along with definitions of the namespaces.
nexml_write(bird.orders, file = "example.xml", meta = list(history, modified,
website), namespaces = c(skos = "http://www.w3.org/2004/02/skos/core#",
foaf = "http://xmlns.com/foaf/0.1/"))## [1] "example.xml"
Add taxonomic identifier metadata to the OTU elements:
nex <- add_trees(bird.orders)
nex <- taxize_nexml(nex)##
## Retrieving data for taxon 'Struthioniformes'
##
##
## Retrieving data for taxon 'Tinamiformes'
##
##
## Retrieving data for taxon 'Craciformes'
##
##
## Retrieving data for taxon 'Galliformes'
##
##
## Retrieving data for taxon 'Anseriformes'
##
##
## Retrieving data for taxon 'Turniciformes'
##
##
## Retrieving data for taxon 'Piciformes'
##
##
## Retrieving data for taxon 'Galbuliformes'
##
##
## Retrieving data for taxon 'Bucerotiformes'
##
##
## Retrieving data for taxon 'Upupiformes'
##
##
## Retrieving data for taxon 'Trogoniformes'
##
##
## Retrieving data for taxon 'Coraciiformes'
##
##
## Retrieving data for taxon 'Coliiformes'
##
##
## Retrieving data for taxon 'Cuculiformes'
##
##
## Retrieving data for taxon 'Psittaciformes'
##
##
## Retrieving data for taxon 'Apodiformes'
##
##
## Retrieving data for taxon 'Trochiliformes'
##
##
## Retrieving data for taxon 'Musophagiformes'
##
##
## Retrieving data for taxon 'Strigiformes'
##
##
## Retrieving data for taxon 'Columbiformes'
##
##
## Retrieving data for taxon 'Gruiformes'
##
##
## Retrieving data for taxon 'Ciconiiformes'
##
##
## Retrieving data for taxon 'Passeriformes'
NeXML also provides a standard exchange format for handling character data. The R platform is particularly popular in the context of phylogenetic comparative methods, which consider both a given phylogeny and a set of traits. NeXML provides an ideal tool for handling this metadata.
We can load the library, parse the NeXML file and extract both the characters and the phylogeny.
library(RNeXML)
nexml <- read.nexml(system.file("examples", "comp_analysis.xml", package = "RNeXML"))
traits <- get_characters(nexml)
tree <- get_trees(nexml)(Note that get_characters would return both discrete and continuous characters together in the same data.frame, but we use get_characters_list to get separate data.frames for the continuous characters block and the discrete characters block).
We can then fire up geiger and fit, say, a Brownian motion model the continuous data and a Markov transition matrix to the discrete states:
library(geiger)
fitContinuous(tree, traits[1])## Loading required package: parallel
## GEIGER-fitted comparative model of continuous data
## fitted 'BM' model parameters:
## sigsq = 1.166011
## z0 = 0.255591
##
## model summary:
## log-likelihood = -20.501183
## AIC = 45.002367
## AICc = 46.716652
## free parameters = 2
##
## Convergence diagnostics:
## optimization iterations = 100
## failed iterations = 0
## frequency of best fit = 1.00
##
## object summary:
## 'lik' -- likelihood function
## 'bnd' -- bounds for likelihood search
## 'res' -- optimization iteration summary
## 'opt' -- maximum likelihood parameter estimates
fitDiscrete(tree, traits[2])## GEIGER-fitted comparative model of discrete data
## fitted Q matrix:
## 0 1
## 0 -0.07308 0.07308
## 1 0.07308 -0.07308
##
## model summary:
## log-likelihood = -4.574133
## AIC = 11.148266
## AICc = 11.648266
## free parameters = 1
##
## Convergence diagnostics:
## optimization iterations = 100
## failed iterations = 0
## frequency of best fit = 1.00
##
## object summary:
## 'lik' -- likelihood function
## 'bnd' -- bounds for likelihood search
## 'res' -- optimization iteration summary
## 'opt' -- maximum likelihood parameter estimates