Open AccessReol: R interface to the E ncyclopedia of L ife
Author(s) -
Banbury Barbara L.,
O'Meara Brian C.
Publication year - 2014
Publication title -
ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.17
H-Index - 63
ISSN - 2045-7758
DOI - 10.1002/ece3.1109
Subject(s) - computer science , interface (matter) , upload , world wide web , construct (python library) , web page , the internet , hyperlink , download , tree (set theory) , crawling , information retrieval , biology , bubble , maximum bubble pressure method , parallel computing , anatomy , programming language , mathematical analysis , mathematics
The Encyclopedia of Life is a website that hosts information about life on Earth. Its mission is to increase awareness and understanding of living nature through a freely accessible digital source. Information is publicly available through graphical webpages (browser interface) or through an application programming interface ( API ). We developed Reol , an open‐source package for the R environment, which downloads data from the EOL API , searches for and extracts specific information, and builds tables with quantitative data and/or hierarchical classifications. We provide a detailed description how Reol can be used as a bridge between the R environment and the EOL API to extract quantitative or hierarchical content. It will be particularly useful for researchers who want information about taxonomic groups of interest (for example: how much information is known about flatworm species? What are the taxonomic synonyms for bird species?) or construct a taxonomic tree. Reol is a tool for researchers who wish to download and gather data from EOL or its provider pages. We provide numerous functions within R for downloading, gathering data in different forms, creating taxonomic trees, and plotting data, which work with functions already available through various packages. It joins a growing body of R packages that interact with web‐based API s to streamline data acquisition, thereby easing the analysis of large publicly available datasets.