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letsR: a new R package for data handling and analysis in macroecology
Author(s) -
Vilela Bruno,
Villalobos Fabricio
Publication year - 2015
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12401
Subject(s) - shapefile , macroecology , r package , data science , computer science , big data , geographic information system , spatial analysis , ecology , data mining , raster data , raster graphics , geography , cartography , species richness , biology , remote sensing , world wide web , computational science , artificial intelligence , metadata
Summary The current availability of large ecological data sets and the computational capacity to handle them have fostered the testing and development of theory at broad spatial and temporal scales. Macroecology has particularly benefited from this era of big data, but tools are still required to help transforming this data into information and knowledge. Here, we present ‘ letsR ’, a package for the R statistical computing environment, designed to handle and analyse macroecological data such as species’ geographic distributions (polygons in shapefile format and point occurrences) and environmental variables (in raster format). The package also includes functions to obtain data on species’ habitat use, description year and current as well as temporal trends in conservation status as provided by the IUCN RedList online data base. ‘ letsR ’ main functionalities are based on the presence–absence matrices that can be created with the package's functions and from which other functions can be applied to generate, for example species richness rasters, geographic mid‐points of species and species‐ and site‐based attributes. We exemplify the package's functionality by describing and evaluating the geographic pattern of species’ description year in tailless amphibians. All data preparation and most analyses were made using the ‘ letsR ’ functions. Our example illustrates the package's capability for conducting macroecological analyses under a single computer platform, potentially helping researchers to save time and effort in this endeavour.