Abstract
Data frames are integral to R. They provide a standard format for passing data to model-fitting and plotting functions, and this standard makes it easier for experienced users to learn new functions that accept data as a single data frame. Still, many data sets do not easily fit into a single data frame; data sets in ecology with a so-called fourth- corner problem provide important examples. Manipulating such inherently multiple-table data using several data frames can result in long and difficult-to-read workflows. We introduce the R multitable package to provide new data storage objects called data.list objects, which extend the data.frame concept to explicitly multiple-table settings. Like data frames, data lists are lists of variables stored as vectors; what is new is that these vectors have dimension attributes that make accessing and manipulating them easier. As data.list objects can be coerced to data.frame objects, they can be used with all R functions that accept an object that is coercible to a data.frame.
Highlights
The standard data management paradigm in R is based on data.frame objects, which are two-dimensional data tables with rows and columns representing replicates and variables (R Development Core Team 2012)
It provides access to those details, which are required for effective analyzes and to develop new methods of analysis within the framework
As new methods are developed, researchers pass their data frames to new functions in much the same way they would pass them to older functions
Summary
The standard data management paradigm in R is based on data.frame objects, which are two-dimensional data tables with rows and columns representing replicates (sometimes called objects) and variables (R Development Core Team 2012). Data frames and formulas are combined by passing them to functions that multitable: Multiple-Table Data in R species environmental variables abundance sites traits fourth corner. Several such methods developed in ecology, focusing on data with a fourth-corner problem (Doledec, Chessel, ter Braak, and Champely 1996; Legendre et al 1997; Dray and Legendre 2008; Pillar and Duarte 2010; Leibold, Economo, and Peres-Neto 2010; Ives and Helmus 2011) These methods do not apply to data sets that have other more complex multipletable data structures (e.g., the zooplankton communities in Lac Croche, which are described in Figure 2; Cantin, Beisner, Gunn, Prairie, and Winter 2011).
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